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Dynamic Weighting-Based Deep Learning for SLAM Loop Closure Detection 3

Dynamic Weighting-Based Deep Learning for SLAM Loop Closure ...

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3rd international conference on Image Processing, computer vision and Machine Learning, ICICML 2024

作者： Yang, Binqi Qiu, Chuan Yang, Wei Ren, Mingwu Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing Shoufa Highway Maintenance and Construction Co. Ltd. Beijing China

ISBN: (纸本)9798350355413

In LiDAR SLAM systems, loop closure detection is a crucial component to ensure localization accuracy and map consistency. OverlapNet, a loop closure detection algorithm based on image overlap rate, has demonstrated strong performance. However, the current loss function of OverlapNet does not account for the uncertainty differences between tasks, which may lead to insufficient adjustment of task weights. To address this, this paper proposes a dynamic weight-based SLAM loop closure detection method, DW-LoopNet. By introducing a dynamic weight mechanism, this method automatically adjusts the relative weights of overlap and yaw angle estimation tasks. Based on real-time evaluation of environmental feature importance, DW-LoopNet dynamically adjusts the weights of different features, ensuring that key features receive higher attention in loop closure detection. Experimental results show that DW-LoopNet significantly outperforms other baseline methods on the KITTI and Ford Campus datasets, providing a more accurate and robust loop closure detection solution for SLAM systems. © 2024 IEEE.

关键词： Deep learning

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control Accuracy Calculation of Flap & Slat System of Civil Aircraft

Control Accuracy Calculation of Flap & Slat System of Civil ...

引用

Energy and Power Engineering, control Engineering (EPECE), international conference on

作者： Jian Xie State Key Laboratory of Airliner Integration Technology and Flight Simulation China

ISBN: (数字)9798350366099

ISBN: (纸本)9798350366105

The control accuracy of the flap & slat system of civil aircraft greatly affects the actual theoretical position of the flap & slat surface, which influences the aircraft’s ability to gain lift and the design of the structural stop block. Therefore, it is important to analyze the origins of the accuracy components to improve the control accuracy of the system. This paper introduces how the control accuracy of the flap & slat system affects the aircraft, analyzes each main factor of the system control accuracy and makes a specific analysis of its origins, and finally proposes a method based on the self-compensating balance of the computer to improve the control accuracy of the system. It is believed that this study is of great significance for the design of domestic large aircraft design related.

关键词： Power engineering Accuracy Costs control engineering Gears Aerospace electronics Sensor systems

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Utilizing AI-powered Drones for Efficient Flood Rescue Operations

Utilizing AI-powered Drones for Efficient Flood Rescue Opera...

引用

Automation, Computing and Renewable Systems (ICACRS), international conference on

作者： M. Jaswanth Kumar Dhanush Bitra Rohan Titus Dill Jazz Cinu C Kiliroor Dept. of Electronics and Communication Indian Institute of Information Technology Kottayam Kottayam Kerala India Dept. of Computer Science Indian Institute of Information Technology Kottayam Kottayam Kerala India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In times of severe earthquakes and floods, rapid and effective aid is crucial. Traditional methods often face challenges due to accessibility issues and the vast scale of *** proposed Autonomous Aerial Humanitarian Assistance and Disaster Relief (A2-HADR) System aims to transform disaster response through the use of drone technology. The proposed solution addresses the obstacles in rescue operations by employing drones equipped with Artificial Intelligence (AI) to provide immediate support. The advanced system utilizes state-of-the-art computer vision technology to autonomously detect people and obstacles from heights of 50-100 meters at various angles and deliver essential supplies like food, clothing, and rescue gear to those in need. We have executed and assessed a range of advanced object detection algorithms, such as YOLOv8, YOLOv9, and Detectron2. After thorough evaluation, Detectron2 emerged as the most effective model among those tested, showcasing exceptional accuracy and resilience.

关键词： Deep learning computer vision Accuracy Disasters Computational modeling Object detection Disaster management Transforms Artificial intelligence Drones

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Artificial Intelligence Assisted Student Learning and Performance Analysis using Instructor Evaluation Model 3

Artificial Intelligence Assisted Student Learning and Perfor...

