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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Sudharson, D. Jananiksha, P. Mukilarasu, R. Sushmita, V. Vaishali, V. Rini Vaishnavi, S.R. Kumaraguru College of Technology Artificial Intelligence and Data Science Coimbatore India Vellore Institute of Technology CSE - Artificial Intelligence and Machine Learning Vellore India

ISBN: (纸本)9798331534950

The potential for transformative urban management is enhanced through the integration of IoT and AI, creating smart ecosystems that revolutionize intelligent lighting systems. This paper explores predictive maintenance solutions for urban street lighting, leveraging advanced machine learning models such as Temporal Fusion Transformer (TFT), Temporal Aggregation and Decomposition-based Attention (TADA), and Graph Neural Networks (GNNs). By analyzing historical operational data, these models detect subtle fault patterns and predict failures, improving system reliability, reducing maintenance costs, and optimizing energy use. The work contributes to United Nations SDGs 11 and 9 by promoting sustainable urban infrastructure and smart city development, fostering a future where IoT and AI systems enhance sustainability and resilience. © 2024 IEEE.

关键词： Sustainable city

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Automated Quality Assessment Using Appearance-Based Simulations and Hippocampus Segmentation on Low-Field Paediatric Brain MR Images 1st

Automated Quality Assessment Using Appearance-Based Simulati...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2024

作者： Sundaresan, Vaanathi Dinsdale, Nicola K Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031830105

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%, thus ranking in the 1st place in the LISA2024 Challenge. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

An Empirical Analysis on Spatial Reasoning Capabilities of L...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs' spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our new benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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On Lp - Lp′ estimates for a class of strongly damped wave equations

arXiv

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arXiv 2024年

作者： Mohamad, Haidar Mathematical Institute for Machine Learning and Data Science KU Eichstätt–Ingolstadt Ingolstadt85049 Germany

The purpose of this paper is to obtain a fundamental Lp - Lp0 estimate for a class of a strongly damped wave equations where the damping operator is given by -δ∆ with δ ≥ 0 and the constant in the estimate is independent of the damping parameter δ. The method used here is based on an estimate of the semigroup associated to the linear equation on the Besov spaces and their elementary *** Codes 35L05, 35L71, 35A99, 35B40 Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Structural Breakpoint Detection in Noisy Time Series Using an Augmented Matrix Profile Index

Structural Breakpoint Detection in Noisy Time Series Using a...

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IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Marco Fuchs Mathematical Institute for Machine Learning and Data Science Catholic University of Eichstätt-Ingolstadt Ingolstadt Germany

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

We introduce a method for partitioning a time series into segments. The method extends the recently introduced Fast Low-Cost Semantic Segmentation (FLUSS) algorithm to increase its robustness against noise and to automatically learn two required hyperparameters. FLUSS is part of the Matrix Profile framework and conceptualises the relationship between every subsequence and its nearest-neighbor as an arc spanning over the time series. It then determines segment boundaries by selecting time indexes which are spanned over by significantly fewer arcs than an average index, such that most subsequences on either side of the time index find their closest neighbor at the same side of the index. We extend FLUSS in two directions. First, we consider the Z nearest neighbors of each subsequence to compute its neighbors’ concentration along the time series as a measure for how characteristic a sequence itself is for a particular segment. This measure is then used to filter out non-characteristic sequences, aimed at increasing the robustness of the segmentation against noise. Second, FLUSS requires the user to set two important yet hard to determine hyperparameters, namely the number of segments and the length of exclusion zones around the index of an already found segment boundary. We introduce a procedure to learn both hyperparameters implicitly by setting the tolerance parameter of an iterative end-point fit algorithm, which extracts (local) minima from a meta time series denoting the number of arcs that cross a time series index. We test the method on a set of 32 diverse and publicly available univariate time series, where it demonstrates promising results.

关键词： Sensitivity Semantic segmentation Time series analysis Noise Robustness Time measurement Partitioning algorithms Indexes Noise measurement Unsupervised learning

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System for Identifying and Avoiding Obstructions on Railway Tracks in Forest Areas 10

System for Identifying and Avoiding Obstructions on Railway ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemanthkumar, P.B. Benjamin, Ruth Moly Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

Railways often faces the challenge of obstructions on the tracks especially passing through the forest area as not much precautions or announcements will be given to there also animals won't be able to understand the arrival of the train. Which results a significant safety risk to trains, travelling passengers and intruding creatures. This paper addresses for identifying the obstacles on railway tracks especially in forest areas which helps to take necessary actions to avoid those obstructions above. The system needs a camera, that has to be installed on the adjacent sides of the railway path which captures the real time data of the track. This captured data is then processed and analyzed to identify any potential obstruction on the tracks using Convolutional Neural Networks You Only Look Once version 8 is used. YOLOv8 is a high speed and accurate model;is trained with a custom dataset which is proposed in this paper. If any obstruction is detected it will inform to the train pilot and nearest stations for testing purpose mailing service is used which helps in taking necessary actions. With appropriate alerts will improve safety and efficiency on railway tracks in the forest areas by providing real time information about potential obstructions on the tracks. This directly helps in preventing accidents and delays. Ensures a safer and more reliable railway network. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Radiological Anomaly Detection using Optimized YOLO-NAS Model

Enhanced Radiological Anomaly Detection using Optimized YOLO...

