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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association

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A Smart Electronic Wheelchair Design Using Artificial Intell...

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： R Balamurugan D. Kanchana B. Anni Princy J Jaisudha P. Malathi E Glory Department of Artificial Intelligence and Machine Learning AJK College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Applications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of ECE Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India Department of Biosciences Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair’s controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair’s DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user’s pulse rate, as well as the wheelchair’s functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner.

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Impact of Ensemble of Vector Embeddings on Speculative Retrieval Augmented Generation

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Impact of Ensemble of Vector Embeddings on Speculative Retri...

data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Sanjay Kukreja Tarun Kumar Vishal Bharate Rajat Singh Abhijit Dasgupta Debashis Guha Department of Machine Learning SP Jain School of Global Management Mumbai India COE AI-ML eClerx Services Ltd. Chandigarh India COE AI-ML eClerx Services Ltd. Pune India COE AI-ML eClerx Services Ltd. Mumbai India Department of Data Science SP Jain School of Global Management Mumbai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Recent developments in Natural Language Processing (NLP) have impacted all fields. Retrieval Augmented Generation (RAG) has played significant part in resent advancements in NLP applications. RAG architecture combines the powers of retrieval-based methods and generative models to increase the relevance and accuracy of generated texts. This paper deals with the performance comparison on speculative RAG, recent development in RAG systems, with different state-of-the-art vector embedding models and their ensembles. Two approaches were adopted to carry out this research: one-to-one mapping context with response-rationale and mapping of clustered contexts with response and rationale. The performance measures semantic answer similarity (SAS), and Rouge score on descriptive-type NCERT books dataset. State-of-the-art vector embedding models, BAAI/bge-base-en, thenlper/gte-base and intfloat/e5-base-v2, were used individually and to create average and weighted average ensemble *** responses of the systems corresponding to the 200 questions used for evaluation were validated against the NCERT Solution dataset. The result showed that the ensemble of vector embedding models outperforms state-of-the-art embedding models with weighted average ensemble achieving the highest accuracy. Further, the clustered context approach consistently outperforms the one-to-one mapping approach with noticeably higher SAS and Rouge metric values corresponding to each vector embedding method employed. For weighted average ensemble, the clustering approach improves the semantic answer similarity value by 8.24% over the one-to-one mapping approach. The performance of the systems may be improved by optimizing the weights of the weighted average ensemble and better clustering approaches. Various institutions using RAG systems for descriptive question-answer solutions may benefit from employing advancement in RAG systems.

关键词： Measurement Accuracy Retrieval augmented generation Semantics Vectors Natural language processing Cognitive informatics Synthetic aperture sonar

AI-DRIVEN AUTOMATED TOOL FOR ABDOMINAL CT BODY COMPOSITION ANALYSIS IN GASTROINTESTINAL CANCER MANAGEMENT

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arXiv 2025年

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. Research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

Performance Evaluation of Vector Embeddings with Retrieval-Augmented Generation

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Performance Evaluation of Vector Embeddings with Retrieval-A...

International Conference on Computers, Communications, and Systems (ICCCS)

作者： Sanjay Kukreja Tarun Kumar Vishal Bharate Amit Purohit Abhijit Dasgupta Debashis Guha Department of Machine Learning SP Jain School of Global Management Mumbai India COE AI-ML eClerx Services Ltd. Chandigarh India COE AI-ML eClerx Services Ltd. Pune India COE AI-ML eClerx Services Ltd. Mumbai India Department of Data Science SP Jain School of Global Management Mumbai India

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Vector embeddings form the basis of sophisticated language models. These language models were developed with the advent of developments in natural language processing (NLP) and aid in a variety of downstream tasks. Contextually relevant responses are improved using a combination of generation-based models and information retrieval, which is combined in the Retrieval-Augmented Generation (RAG) framework. The state-of-the-art research focuses on the RAG framework. Performance evaluation of vector embeddings in context with the RAG framework for data querying from documents is presented in this paper. The research encompasses a comparative analysis of various vector embeddings and their average, weighted average ensemble, evaluating their effectiveness in easing information retrieval and subsequent generation activities. The investigations focus on the impact of alternative embedding approaches on the overall performance of context generation across the NCERT books dataset using a systematic evaluation. The capabilities of ChatGPT and Llama2 are employed for evaluating the performance of embedding models. NCERT books form the underlying database, and LLM models are used to rank the contexts derived from the database. The optimized prompt is utilized to achieve ranking of the results. The same LLM is also utilized to generate the response for all the embedding models employing generated contexts. The variety in vector embedding approaches is exhibited by the experimental results.

