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International Conference on Computing and Networking Technology (ICCNT)

作者： D Pradeep Kumar B J Sowmya Ashvin Vincent Aravindh Pandiyan Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bangalore India Machine Learning Engineer Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

One common clinical symptom seen in Parkinson’s disease (PD) patients is freezing of gait (FOG). It manifests as an irregular gait, marked by abrupt, involuntary stopping of movement during gait episodes. FOG entails a major negative effect on PD patients’ quality of life. In this work, we analyze the movement data that is collected using the lower-back 3D accelerometer worn by the patients during FOG episodes. By employing machine learning methods to classify this data, we aim to accurately detect FOG trends. In particular, we focus on Deep learning-based techniques, specifically an attention-based 1D Convolutional Neural Network (1D-CNN), due to their capacity to extract intricate features from unprocessed sensor input. Our results indicate that the proposed model achieves AUC scores of 0.9032 with respect to the TDCSFOG dataset and 0.8682 for the DeFOG dataset, for a combined average AUC of 0.8857. Mean Average Precision (mAP) scores for TDCSFOG and DeFOG reach 0.8388 and 0.779, respectively, for an average of 0.8089, showing the ability of the model to predict FOG occurrences over time. These findings suggest techniques based on Deep learning enhance FOG detection, allowing clinicians to more effectively tailor treatments and care strategies for Parkinson’s patients, thus enhancing their ability to move and overall well-being.

关键词： Deep learning Accelerometers Three-dimensional displays Accuracy Predictive models Feature extraction Transformers Convolutional neural networks Forecasting Diseases

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Semantic Segmentation of Areal Images using Pixel Wise Segmentation

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Procedia computer science 2025年 259卷 463-472页

作者： Swathi Gowroju V. Swathi Narsimhulu K. Shilpa Choudhary Associate Professor Department of Artificial Intelligence & Machine Learning Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Matrusri Engineering College Hyderabad Telangana Associate Professor Department of Computer Science NGIT Hyderabad Telangana

Particularly in regions hit by flooding, precise semantic segmentation of aerial photographs is essential for post-disaster evaluation and urban planning. The high-resolution aerial photography collection FloodNet, created for pixel-by-pixel segmentation of flooded and non-flooded landscapes, is presented in this work. Aerial photos taken from various geographic regions make up the collection, and segmentation masks are used to detect flooded roads, houses, cars, plants, and other vital infrastructure. In order to conduct semantic segmentation on aerial photos and capture the fine-grained information necessary for flood damage assessments, the suggested model makes use of a U-Net architecture. Morphological post-processing is also used to improve object delineation and smooth segmentation borders. The model showed consistent performance with little overfitting, achieving training accuracy of 84% and validation accuracy of 81% over 200 epochs. Effective generalization to unknown data was demonstrated by the validation loss stabilizing at 0.55 and the training loss declining from 1.1 to 0.45. The findings show that the suggested method can reliably identify and categorize areas damaged by flooding from aerial photos, enabling automated flood assessment and speeding up disaster relief activities. There is a lot of promise for integrating this model and dataset combination into real-time catastrophe monitoring, urban planning, and geospatial analytic pipelines.

关键词： Satellite images areal images semantic segmentation UNet using morphology Pixel wise segmentation

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Leveraging Patient Flow Data to Reduce Hospital Readmissions: A Study Using Medical Dashboards

