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Using Deep Learning depth Maps to Improve Monocular SLAM 38

Using Deep Learning Depth Maps to Improve Monocular SLAM

引用

38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Hollows, Simon Green, Richard University of Canterbury Dept. Mechatronics Engineering Christchurch New Zealand University of Canterbury Dept. Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes the use of a neural network to generate depth maps from 2D frames, offering an alternative to depth cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate depth data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between depth sensor depth maps in test datasets and the generated depth maps. The MiDaS neural network was used to generate estimated depth maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated depth maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM depth maps had a root mean square error (RMSE) or 0.24 m while the generated depth maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between depth maps causes this approach to fail in slow-moving scenes. © 2023 IEEE.

关键词： Cost effectiveness

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Driving Behavior Classification Using Smartphone Sensor Data 8

Driving Behavior Classification Using Smartphone Sensor Data

引用

8th International Conference on computer Science and engineering, UBMK 2023

作者： Dikbiyik, Deniz Alagoz, Fatih Boǧaziçi University Dept. of Software Engineering İstanbul Turkey Boǧaziçi University Dept. of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798350340815

Given the substantial number of accidents because of human error, the development of automatic driver behavior monitoring systems has become a pressing need. By providing real-time monitoring and analysis of driving behavior, these systems have the potential to decrease the incidence of accidents and improve overall road safety. This study presents a novel analysis framework for classifying driving behavior based on data gathered from passengers' smartphones. The data were collected using our mobile application installed on the smartphones and processed using machine learning algorithms. The study utilized several machine learning classification t echniques, w ith a focus on developing a Long Short Term Memory (LSTM) algorithm for improved accuracy. A Federated Learning algorithm was also developed to collect the data not into one area and train a global model to apply for labeling the driving behavior. The results show the efficacy of the proposed approach in accurately classifying driving behavior based on data obtained from smartphones. © 2023 IEEE.

关键词： Smartphones

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The Early Detection of Dementia Disease Using Machine Learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kabir, Md. Shariar Khanom, Jakia Bhuiyan, Md. Atik Tumpa, Zerin Nasrin Rabby, S.K. Fazlee Bilgaiyan, Saurabh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350348217

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia. © 2023 IEEE.

关键词： Machine learning

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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Mitigating YouTube Recommendation Polarity using BERT and K-Means Clustering 10

Mitigating YouTube Recommendation Polarity using BERT and K-...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ahmad, Zoheb Quasim, Danish Garg, Shreeya Kumar, Vinod Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Dept. of Computer Engineering New Delhi India Iiit Delhi Dept. of Computer Engineering and Bioscience New Delhi India

ISBN: (纸本)9789380544472

YouTube's recommendation system is famous for its success in maintaining high retention rates. The cause of its success is its ability to learn and predict an individual user's preferences appropriately. An unintended consequence, however, is that users get stuck in what is known as their own 'echo chambers' when dealing with and feeding users back their preferences. These echo chambers can cause increasing perspective bias within users, making it difficult for users to understand differing opinions. This work aims to prepare a model that counteracts YouTube's recommendation system by forcefully exposing users to content from varying viewpoints. The SSKA pipeline (Suno Sabki, Karo Apni) is a complementary deep learning model that involves Natural Language Processing (NLP) and K-Means clustering. It utilizes modern software libraries such as the YouTube API (Application Programming Interface) for data collection and was trained and tested on a varied set of users. The results prove that the model is successful in decreasing the bias recommendation by exposing users to the content of varying opinions and helping them break away from their echo chambers. The proposed methodology of explicitly exposing users to the content of varying opinions can positively impact local societies and the global community. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： K-means clustering

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Kubernetes Cluster Disaster Recovery Using AWS 7

Kubernetes Cluster Disaster Recovery Using AWS

引用

2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Bhavsar, Simran Agrawal, Apoorva Ropalkar, Tanvi Kamdi, Pranjali Hajare, Aparna Deshpande, Shilpa Rathi, Rahul Garg, Devendra Cummins College of Engineering for Women Dept. of Computer Engineering Pune India Veritas Software Technologies India Private Limited Pune India

