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An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework 2

An Efficient Detection of Intracranial Hematoma Using Window...

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2nd international conference on Advancements in Electrical, Electronics, Communication, Computing and automation, ICAECA 2023

作者： Vidhya, V. Gudigar, Anjan Raghavendra, U. Basak, Sudipta Mallappa, Sankalp Hegde, Ajay Menon, Girish Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Instrumentation and Control Engineering Manipal576104 India Department of Neurosurgery Manipal Hospitals Karnataka India Department of Neurosurgery Manipal Academy of Higher Education Kasturba Medical College Manipal576104 India

ISBN: (纸本)9798350306811

Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients. © 2023 IEEE.

关键词： Object detection

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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2024 international conference on Decision Aid Sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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A Seismic Phase-Picking Model Transfer Learning Approach Based on Maximum Mean Discrepancy 3

A Seismic Phase-Picking Model Transfer Learning Approach Bas...

引用

3rd international conference on computer Science, Electronic Information engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Kun'ao Huang, Yongming Chen, Jun Sun, Yejun School of Automation Southeast University Nanjing China School of Electronic Information and Electrical Engineering Hefei Normal University Hefei China Jiangsu Earthquake Agency Nanjing China

ISBN: (纸本)9798350372052

Thanks to the abundance of earthquake data and the advancement of deep neural networks, many seismic phase-picking models have been proposed to detect the arrival time of P-waves and S-waves. Currently, the accuracy of these models exceeds that of traditional methods, such as short-term average/long-term average (STA/LTA). While many studies concentrate on phase picks on a single seismic dataset, there is limited research regarding the generalization of phase-picking models. Specifically, applying a model trained on one seismic dataset to an unseen dataset remains underexplored. A phase-picking model is designed in this paper, which consists of an encoder and a decoder. The self-attention is applied in the encoder module to focus seismic features. Then a seismic phase-picking model transfer learning approach based on maximum mean discrepancy (SPPTL-MMD) is proposed to train a universal seismic feature extraction module by utilizing diverse seismic datasets. The module will be transferred and a significantly improved phase-picking model is obtained through fine-tuning techniques. Experiments show that the model after fine-tuning can be generalized to other datasets. © 2023 IEEE.

关键词： Signal encoding

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Decoding Gait Speed Using Intracortical Signals in a Freely-Moving Non-Human Primate for the Control of Neurorobotics

Decoding Gait Speed Using Intracortical Signals in a Freely-...

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2024 IEEE international conference on Robotics and Biomimetics, ROBIO 2024

作者： Chen, Long Zhang, Yilin Mo, Lifen Guo, Zheshan Gao, Fei Liang, Fengyan Liao, Wei-Hsin Yin, Ming School of Biomedical Engineering Hainan University Laboratory of Biomedical Engineering of Hainan Province Sanya China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong

ISBN: (纸本)9781665481090

Locomotion is one of the most fundamental and crucial motor abilities in both humans and animals. Compared to other quadrupedal mammals, primates exhibit unique quadrupedal characteristics, such as diagonal gait and compliant walking. Significant progress has been made in past studies to understand the relationship between behaviors and neural activities in primates. However, these studies often limit the behavior to constrained environments and focus only on the upper limbs. Here, we trained a macaque to walk freely on a treadmill at four different speeds (0.5, 1.0, 1.5, 2.0 km/h), Intracortical signals and videos were recorded at the same time. Our results indicate that the primary motor cortex (M1) exhibits higher neuronal firing rates during the swing phase of gait, suggesting its critical role in gait encoding. Furthermore, we utilized only eight neural units to decode gait speeds and employed mutual information for feature selection to improve the decoding accuracies. Results indicated that four gait speeds can be reliably classified with an accuracy of 82.1%. The firing rates of neurons from 100 ms to 300 ms before the forelimb raise exhibited the most variations during task execution and contributed the most to the decoder model. This study provides new insights into the cortical control of quadrupedal locomotion. The proposed cortical speed decoder can be applied in brain-computer-interface (BCI) to control neurorobotics such as exoskeletons and prostheses. © 2024 IEEE.

关键词： Brain computer interface

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Efficient Fire Detection and automation Using Haar Cascade 1

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8th international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Sandhya, G. Harshavardhan, M. Inbasudan, S. Jayabal, S. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India

ISBN: (数字)9789819994892

ISBN: (纸本)9789819994885

In the current scenario breaking out fire is a common thing that occurs everywhere, and this may also cause many damages for both nature and humans. Here the system aims at the major role on detecting fire that provides more information which helps to compare using sensor. The detection process is done by using the method of image process. The prediction using image detection provides more accuracy rather than any other detection process. The dataset that is used for fire detection is totally to be about 1750 images. The images which are downloaded through internet are needed to be resized and reshaped. After detecting the fire, the system provides the alert through Blynk IoT app. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fires

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning 4

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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4th international conference on Electronic Information engineering and computer Science, EIECS 2024

作者： Yu, Yang Xie, Yufei Wang, Wenlin Wu, Guohua Lan, Haitao Lin, Enbo An, Ping Sun, Zibin Zhang, Haichuan Wu, Yixian National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9798331531409

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R2: 0.735). © 2024 IEEE.