关键词： Performance evaluation Systematics databases Communication systems Semantics Information retrieval Vectors

UCPM: Uncertainty-Guided Cross-Modal Retrieval with Partially Mismatched Pairs

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IEEE Transactions on Image Processing 2025年 34卷 3622-3634页

作者： Zha, Quanxing Liu, Xin Cheung, Yiu-Ming Peng, Shu-Juan Xu, Xing Wang, Nannan Huaqiao University Department of Computer Science Xiamen 361021 China Key Laboratory of Pattern Recognition and Computer Vision Xiamen 361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021 China Hong Kong Baptist University Department of Computer Science Hong Kong Huaqiao University Department of Artificial Intelligence Xiamen 361021 China Fujian Province University Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Xiamen 361021 China University of Electronic Science and Technology of China Center for Future Multimedia School of Computer Science and Engineering Chengdu 610051 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an 710071 China

The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i

关键词： Cross-modal retrieval partially mismatched pairs trusted margin loss uncertainty guided division uncertainty self-correction

"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event data"

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arXiv 2023年

作者： Rappl, Anja Kneib, Thomas Lang, Stefan Bergherr, Elisabeth Institute of Medical Informatics Biometry and Epidemiology Friedrich-Alexander Universität Erlangen-Nürnberg Germany Chair of Statistics Georg-August-Universität Göttingen Germany Department of Statistics Universität Innsbruck Austria Chair of Spatial Data Science and Statistical Learning Georg-August-Universität Göttingen Germany

Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazards formulation of the time-to-event part of the model is accompanied by computational challenges. We propose a joint model with a piece-wise exponential formulation of the hazard using the counting process representation of a hazard and structured additive predictors able to estimate (non-)linear, spatial and random effects. Its capabilities are assessed in a simulation study comparing our approach to an established one and highlighted by an example on physical functioning after cardiovascular events from the German Ageing Survey. The Structured Piece-wise Additive Joint Model yielded good estimation performance, also and especially in spatial effects, while being double as fast as the chosen benchmark approach and performing stable in imbalanced data setting with few events. © 2023, CC BY.

关键词： Additives

ACTIONS ON CLASSIFIABLE C*-ALGEBRAS WITHOUT EQUIVARIANT PROPERTY (SI)

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arXiv 2024年

作者： Gardella, Eusebio Kranz, Julian Vaccaro, Andrea Department of Mathematical Sciences Chalmers University of Technology University of Gothenburg GothenburgSE-412 96 Sweden Data Science: Machine Learning and Data Engineering Prof. Dr. Fabian Gieseke Leonardo-Campus 3 Münster48149 Germany Mathematisches Institut Fachbereich Mathematik Informatik der Universität Münster Einsteinstrasse 62 Münster48149 Germany

We exhibit examples of actions of countable discrete groups on both simple and non-simple nuclear stably finite C*-algebras that are tracially amenable but not amenable. We furthermore obtain that, under the additional assumption of strict comparison, amenability is equivalent to tracial amenability plus the equivariant analogue of Matui-Sato's property (SI). By virtue of this equivalence, our construction yields the first known examples of actions on classifiable C*-algebras that do not have equivariant property (SI). We moreover show that such actions can be chosen to absorb the trivial action on the universal UHF algebra, thus proving that equivariant Z-stability does not in general imply equivariant property (SI). Copyright © 2024, The Authors. All rights reserved.

关键词： Algebra

Parkinson’s Disease Detection using Hand Drawn Spirals and Waves

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Parkinson’s Disease Detection using Hand Drawn Spirals and ...

Recent Advances in science and Engineering Technology (ICRASET), International Conference on

作者： Shashidhara H S Saish K Kalbag Tanya Mathias Srinivasa K G Niranjanamurthy M Department of Informaition Science and Engineering M S Ramaiah Institute of Techology and Engineering Affiliated to Visvesvaraya Technological University Belgaum Bangalore India Department of Data Science and Artififial Intelligece International Institute of Information Technology Naya Raipur Chhattisgarh India Department of Artificial Intelligence and Machine Learning B M S Institute of Technology and Management Affiliated to Visvesvaraya Technological University Belgaum Bangalore India

Parkinson’s disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson’s disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson’s disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson’s disease by analyzing hand-drawn spirals and waves in photographs.

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Your Transformer May Not be as Powerful as You Expect

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arXiv 2022年

作者： Luo, Shengjie Li, Shanda Zheng, Shuxin Liu, Tie-Yan Wang, Liwei He, Di Key Laboratory of Machine Perception MOE School of Intelligence Science and Technology Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States Microsoft Research United States Center for Data Science Peking University China Zhejiang Lab China

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power of RPE-based Transformers regarding whether the model is capable of approximating any continuous sequence-to-sequence functions. One may naturally assume the answer is in the affirmative-RPE-based Transformers are universal function approximators. However, we present a negative result by showing there exist continuous sequence-to-sequence functions that RPE-based Transformers cannot approximate no matter how deep and wide the neural network is. One key reason lies in that most RPEs are placed in the softmax attention that always generates a right stochastic matrix. This restricts the network from capturing positional information in the RPEs and limits its capacity. To overcome the problem and make the model more powerful, we first present sufficient conditions for RPE-based Transformers to achieve universal function approximation. With the theoretical guidance, we develop a novel attention module, called Universal RPE-based (URPE) Attention, which satisfies the conditions. Therefore, the corresponding URPE-based Transformers become universal function approximators. Extensive experiments covering typical architectures and tasks demonstrate that our model is parameter-efficient and can achieve superior performance to strong baselines in a wide range of applications. The code will be made publicly available at https://***/lsj2408/URPE. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems