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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： P Rajeshwari Pamujula Lakshmi Priya M Pooja G Abhishek Department of Computer Science Artificial Intelligence and Data Science Hyderabad India Department of Computer Science Artificial Intelligence and Machine Learning Hyderabad India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

Remote sensing technologies are integral to monitoring and analyzing Earth's surface, yet the effective extraction of information relies heavily on high-resolution imagery. This study proposes an innovative method for enhancing the spatial resolution of remote sensing images using the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) deep learning architecture. Adapted specifically for remote sensing applications, the model is trained on a dataset of low-resolution and high-resolution images, allowing it to learn the intricate relationships between them. The methodology addresses challenges such as limited training data and complex atmospheric conditions, offering promising solutions for enhancing the resolution of remote sensing imagery.

关键词： Earth Training Atmospheric modeling Superresolution Training data Generative adversarial networks Sensors

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Exploring PM2.5 Air Pollution and Its Impact on Health Using machine learning 5th

Exploring PM2.5 Air Pollution and Its Impact on Health Using...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Pati, Alok Kumar Tripathy, Alok Ranjan Tripathy, Alakananda Department of Computer Science Ravenshaw University Cuttack India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Centre for Artificial Intelligence and Machine Learning Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9789819795147

The increase in vehicle and industrial pollution in air is rising. Air pollution is the major factor driven behind the environmental pollution. An increase in air pollution leads to chronic lung diseases and lower respiratory. In order to check the pollution in time, it is necessary to know about it. Using historical data and machine leaning models, it becomes easier to track the daily increase in air pollution levels. This will help to restrict the air pollution. This paper focuses on the prediction of different pollutants affecting human lungs. Different regression algorithms have been used to predict the amount of particulate matter (PM2.5) in the air which leads to the pollution. This paper focuses on linear regression, ridge regression, lasso regression, support vector regression, and gradient boosting regression for analyzing and predicting air pollution levels. Gradient boosting regression has better accuracy as compared to other regression techniques. Cross-validation is used to ensure the data does not have any outliers. The different regression algorithms have been evaluated using different parameters like root mean squared error (RMSE), R-squared score, mean absolute error (MAE), and mean squared error (MSE). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Ant Lion Optimizer with Deep Transfer learning Model for Diabetic Retinopathy Grading on Retinal Fundus Images

Ant Lion Optimizer with Deep Transfer Learning Model for Dia...

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Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Presilla, R. Kallimani, Jagadish S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Visvesvaraya Technological University Karnataka Belagavi India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9789819955206

Diabetic retinopathy (DR) becomes a sight-threatening complication because of diabetes mellitus which affects the retina. Initial identification of DR turns out to be a significant one as it might cause permanent impaired vision in the late stages. The automatic grading of DR seems to have effective benefits in solving such impediments, like rising efficiency, scalability, and coverage of analyzing process, extending applications in developed areas, and enhancing patient prevention by offering premature diagnosis and referral. In recent times, the performances of deep learning (DL) systems in the analysis of DR are close to that of expert-level diagnoses for grading fundus images. This article introduces an Ant Lion Optimizer using ALODTL-DRG technique on retinal fundus images. The presented ALODTL-DRG model performs preprocessing via interpolation image resizing, weighted Gaussian blur, and CLAHE-based contrast enhancement. For feature extraction, Inception with ResNet-v2 model is utilized in this study. At last, the ALO algorithm can be exploited as a hyper parameter tuning strategy to accomplish enhanced DR detection performance. The experimental assessment of the ALODTL-DRG method can be tested by making use of benchmark datasets. A widespread comparison study stated the enhanced performance of the ALODTL-DRG model over recent approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Grading

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OncoNLP: Cancer Comprehend Annotation - a Pipeline for Cancer Phenotype and Clinical Extraction

OncoNLP: Cancer Comprehend Annotation - a Pipeline for Cance...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Thanh Duong Thanh Thieu Department of Computer Science and Engineering University of South Florida Tampa FL Department of Machine Learning Moffitt Cancer Center Tampa FL

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Information extraction from clinical text is needed to comprehend patient conditions and determine anticancer treatment. Existing NLP systems, such as Amazon Comprehend Medical, Clamp, and DeepPhe, are proprietary and suboptimal due to the shift in the textual distribution and expression of cancer phenotypes. We introduce OncoNLP, a natural language processing toolkit that comprises deep neural network BERT models designed to extract cancer phenotype and related biomedical information. Currently, the toolkit contains two primary components: a Biomedical BERT(BiomedBERT) model to extract general medical information and a Cancer Bert(CaBERT) model to identify the primary tumor site and histology. We evaluate the performance of BiomedBERT on Informatics for Integrating Biology & the Bedside (i2b2) dataset against Amazon Comprehend Medical and Clamp. BiomedBERT outperforms all other methods with an exact matching F1-score of 88.5%. Next, we evaluate CaBERT on a Moffitt dataset with over 2000 clinical notes against DeepPhe, which shows a 50% improvement in both tumor site and histology tasks. Lastly, we introduce a knowledge prompt with OncoNLP and engineer it for large language models. We name it OncoNLP-Assist, a chatbot system powered by OncoNLP and Llama2 that could extract information from electrical health records and interact with physicians.