关键词： Coolants

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Automated Bengali abusive text classification: Using Deep Learning Techniques 1

Automated Bengali abusive text classification: Using Deep Le...

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1st IEEE international conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Titli, Showni Rudra Paul, Shimul National Institute of Technology Dept. Electronics and Communication Engineering Calicut India Ahsanullah University of Science and Technology Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350348057

In recent years, the breakdown of most human causes that hampers a large number of people is verbal abuse. The most noticeable fact is that most of the verbal abuse is made online by people behind the screen. While this problem has occurred with growing modern technologies, a modern solution is needed to solve the drawback. The growing rate of abusive comments and hate speech online is rapidly increasing on a large scale, and manual reports and corrections cannot help this critical situation. This proposed model introduces automation of the hate speech filtering process through deep learning. This work uses only Bengali datasets to find the real classification, as many Bengali and English mixed research works are available in this field. Using the intelligent automated classification of text comments in a limited resource-constrained language (example: Bengali) is critical for several reasons. This proposed system classifies 'Personal Offensive Text', 'Geographical Offensive Text', 'Religious Offensive text', 'Crime Offensive Text', 'Entertainment, Sports, Meme Tiktok, and others'. By combining two large datasets for this research and employing Bengali BERT, the best feasible outcome has been *** paper indicates the complete results of the combined two different datasets and classified into nine segments, while the proposed Bengali BERT models achieved the highest accuracy of 0.706 and a weighted f1 score of 0.705 in the identification and classification tasks. © 2023 IEEE.

关键词： automation

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Implementing Robotic Process automation in small and medium-sized enterprises - implications for organisations 27

Implementing Robotic Process Automation in small and medium-...

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27th international conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Pyplacz, Paula Žukovskis, Jan Czestochowa University of Technology al Armii Krajowej 19b Czestochowa42-200 Poland Vytautas Magnus University Universiteto 10 Akademia LT-53361 Lithuania

Process automation is most often associated with physical solutions used in industry. However, in addition to hardware robots, a separate group are software robots that can be used to replace human labour in terms of repetitive, structured business processes. Robotic Process automation (RPA) solutions are becoming feasible for small and medium-sized entities (SMEs) due to their price and ease of use. The aim of this study is to diagnose the relationship between a company's orientation towards employee education and retraining, the provision of support from the company's management and the implementation of RPA understood as a change in work organisation and process change. Empirical research was conducted on a sample of 248 SMEs, via a survey questionnaire. They confirmed the relationships between the analysed variables indicating the necessity of treating the implementation of RPA technology in the organisation as a change not only as an IT solution, but in a broader scope - organisational, affecting employees and processes. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： change management organisation Robotic Process automation (RPA) SME

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Wheeled Mobile Robot Navigation Based on the Fusion of A* Algorithm and Improved Dynamic Window Approach 1

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Jiao, Ting Kong, Lingxin Hu, Conglin School of Automation and Software Engineering Shanxi University Taiyuan030031 China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

The A* method is frequently utilized for strategizing navigational routes for autonomous wheeled robots, but it is not applicable to scenarios with dynamic obstacles. While the Dynamic Window Approach (DWA) algorithm can adapt to dynamic environments, it is limited in terms of path smoothing and the flexibility of adjusting the weights within its evaluation function. To address these challenges, this paper proposes a hybrid path planning strategy that integrates the A* algorithm with an enhanced DWA algorithm, aiming at improving navigation efficiency and ensuring safety of robots in environments containing both static and dynamic obstacles. Initially, we introduce the principles of the A* and DWA algorithm. Subsequently, by incorporating a smooth evaluation function into the DWA algorithm and proposing a mechanism to dynamically adjust the weights, we improve the algorithm’s local obstacle avoidance capability. Further, by combining these two algorithms, we utilize the A* algorithm for global path planning and the improved DWA algorithm for local obstacle avoidance along the path. A series of real-robot tests validate the efficiency of the suggested approach in handling static and dynamic obstacles, demonstrating its good navigation performance in complex environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mobile robots

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automation for Requirements engineering: Opportunities and Pitfalls — RE '24 Keynote

Automation for Requirements Engineering: Opportunities and P...

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IEEE international conference on Requirements engineering

作者： Jan Mendling Department of Computer Science Humboldt-Universität zu Berlin Germany

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

Recent technologies like ChatGPT inspire ideas of increasing automation for professional tasks in various domains. This includes among others requirements engineering. So far, however, the divide between useful and harmful automation has been blurry and in flux. In this keynote, I will discuss foundational concepts with which automation has been analyzed. Using these concepts, I will discuss the opportunities arising for requirements engineering from the advent of natural language processing, image processing, and event sequence analysis techniques. Furthermore, I will discuss important pitfalls that have been well documented for other automation technologies in the past.

关键词： automation Sequences Image processing Chatbots Requirements engineering Task analysis

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