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Nithiyanandam, N. Dhanasekaran, S. Kumar, A. Sivanesh Gobinath, D. Vijayakarthik, P. Rajkumar, G. Vinoth Muthuraman, U. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Kalasalingam University Department of Information Technology Tamilnadu Srivilliputtur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Sri Manakulavinayagar Engineering College Department of Information Technology Puducherry India Sir M Visvesvaraya Institute of Technology Department of Information Science and Engineering Bengaluru India J P College of Engineering Department of Electronics and Communication Engineering Tamilnadu Tenkasi627852 India Francis Xavier Engineering College Dept of Eee Tirunelveli India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Student's proficiency in a subject may be gauged through the use of Learning Management Systems (LMS) (LMSs). Using a scoring algorithm to calculate the percentage of each student's attentiveness in the specific class, this system aims to increase the use of artificial intelligence approaches. The graph provided by this tool may be used to measure how well a student understands the material being taught. Data mining tools are increasingly being used in higher education to help students and administrators better understand and resolve educational and administrative difficulties. The majority of educational mining research focuses on replicating students' performance rather than instructors' performance. The course assessment questionnaire is a standard instrument for evaluating teachers' performance based on students' perceptions. There are a number of different ways for creating classification techniques in this system, from decision trees to support vector machines to machine learning techniques, including differential evolution. Comparing their answers to an actual course assessment question based on its accuracy, retention and sensitivity. Students' online conduct has become more accessible because to the use of Learning Management Systems (LMSs) in educational institutions. These statistics have been utilized by several studies to predict student outcomes. A wide range of subjects and predictive characteristics collected from the LMS make it difficult to draw generalized statements about the processes that control student performance. To get started let's take a look at some of the most recent research' theoretical foundations and common predictions in the field of learning analytics. A total of 4,989 students took 17 blended courses with Moodle LMS at the same school, and both multi-level and conventional regressions are utilized to examine the impact of LMS predictor factors and between-course evaluation grades on student performance. Despite the fact that the

关键词： Students

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Web-based Plant Leaf Disease Detection and Prescription Module

Web-based Plant Leaf Disease Detection and Prescription Modu...

引用

Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Kalamani M Krishnamoorthi M Hiruthik Bose G Charan S Hariharan A Dhilipan S Department of Electronics and Communications Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Information Technology Dr.N.G.P. Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Plant disease (PD) is a persistent issue for farmers that reduces both their income and food production. Even for subject matter specialists, it might be challenging to stop the disease from scattering to other portions of the plant once it has begun. Although it takes time and requires a close inspection of the sick region, experts may identify and diagnose the ailment. Smallholder farmers do not have simple access to these experts while constituting a major section of our nation. To identify the plant leaf disease and to evaluate its phases, a proper approach is thus required. Thanks to the present internet revolution and advancements in computer vision models, computer vision can now be used in agriculture. Recently, the Convolutional Neural Networks (CNN) are regarded as the most advanced method for classifying pictures and are capable of making a conclusive diagnosis. To diagnose the illness using a web-based application module created using deep learning techniques through the Transfer Learning approach is used in this research paper. A pre-trained method is built on photos of diverse plant leaves from the Plant Village dataset. Various efficiency metrics such as f1-score, Precision, accuracy, recall calculations and tracking are simulated to validate the efficiency of the suggested approach. The accuracy of ResNet50 with the Plant Village Dataset was around 97.5%, and it will provide information on the disease spread level on the scanned leaf. This study will improve farming practices for producing food and reduce early-stage crop loss.

关键词： Deep learning Plant diseases computer vision Accuracy Food manufacturing Computational modeling Transfer learning Agriculture Convolutional neural networks Farming

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GAN-based data augmentation with recurrent residual U - Net for exudate segmentation in diabetic retinopathy 3

GAN-based data augmentation with recurrent residual U - Net ...

引用

3rd international conference on Disruptive Technologies, ICDT 2025

作者： Nigam, Prakhar Jain, Ahan Sharma, Seema Aset Amity University Cse Department U.P. Noida India Amity University Computer Science Dept. U.P. Noida India

ISBN: (纸本)9798331519582

Diabetic Retinopathy (DR) is a leading cause of vision impairment worldwide, often characterized by the presence of retinal lesions such as exudates. Hard exudates (HE), which appear as bright lesions in retinal fundus images, are an early and critical indicator of DR, making their accurate detection vital for timely diagnosis and intervention. Despite improvements in medical imaging, creating reliable deep learning models for HE segmentation is largely affected by the scarcity of annotated datasets. Recent studies on classifying the segmentation of sub-lesions in retinal images face a significant challenge due to the limited diversity of datasets. In this study, we propose an approach that combines Generative Adversarial Networks (GANs) for data augmentation with a Recurrent Residual U-Net (R2U-Net) architecture for segmentation. Through the use of GANs, this study addresses the dataset scarcity issue caused by the time-intensive process of manual annotation. The datasets - IDRiD and E-Ophtha - were used to assess our model after it was trained on the IDRiD dataset. Performance comparisons were conducted between models trained on (i) original dataset with no augmentation (ii) original dataset with traditional/offline augmentation, (iii) synthetic dataset made with augmented orig-inal dataset and GAN-generated images. The GAN-augmented dataset achieved the best metrics: IoU 0.9319, DSC 0.9435, accu-racy 0.9926, sensitivity 0.9554, and specificity 0.9930, surpassing recent literature. © 2025 IEEE.

关键词： Image segmentation

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Address Resolution Protocol Spoofing Mitigation in Software-Defined Networks using Ryu controller's Centralized Flow control

Address Resolution Protocol Spoofing Mitigation in Software-...

