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Continuous and generalized visual odometry based on FLANN_PSC-RANSAC_LSTM

Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 15-28页

作者： Li, Dengao Feng, Ran Wu, Fanming Zhao, Jinhua Zhao, Jumin College of Computer Science and Technology College of Data Science Taiyuan University of Technology Shanxi Taiyuan China College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan University of Technology Shanxi Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China

In the field of simultaneous localization and mapping (SLAM), visual odometry (VO) always has great application prospects. In recent years, with the progress in the field of machine learning, methods based on neural networks are constantly being updated and applied. In this paper, we propose a continuous and generalized monocular visual odometry method based on features and neural networks. First, the feature information of adjacent image sequences is extracted by matching and troubleshooting algorithm (FLANN_PSC-RANSAC), then it and the corresponding six-degree-of-freedom information are simultaneously input into the long short-term memory artificial neural network (LSTM) for model construction, which not only ensures the reliability of the mode but also eliminates the influence of illumination on the data. In the real environment test, it has been effectively proved in terms of trajectory recovery accuracy and generalization ability to different environments and different illuminations. © 2024 - IOS Press. All rights reserved.

关键词： Long short-term memory

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Combating deepfakes: a comprehensive multilayer deepfake video detection framework

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Multimedia Tools and Applications 2024年第38期83卷 85619-85636页

作者： Rathoure, Nikhil Pateriya, R.K. Bharot, Nitesh P., Verma Department of Computer Science and Engineering Maulana Azad National Institute of Technology Mata Mandir Bhopal462003 India Data Science Institute University of Galway University road GalwayH91TK33 Ireland Department of Electronics and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland

Deepfakes represent a class of synthetic media crafted with the aid of advanced deep learning techniques that exhibit an unparalleled degree of authenticity. The rapid advancement in Artificial Intelligence (AI) has contributed to an increase in the prevalence of deepfakes on the internet, consequently amplifying the spread of misinformation among the public. Consequently, the detection of deepfakes has become a pressing concern. In this context, we put forth a comprehensive framework for deepfake video detection, which is built upon three distinct layers. The first layer, termed as the RGB features extraction layer, is designed to identify potential signs of forgery within the spatial domain of analogous video frames. The second layer, known as the GAN features extraction layer, focuses on the extraction of forgery fingerprints in the high-frequency region. This layer is specifically engineered to detect the fingerprints left by the Generative Adversarial Network (GAN) process in fake videos and the traces of the imaging process in genuine videos. The third and final layer, referred to as the facial region intra-frame inconsistency feature extraction layer, is dedicated to uncovering the anomalies associated with the manipulation process. This is achieved by extracting features from both the inner and outer regions of the manipulated portion of a frame. The extensive experimental evaluations have underscored the superior performance of proposed approach in comparison to existing state-of-the-art methods. © The Author(s) 2024.

关键词： Generative adversarial networks

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Machine Learning for Detecting Subtle Signs of EyeDisease 4

Machine Learning for Detecting Subtle Signs of EyeDisease

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Dafny Neola, J. Ebenezer, V. Edwin, E.Bijolin Kirubakaran, S.Stewart Thanka, M.Roshni Yona Davincy, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350385793

Eye disorders can have serious repercussions, such as blindness or visual impairment, if they are not identified or addressed, Treatment that works and preventing permanent harm depend on early diagnosis and prediction. Using cutting-edge clinical observations, this method looks for diseases such diabetic retinopathy, cataracts, myopia, glaucoma, age-related eye illnesses, and hypertension-related eye disorders. This study presents an innovative method for early detection of eye health disorders: The Eye Health Station. It uses machine learning algorithm, by leveraging EfficientNet's superior performance in image classification tasks, our system analyzes ocular images, such as retinal scans, to identify potential indicators of eye disorders at an early stage., The proposed system seeks to discover possible markers of eye problems by evaluating several factors taken from ocular pictures and patient data, including retinal scans and medical history. The system is able to learn from various datasets and gradually increase its accuracy since machine learning techniques are included into the system. © 2024 IEEE.

关键词： Ophthalmology

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Jamandlamudi, Sahit Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Knee Osteoarthritis Detection Using X-Rays and DNN 3

Knee Osteoarthritis Detection Using X-Rays and DNN

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sinha, Vaishnawi Veemaraj, Ebenezer Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India Division of Data Science and Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350375190

Knee osteoarthritis (OA) is a prevalent degenerative joint disease that affects millions worldwide, leading to pain, disability, and reduced quality of life. Early detection and intervention are crucial for effective management and prevention of disease progression. In this paper, we propose a novel approach for knee OA detection using X-ray images and deep neural networks (DNNs). Specifically, we investigate the performance of two popular DNN architectures: EfficientNet-B5 and DenseNet121. We begin by pre-processing a dataset of knee X-ray images to extract relevant features and mitigate noise. Subsequently, we fine-tune the pre-trained EfficientNet-B5 and DenseNet121 models using transfer learning techniques to adapt them to the task of knee OA detection. We then evaluate and compare the performance of these models in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Our experimental results demonstrate the efficacy of both EfficientNet-B5 and DenseNet121 architectures in knee OA detection, outperforming traditional machine learning approaches. However, we observe variations in their performance metrics, indicating nuanced differences in their ability to capture and discriminate pathological features in knee X-ray images. Furthermore, we conduct an in-depth analysis to understand the factors contributing to the performance disparities between the two models. Overall, our study contributes to the growing body of research aimed at leveraging DNNs for medical image analysis tasks, particularly in the domain of musculoskeletal disorders. The insights gained from this research can potentially aid clinicians in early diagnosis and personalized treatment planning for knee OA patients, ultimately improving patient outcomes and healthcare efficiency. © 2024 IEEE.