Leveraging Patient Flow Data to Reduce Hospital Readmissions...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Santoshi Deshmukh Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Transparent reporting of patient outcomes is becoming more and more common as the inequalities in health outcomes amongst groups are being more widely acknowledged. In addition, documenting health outcomes over time across subgroups will be necessary for pay-for-equity programs like those put forth by the Centers for Medicare and Medicaid. One way to visualize data in the context of healthcare is through dashboards, which may be used to identify important differences in clinical outcomes, direct focused efforts toward quality improvement, and eventually enhance health equity. We assess every study that has documented the effective creation of a disparity dashboard in this viewpoint, and we also publish the information gathered and any unexpected repercussions that have been mentioned. We offer a framework for the systematic enhancement of equity via reward systems to lessen health inequalities and incentives for gathering and reporting health data. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these executives serve as the company's role models. The beliefs, vision, and leadership style of healthcare professionals greatly influence their behavior and performance. Competent leaders who inspire and empower their staff foster a culture of responsibility, creativity, and cooperation. They exhort healthcare professionals to put their patients' safety, satisfaction, and well-being first. Strategic planning, resource allocation, and decision-making are all components of good leadership that support the hospital's goal of offering the highest quality of care. essential elements of efficient hospital management. A sophisticated approach is necessary to address the impact that hospital leadership has on patient outcomes. Because they embody patient-centered care in their everyday lives, these ex

关键词： Leadership Sentiment analysis Systematics Hospitals Decision making Strategic planning Safety Resource management Predictive analytics Creativity

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Multimodal Gas Detection Using E-Nose and Thermal Images:An Approach Utilizing SRGAN and Sparse Autoencoder

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computers, Materials & Continua 2025年第5期83卷 3493-3517页

作者： Pratik Jadhav Vuppala Adithya Sairam Niranjan Bhojane Abhyuday Singh Shilpa Gite Biswajeet Pradhan Mrinal Bachute Abdullah Alamri Artificial Intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India Symbiosis Centre of Applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringUniversity of Technology SydneyUltimoNSW 2007Australia Department of Geology and Geophysics College of ScienceKing Saud UniversityRiyadh11362Saudi Arabia

Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time *** fusion of these modalities can result in the development of highly accurate diagnostic *** low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the *** objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal *** dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature *** Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal *** autoencoder was trained on the grayscale and colorized thermal *** SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of *** autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of *** multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,***,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.

关键词： Thermal imaging gas detection multimodal learning generative models autoencoders

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SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net

SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Helin Cao Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We introduce SLCF-Net, a novel approach for the Semantic Scene Completion (SSC) task that sequentially fuses LiDAR and camera data. It jointly estimates missing geometry and semantics in a scene from sequences of RGB images and sparse LiDAR measurements. The images are semantically segmented by a pre-trained 2D U-Net and a dense depth prior is estimated from a depth-conditioned pipeline fueled by Depth Anything. To associate the 2D image features with the 3D scene volume, we introduce Gaussian-decay Depth-prior Projection (GDP). This module projects the 2D features into the 3D volume along the line of sight with a Gaussian-decay function, centered around the depth prior. Volumetric semantics is computed by a 3D U-Net. We propagate the hidden 3D U-Net state using the sensor motion and design a novel loss to ensure temporal consistency. We evaluate our approach on the SemanticKITTI dataset and compare it with leading SSC approaches. The SLCF-Net excels in all SSC metrics and shows great temporal consistency.

关键词： Measurement Image segmentation Three-dimensional displays Laser radar Semantics Pipelines Robot sensing systems

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics

A Comparison of Prompt Engineering Techniques for Task Plann...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jonas Bode Bastian Pätzold Raphael Memmesheimer Sven Behnke Autonomous Intelligent Systems group Computer Science Institute VI – Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence and Center for Robotics University of Bonn Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt_Engineering.

关键词： Knowledge engineering Service robots Large language models Instruments Humanoid robots Reliability engineering Time measurement Planning Prompt engineering Tuning

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SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Net

arXiv

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arXiv 2024年

作者： Cao, Helin Behnke, Sven Autonomous Intelligent Systems group Computer Science Institute VI-Intelligent Systems and Robotics Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

关键词： Semantics

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Sentiment Analysis Using machine learning Methods