引用

Advancement in Computation & computer Technologies (InCACCT), international conference on

作者： Sendil Vadivu D Narendran Rajagopalan Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Address Resolution Protocol (ARP) spoofing is a severe threat in Software-Defined Networking (SDN) systems, allowing attackers to modify network traffic via Man-in-the-Middle (MITM) attacks. This study presents an effective proactive ARP spoofing defense that uses SDN's centralized control and programmability features. The system continually analyzes ARP packets, keeps an accurate logical and physical address mapping, and dynamically adds flow restrictions to prevent malicious hosts from imitating legitimate devices. The proposed system identifies and prevents single and multiple source ARP spoofing attacks in real-time at O(1) processing cost, resulting in low network overhead. The experiment conducted on an SDN-simulated environment proved that the Ryu controller can mitigate ARP spoofing attempts while preserving legitimate network activities. Furthermore, the proposed technique can scale in terms of network capacity and traffic volume. The proposed automated and scalable ARP spoofing approach enhances SDN cybersecurity.

关键词： Protocols Costs Accuracy Flow production systems Prevention and mitigation Telecommunication traffic Real-time systems Software defined networking computer security Centralized control

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Classification of Footprints for Correctives in Orthopaedics

Classification of Footprints for Correctives in Orthopaedics

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Sophie Bauernfeind Christoph Praschl Markus Wakolbinger Gerald A. Zwettler Research and Development Department University of Applied Sciences Upper Austria Austria WAKO 3D Ltd. Linz Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Austria

Foot disorders, a commonly overlooked issue, are prevalent in developed societies. These disorders can have a significant impact on a person’s quality of life and can even be debilitating, regardless of age. Non-invasive pedobarographic examinations, which evaluate the pressure fields of the plantar surface of the foot and a supporting surface, allow for analysis of a patient’s gait and posture using 2D footprints or scans. This data can then be used by orthopaedists to create customized shoes or insoles as correctives. However, the lack of standardized protocols and guidelines and the scarcity of evidence-based information can lead to a subjective evaluation and selection of correctives by the orthopaedist. This study proposes an objective and quantifiable method for the classification of footprints using computer vision paradigms, in order to create more appropriate correctives. The results show that the proposed machine learning models are able to correctly identify the required one of three correctives with an accuracy of 70% for RGB scans and 49% for blueprints. Based on the current results, future work should focus not only on the classification of suitable correctives, but also on the determination of the corrective’s position to ensure the best possible patient outcomes.

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Multistage N/W Intrusion - Detection system implementation through DL and Image Classification

Multistage N/W Intrusion - Detection system implementation t...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Kadambari Raghuram Gaurav Sethi Ujjawal Kumar G. Ranjith Kumar Hayder Al-Chilibi Malik Bader Alazzam Department of Computer Science and Engineering University College of Engineering Kakinada(Autonomous) JNTUK Kakinada Andhra Pradesh Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Computer Science and IT Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Jordan

The current worldwide surge in hack and computer cluster attacks has heightened the demand for stronger prevention and detection systems. The system that detects intrusions (IDS) is crucial for identifying net assaults and abnormalities, which are becoming bigger and more widespread. We provide a novel three different dnnvision-based technique for detecting security breaches in this paper. The circuits are used to create pictures of four segments (Red, Colored, Aquamarine, and Alpha). However, the categorization that was done on the photographs is used to train and verify the well-known computer vision framework ResNet50. Two freely available standardized datasets, UNSW-NB15 and Regularly then used manage Denial of service ( dos, are utilised to evaluate the proposed technique. On the Ub test, the proposed approach successfully identifies the generalized exploit 99.8% of the time. The suggested approach achieves 99.7% precision on both the diagnostics of vehicles coming upon that Two or more homogeneous Intrusion data or the detection of the Gadget.

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Analysis of the Scientific Publication of Libyan State: A Bibliometric Review

Analysis of the Scientific Publication of Libyan State: A Bi...

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Yousef M. Jaradat Amer R. Zerek Mohammad Z. Masoud Ahmad A. Manasrah Sana Ahmed Abdaljlil Omar Alheyasat Communications and Computer Engineering Al-Zaytoonah University of Jordan Amman Jordan Communication Engineering Libya Open University Tripoli Libya Mechanical Engineering Al-Zaytoonah University of Jordan Amman Jordan Computer Engineering Department AlBalqa’ Applied University Salt Jordan

This paper provides a detailed bibliometric analysis of the Libyan State’s scientific publications (Libya). The study is based on data from the well-known Scopus database. The study spans the years 1948 to 2022. The study examines the growth of annual publications and citations. It has been demonstrated that over the last five years, 36% of total publications and 66% of total citations have been achieved. Science mapping activities such as co-occurrence and co-citation networks were used to reveal thematic clustering and research topic trends. The top source title with the most articles is the 2021 1ST MI-STA conference, which has 142 articles. The LANCET journal is the most frequently cited locally, with 1181 articles citing it, and the most prolific author is Dr. ElHadi from the University of Tripoli with 76 articles.

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