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

作者： Leka Shree, J. Joy, Emmanuel Sri Ram, M.S. Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Division Of Artificial Intelligence And Machine Learning Karunya Institute Of Technology And Sciences Coimbatore India

ISBN: (纸本)9798350371567

Chest X-rays are commonly used to detect lung diseases. Chest radiographs often have a lot of perceptual errors. The application of image processing in diagnosing lung diseases has improved medical imaging. This has also enabled the creation of databases for classifying chest radiographs. However, current methods are not accurate enough to reliably detect anomalies. Aimed at improving the shortcomings of existing techniques, the proposed approach uses the Automated Neural Architecture Construction (AutoNAC) engine, a proprietary of Neural Architecture Search (NAS). Comparative evaluations against established models, including YOLOv3, YOLOv5, YOLOv7, and YOLOv8, showcase the superiority of YOLO-NAS in mean Average Precision (mAP@0.5), precision, and recall metrics. The originality of this study lies in introducing NAS specifically AutoNAC, to optimize neural network architectures for chest X-ray anomaly detection, marking a significant advancement in diagnostic capabilities in medical imaging. © 2024 IEEE.

关键词： Anomaly detection

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WebSight: An AI-based Approach to Enhance Web Accessibility for the Visually Impaired

WebSight: An AI-based Approach to Enhance Web Accessibility ...

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2024 International Conference on science, Technology, Engineering and Management, ICSTEM 2024

作者： Sri Ram, M.S. Joy, Emmanuel Shree, Leka J. Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

Web accessibility plays a significant role in shaping our online experiences in today's ever-changing digital world. People with vision impairmentsstill have trouble using most websites. This paper introduces WebSight an innovative AI-based Image Description Generator web extension to address these challenges. By seamlessly interfacing with a Flask server, Optical Character Recognition, and a dedicated Image Description Generator model, this browser extension systematically enhances web accessibility by methodically replacing 'alt' attributes of images with meticulously generated descriptions. This approach significantly contributes to web inclusivity, providing visually impaired users with accurate and contextually rich information. The extension's commitment to inclusivity is further demonstrated through the intelligent integration of Tesseract-OCR and Text-to-Speech ensuring a novel and far-reaching impact, offering an enhanced and tailored browsing experience for users with visual impairments. © 2024 IEEE.

关键词： Optical character recognition

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Predicting Indian Sign Language Gestures Using Deep learning and machine learning Techniques 4

Predicting Indian Sign Language Gestures Using Deep Learning...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Fulkar, Bhushan Burle, Rushikesh Mahale, Pramod Pacaharaney, Utkrsha Datta Meghe institute of Higher Education and Research Department of Artificial intelligence And Data science Wardha India Department of Artificial Intelligence And Machine Learning Maharashtra Wardha India Datta Meghe institute of Higher Education and Research Department of Artificial Intelligence And Machine Learning Wardha India

ISBN: (纸本)9798331540364

Indian Sign Language is the language used by people with disabilities in India. Unfortunately, the general public's ignorance of sign language makes it difficult for the especially abled and the general public to communicate. We are releasing a strategy to reduce this difference. We have adopted two distinct strategies to deal with this problem. first by employing the depth+RGB data acquired with a Microsoft Kinect to make gesture predictions in real-time. To segment the hand region from the RGB-D camera data, we used affine transformation and 3D reconstruction to map the RGB and depth information. Convolutional neural networks were trained with inputs that were segmented hand images or videos. With 50 static Indian Sign Language hand gestures used for training, a 97.18% classification accuracy was achieved on test data. Furthermore, this model achieved a 97.71 % classification accuracy when we transferred learning American Sign Language. A convolutional kernel and an LSP were used to train twenty dynamic gestures. This model's classification accuracy was 98.88%. Nevertheless, as soon as we implemented this system, we found that the methodology was flawed. Essentially, it is absurd to need to carry around both a bulky Microsoft Kinect and a device capable of performing the computation required for human-to-person interaction. We attempted to address this issue by using semantic segmentation of the hands. U-Net and ResNet 101 were combined. © 2024 IEEE.

关键词： Affine transforms

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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