关键词： Grading

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Machine Learning Approaches for Stroke Detection and SMOTE for Imbalanced data 8th

Machine Learning Approaches for Stroke Detection and SMOTE f...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Ruchitha, H.K. Sharma, B. S. Sanjana Sneha Rao, Trupthi Kodipalli, Ashwini Devamane, Shridhar B. Department of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bangalore India Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9789819976324

The majority of strokes will be caused by an unanticipated blockage of pathways by the heart and brain. To provide analytical data backing for timely, patient stroke prevention and detection, by creating a highly accurate and well-risk prediction model based on stroke risk factors using machine learning models (ML). We used numerous ML algorithms to analyse influencing factors to perform early detection, timely diagnosis, and timely treatment. In this paper, we have applied Decision Tree Classifier (DTC), K-Nearest Neighbours (KNN), and Logistic Regression (LR) for the analysis. The results were misleading because the original dataset had a data imbalance. To tackle this issue, the synthetic minority oversampling technique (SMOTE) was used in a sampling technique. There is also the use of k-fold cross-validation to extract more information about the performance of the algorithm and additionally comparing the accuracy of the three models. Therefore, this model can be used to support the decision-making of health care professionals. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Securing 3D Point and Mesh Fog data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration and Treatment Identification

Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration a...

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International Conference on Computing and Machine Learning, CML 2024

作者： Rai, Bipin Kumar Bisht, Deepanshu Kumar, Ekansh Chaudhary, Aashish Department of Computer Science & Engineering Dayananda Sagar University Bengaluru India Department of CSE-Data Science ABES Institute of Technology Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9789819765874

This research aimed to develop a 3D Faster-R-CNN model for detecting dental restorations and treatments in panoramic view radiographs and dental intra-oral X-rays. Trained on a comprehensive collection of 2D and 3D dental X-ray images, it marked a notable advancement. Utilizing the 3D TeethSeg 2022 MICCA dataset, the model demonstrated its ability to automatically detect and classify dental features, including challenging traits like cavity spots and tooth cracks. The study addressed the limitations of current methods for analyzing dental X-rays, especially in 3D format. While existing 2D models have some recognition capabilities, they struggle with 3D data intricacies. Moreover, dedicated 3D models for dental applications are lacking. This proposed model introduced a novel approach tailored for 3D dental image analysis, aiming to fill this gap. The model played a vital role in assisting dentists by identifying restorative and treatment needs for each tooth or patient, significantly enhancing dental care quality and efficiency. It showcased notable performance metrics: an accuracy of 89.14%, a precision of 95.84%, and a recall of 91.93%. Through advanced data augmentation and meticulous model training, it represented a significant advancement in detecting and classifying dental interventions, offering a comprehensive approach to dental image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： X ray radiography

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An Effective Deep Learning based Recommender System with user and item embedding

An Effective Deep Learning based Recommender System with use...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Nareshkumar, R. Agalya, K. Arunpandiyan, A. Vijayalakshmi, M. Ranjani, V. Ramya, A. SRM Institute of Science and Technology Department of Networking and Communication Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India A. v C. College of Engineering Department of Computer Science and Engineering Mayiladuthurai India Care College of Engineering Artificial Intelligence and Data Science Trichy India Care College of Engineering Department of Computer Science and Engineering Trichy India Indra Ganesan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350334357

The success of deep learning in a variety of fields has spurred a rush of desire to develop new novel recommender systems. As a result, our research introduces new RecDNNing, a ground-breaking technique that combines integrated people and products with a deep learning model. There are two phases to the suggested suggestion method. We begin by constructing a unique mathematical version for each human and item, which we refer to as user and item embedding. The embeddings of the goods and users then are aggregated, concatenated, and sent into the deep learning model. In the second phase, the spliced people and items implants are utilised as sources, and the ratings scores are forecasted using the forward propagation technique. The suggested new RecDNNing algorithm outperforms state-of-the-art techniques on MovieLens, according to the results. © 2023 IEEE.

关键词： Recommender systems

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Fededrated Learning: Research, Challenges, and Future Directions 5

Fededrated Learning: Research, Challenges, and Future Direct...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Yadav, Rishika Malik, Kiran Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering UP Greater Noida India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Mriem Department of Computer Science and Engineering Rohtak India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field's increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning's full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms. © 2024 IEEE.

关键词： data Analysis Distributed Environments Edge Computing Federated Learning Machine Learning

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