Sentiment Analysis Using Machine Learning Methods

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Badholia, Abhishek Diwan, Tarun Dhar Narooka, Preeti Khatkale, Pravin B Vishnoi, Ankit Kaushik, Keshav Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Atal Bihari Vajpayee University Bilaspur India Manipal University Artificial Intelligence and Machine Learning School of Computer and Engineering Jaipur India Sanjivani University Maharashtra Kopargaon India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Amity University Punjab Amity School of Engineering and Technology Mohali India

ISBN: (纸本)9798350390049

machine learning (ML) will be utilized to evaluate sentiment analysis in this project. This study aims to do this. Sentiment analysis is a popular natural language processing area. This is a natural language processing subfield. This section identifies textual senses. Due to the growing availability of digital text across platforms, sentiment analysis is now vital for many applications. This is because sentiment analysis has become essential. This technology might be used for market research, political analysis, and consumer feedback. A few instances. This study analyzes several machine learning methods, to name a few. This category includes SVM, Naive Bayes, Random Forest, and RNNs. To clarify, several datasets are used to assess these methodologies. To get more information. Preprocessing and feature engineering are crucial to sentiment analysis model construction, as this paper shows. The research emphasises these two phases. In addition, it provides insights into how these approaches are used in constantly changing digital situations. © 2024 IEEE.

关键词： Adversarial machine learning

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Feasible Virtual Reality Application for Special Kids 4

Feasible Virtual Reality Application for Special Kids

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4th International Conference on Emerging Research in Electronics, computer science and Technology, ICERECT 2022

作者： Ajitha, S. Balakrishna, M. Niranjanamurthy, M. Ramaiah Institute of Technology Department of Master of Computer Application Karnataka Bengaluru560054 India BMS Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9781665456357

When caring for special kids with neuro-development disorders, much needs to be done as there is no medical test to detect some of the special needs. The main problems faced by special kids are social skills and learning disabilities. They cannot communicate like others in the real physical world. To develop these skills a virtual imaginary world or virtual reality can be used where they will learn skills in a virtual manner. It reduces the fear of social intimacy. Augmented-Reality and Virtual-Reality are used to develop their skills and provide a platform for phobia treatments too. These technologies give the kids simple strategies that lead to awareness and acceptance of the actual physical world by stimulation in the virtual world. The use of Augmented-Reality and Virtual-Reality in the field of teaching special kids improves the concepts of understanding. Rather than learning from old or domestic teaching methods technology, where the stimulation of the actual world and the objects in it can enhance the experience of learning and understanding and implementing the knowledge or the discipline that occurred from the process in the real world without the fear of being wrong and removes the margin of the mistakes. This leads to a child having a normal and educational life. In this article proposed Feasible Virtual Reality Application for Special Kids. © 2022 IEEE.

关键词： Augmented reality

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MOTPose: Multi-object 6D Pose Estimation for Dynamic Video Sequences using Attention-based Temporal Fusion

MOTPose: Multi-object 6D Pose Estimation for Dynamic Video S...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Arul Selvam Periyasamy Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Cluttered bin-picking environments are challenging for pose estimation models. Despite the impressive progress enabled by deep learning, single-view RGB pose estimation models perform poorly in cluttered dynamic environments. Imbuing the rich temporal information contained in the video of scenes has the potential to enhance models’ ability to deal with the adverse effects of occlusion and the dynamic nature of the environments. Moreover, joint object detection and pose estimation models are better suited to leverage the co-dependent nature of the tasks for improving the accuracy of both tasks. To this end, we propose attention-based temporal fusion for multi-object 6D pose estimation that accumulates information across multiple frames of a video sequence. Our MOTPose method takes a sequence of images as input and performs joint object detection and pose estimation for all objects in one forward pass. It learns to aggregate both object embeddings and object parameters over multiple time steps using cross-attention-based fusion modules. We evaluate our method on the physically-realistic cluttered bin-picking dataset SynPick and the YCB-Video dataset and demonstrate improved pose estimation accuracy as well as better object detection accuracy.

关键词： Deep learning Accuracy Fuses Aggregates Pose estimation Video sequences Object detection

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