ISBN: (纸本)9798350304268

As applications in IT have been spanning multiple containers across servers, kubernetes is being used extensively for automating software deployment, scaling and management. Kubernetes eases the container tasks and also other activities like horizontal scaling and canary development. To run cloud-native applications, kubernetes has become the de facto standard for container orchestration, regardless of the underlying platform. This makes it very crucial to have a disaster recovery plan for kubernetes environments. This paper proposes a system for backup and restore of a kubernetes cluster using the snapshot method along with the Public Key Infrastructure (PKI) certificates of a cluster. It also focuses on automating the backup and restore operations using microservices. Furthermore, it makes use of Amazon Web Services (AWS) tools for the reliability, scalability and the automation of backup and restore operations. The user can run the proposed microservices to backup and restore the cluster from any specific timestamp before the disaster. The experimental results demonstrate the effective performance of the proposed method in terms of efficiently restoring the kubernetes cluster in optimal time. © 2023 IEEE.

关键词： Cluster backup Cluster initialization Cluster setup Disaster recovery Kubernetes Master node Restore Worker node

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Enhancing Autism Education: Exploring Interactive Videos and AI Integration for Effective Teaching 15

Enhancing Autism Education: Exploring Interactive Videos and...

引用

15th International Conference on Innovations in Information Technology, IIT 2023

作者： Raeesi, Fatima Al Kuwaiti, Mariam K. Al Berengueres, Jose United Arab Emirates University Dept. of Software Engineering Al Ain United Arab Emirates United Arab Emirates University Dept. of Computer Science & Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

This research project proposes the use of interactive videos to enhance teachers' training in educating autistic children. Effective teaching methods for students with autism require teachers to comprehend the condition and employ tailored instructional strategies, including adapting assignments, aiding those with language difficulties, and utilizing visual aids for better organization and focus. Traditional teacher training methods can be both expensive and time-consuming. In contrast, interactive videos provide a proactive and flexible way to access training content, empowering teachers to engage with the material dynamically and take control of their learning experiences. Future work will explore the integration of AI-driven ChatGPT to offer personalized support and create a dynamic training program, with the goal of improving inclusivity and educational quality in autism settings while benefiting teachers, students, and the education system at large. © 2023 IEEE.

关键词： Students

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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Enhanced Reversible Data Hiding in Encrypted Images by Pixel Shifting 16

Enhanced Reversible Data Hiding in Encrypted Images by Pixel...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Yang, Ching-Nung Lu, Yu-Quan Liu, Shu-Yu Xiong, Lizhi Dept. of Computer Sci. and Inf. Engineering National Dong Hwa University Hualien Taiwan School of Computer and Software Nanjing University of Inf. Sci. & Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Reversible data hiding (RDH) is one special type of data hiding, and is widely used for many intended applications. Moreover, to protect the cover image, RDH in encrypted image (RDHEI) is accordingly proposed. With the reversibility and hiding data in encrypted image, RDHEI has potential utility in fields such as military and medical image transmission, and forensic analysis. Recently, Panchikkil et al. propose a RDHEI by using pixel swapping (RDHE-PSW) to swap some pixels in a block. The pixel swapping does not change pixel values, and thus Panchikkil et al.’s RDHEI-PSW can retain entropy and histogram. The smoothness of a block is calculated to extract the hidden bits and meantime recover the cover image. Panchikkil et al.’s RDHEI-PSW only hides two secret bits in every block. Recently, Kim et al. use pixel shifting (PSH), a circular right shift on a block, instead of pixel swapping to propose a RDHEI-PSH. In this paper, we still adopt PSH approach but use it in a different way (a modified PSH) to propose an enhanced RDHEI-PSH with a higher embedding capacity than Kim et al.’s RDHEI-PSH. Our enhanced RDHEI-PSH double the patterns on a block, on which we can embed more secret bits. Experimental results demonstrate that our enhanced RDHEI-PSH surpasses Panchikkil et al.’s RDHEI-PSW and Kim et al.’s RDHEI-PSH. © 2025 SPIE.

关键词： Military photography

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A Reinforcement-Learning Self-Healing Approach for Virtual Network Function Placement 36

A Reinforcement-Learning Self-Healing Approach for Virtual N...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Dept. of Systems and Computer Eng. Ottawa Canada Dept. of Software Engineering and It Montréal Canada

ISBN: (纸本)9781665477161

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism. © 2023 IEEE.

关键词： Resource allocation

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