关键词： Phenotypes Accuracy Biological system modeling Pipelines Medical services Oncology Data mining Medical diagnostic imaging Cancer Tumors

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GroundingMate: Aiding Object Grounding for Goal-Oriented Vision-and-Language Navigation

GroundingMate: Aiding Object Grounding for Goal-Oriented Vis...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liu, Qianyi Zhang, Siqi Qiao, Yanyuan Zhu, Junyou Li, Xiang Guo, Longteng Wang, Qunbo He, Xingjian Wu, Qi Liu, Jing Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Tongji University Department of Computer Science and Technology China Australian Institute for Machine Learning The University of Adelaide Australia

ISBN: (纸本)9798331510831

Goal-Oriented Vision-and-Language Navigation (VLN) aims to enable agents to navigate to specified locations and identify designated target objects following natural language instruction. This approach has gained popularity due to its close alignment with real-world scenarios. However, existing studies have predominantly focused on enhancing navigation performance, neglecting the ability to locate objects at the navigation endpoint. This oversight has resulted in a significant discrepancy between the success rates of navigation and object grounding. The challenge is compounded by the complex reasoning required by the instructions and the necessity to synthesize multiperspective images of objects, which overwhelms traditional object grounding methods. We leverage the Multi-Modal Large Language Model (MLLM) to bridge this gap, allowing agents to seek assistance from these models when struggling to locate the target object. The agent conducts a multi-stage evaluation to discern the cause of its confusion and promptly extracts and updates the most relevant information for MLLM to assess. Our method is plug-and-play and model-agnostic, facilitating integration with numerous existing VLN strategies without the need for retraining. Implementing our approach across four distinct methods has improved performance on the REVERIE and SOON datasets, demonstrating the effectiveness and generalizability of our technique. © 2025 IEEE.

关键词： Electric grounding

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Visual Computing Unified Application Using Deep learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks 2

End to End Model to Reduce the Inference, Jamming, and to In...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Dhananjaya, Shashank Narender, M. Sunitha, R. The National Institute of Engineering Department of Information Science and Engineering Mysore India The National Institute of Engineering Department of Computer Science and Engineering Mysore India B. N. M. Institute of Technology Dept. of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350306415

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has been challenged by the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization by allowing users to access unused spectrum bands on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all become key issues. Proposing a unique approach to address the interference problem produced by secondary nodes that have been hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ability to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have been working on reducing inference and the effect of jamming in the network. But still it is not resulted with the proper solution for the jamming problem in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security problem to increase the trust in the cognitive radio network. The secondary users must be able to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance by reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network. © 2024 IEEE.

关键词： Cognitive Radio Network Inference Jamming Q-learning Software Defined Radio Trust

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Differentiable quantum computing for large-scale linear control 24

Differentiable quantum computing for large-scale linear cont...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Connor Clayton Jiaqi Leng Gengzhi Yang Yi-Ling Qiao Ming C. Lin Xiaodi Wu Joint Center for Quantum Information and Computer Science and Department of Computer Science University of Maryland Joint Center for Quantum Information and Computer Science and Department of Mathematics University of Maryland and Department of Mathematics and Simons Institute for the Theory of Computing UC Berkeley Joint Center for Quantum Information and Computer Science and Department of Mathematics University of Maryland Department of Computer Science and Center for Machine Learning University of Maryland

ISBN: (纸本)9798331314385

As industrial models and designs grow increasingly complex, the demand for optimal control of large-scale dynamical systems has significantly increased. However, traditional methods for optimal control incur significant overhead as problem dimensions grow. In this paper, we introduce an end-to-end quantum algorithm for linear-quadratic control with provable speedups. Our algorithm, based on a policy gradient method, incorporates a novel quantum subroutine for solving the matrix Lyapunov equation. Specifically, we build a quantum-assisted differentiable simulator for efficient gradient estimation that is more accurate and robust than classical methods relying on stochastic approximation. Compared to the classical approaches, our method achieves a super-quadratic speedup. To the best of our knowledge, this is the first end-to-end quantum application to linear control problems with provable quantum advantage.

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On the Origins of Linear Representations in Large Language Models 41

On the Origins of Linear Representations in Large Language M...

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41st International Conference on machine learning, ICML 2024

作者： Jiang, Yibo Rajendran, Goutham Ravikumar, Pradeep Aragam, Bryon Veitch, Victor Department of Computer Science University of Chicago United States Machine Learning Department Carnegie Mellon University United States Booth School of Business University of Chicago United States Department of Statistics University of Chicago United States Data Science Institute University of Chicago United States

Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism to show that the next token prediction objective (softmax with cross-entropy) and the implicit bias of gradient descent together promote the linear representation of concepts. Experiments show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already suffices to yield linear representations. We additionally confirm some predictions of the theory using the LLaMA-2 large language model, giving evidence that the simplified model yields generalizable insights. Copyright 2024 by the author(s)

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 IEEE.

关键词： Fruits

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