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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

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Electric Vehicle Charging Demand Prediction using Multiresol...

Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. These issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. The data is gathered from a dataset on EV charging. The data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). The proposed MSNN-AO is utilized within the MATLAB platform. The proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. The MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. This highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. The MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model – Bringing Motion Generation to the Clinical Domain

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arXiv 2025年

作者： Adeli, Vida Mehraban, Soroush Mirmehdi, Majid Whone, Alan Filtjens, Benjamin Dadashzadeh, Amirhossein Fasano, Alfonso Iaboni, Andrea Taati, Babak University of Toronto Computer Science Department Canada Vector Institute Canada KITE Research Institute UHN Canada University of Toronto Institute of Biomedical Engineering Canada University of Bristol School of Computer Science United Kingdom University of Bristol Translational Health Science United Kingdom University of Toronto Data Sciences Institute Canada University of Toronto Department of Medicine Division of Neurology Canada Krembil Research Institute UHN Canada Edmond J. Safra Program in Parkinson’s Disease UHN Canada University of Toronto Department of Psychiatry Canada Centre for Mental Health UHN Canada

Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson’s Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias. To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis. © 2025, CC BY-NC-SA.

关键词： Pathology

Mobile Augmented Reality Framework with Fusional Localization and Pose Estimation

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arXiv 2025年

作者： Hou, Songlin Lin, Fangzhou Huang, Yunmei Peng, Zhe Xiao, Bin Department of Computer Science Worcester Polytechnic Institute Worcester01609 United States Department of Robotics Engineering Worcester Polytechnic Institute Worcester01609 United States Unprecedented-scale Data Analytics Center Tohoku University Sendai980-8577 Japan Department of Forestry and Natural Resources Purdue University West Lafayette47907 United States Department of Computing The Hong Kong Polytechnic University 999-077 Hong Kong

As a novel way of presenting information, augmented reality (AR) enables people to interact with the physical world in a direct and intuitive way. While there are some mobile AR products implemented with specific hardware at a high cost, the software approaches of AR implementation on mobile platforms(such as smartphones, tablet PC, etc.) are still far from practical use. GPS-based mobile AR systems usually perform poorly due to the inaccurate positioning in the indoor environment. Previous vision-based pose estimation methods need to continuously track predefined markers within a short distance, which greatly degrade user experience. This paper first conducts a comprehensive study of the state-of-the-art AR and localization systems on mobile platforms. Then, we propose an effective indoor mobile AR framework. In the framework, a fusional localization method and a new pose estimation implementation are developed to increase the overall matching rate and thus improving AR display accuracy. Experiments show that our framework has higher performance than approaches purely based on images or Wi-Fi signals. We achieve low average error distances (0.61-0.81m) and accurate matching rates (77%-82%) when the average sampling grid length is set to 0.5m. © 2025, CC0.

关键词： Wi-Fi

Detection of Breast Cancer using Quasi-Cross Focused Transformer-Guided Quantum Network with Lotus Optimization

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Detection of Breast Cancer using Quasi-Cross Focused Transfo...

International Conference on ent Computing and Control Systems (ICICCS)

作者： Rajesh Munirathnam Vishal Shukla Deepashree Devaraj Chintan Thacker Ujwal Nandekar Natrayan L Data & Analytics Division TechnoGen Inc. Hillsborough New Jersey USA Department of Mechanical Engineering Ramdeobaba College of Engineering and Management Nagpur Maharashtra Electronics and Instrumentation Engineering RV College of Engineering Bengaluru Karnataka Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat Symbiosis Centre for Advanced Legal Studies and Research (SCALSAR) Symbiosis Law School Pune Symbiosis International (Deemed University) Pune Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai TamilNadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Patient outcomes improve if breast cancer is discovered early. Although many deep learning methods have been studied for breast cancer identification, the efficacy of many has been limited. It presents a new method encompassing the Quasi Cross Focused Transformer Guided Quantum Network with Lotus Optimization (QCFT-GQNet-LO) which is novel to resolve these issues. This technique uses Quasi-Cross Filtering (QCF) for image pre-processing, which improves image quality by reducing noise and artefacts. It was evaluated on histopathological images from the MIAS (Mammographic Image Analysis Society) dataset. A Focused Transformer with U-Net (FTUNet) is utilized to precisely segment malignant regions, guaranteeing precise identification of crucial places. The Self-Guided Quantum Network with Triple Attention (SGQNet-TA) controls classification and feature extraction, significantly improving the model's diagnostic abilities. The whole system is optimized using the Lotus Effect Optimization Algorithm (LEOA) to improve the efficiency and robustness. The QCFT-GQNetLO approach is shown to outperform other approaches, with experimentally demonstrated accuracy of 99.9%, and recall rate of 99.8% on the MIAS dataset. This significantly improves diagnostic precision of lesion type categorization and early breast malignancy diagnosis in histopathological images.

关键词： Image segmentation Accuracy Filtering Noise Transformers Breast cancer Robustness Quantum networks Lesions Optimization

Planning with Diffusion Models for Target-Oriented Dialogue Systems

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arXiv 2025年

作者： Du, Hanwen Peng, Bo Ning, Xia Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States

Target-Oriented Dialogue (TOD) remains a significant challenge in the LLM era, where strategic dialogue planning is crucial for directing conversations toward specific targets. However, existing dialogue planning methods generate dialogue plans in a step-by-step sequential manner, and may suffer from compounding errors and myopic actions. To address these limitations, we introduce a novel dialogue planning framework, DiffTOD, which leverages diffusion models to enable non-sequential dialogue planning. DiffTOD formulates dialogue planning as a trajectory generation problem with conditional guidance, and leverages a diffusion language model to estimate the likelihood of the dialogue trajectory. To optimize the dialogue action strategies, DiffTOD introduces three tailored guidance mechanisms for different target types, offering flexible guidance towards diverse TOD targets at test time. Extensive experiments across three diverse TOD settings show that DiffTOD can effectively perform non-myopic lookahead exploration and optimize action strategies over a long horizon through non-sequential dialogue planning, and demonstrates strong flexibility across complex and diverse dialogue scenarios. Our code and data are accessible through https://***/r/DiffTOD. © 2025, CC BY.

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Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance

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arXiv 2025年

作者： Chen, Ziqi Peng, Bo Zhai, Tianhua Adu-Ampratwum, Daniel Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Medicinal Chemistry and Pharmacognosy The Ohio State University ColumbusOH43210 United States Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSMol to facilitate drug development. DiffSMol generates 3D binding molecules based on the shapes of known ligands. DiffSMol encapsulates geometric details of ligand shapes within pre-trained, expressive shape embeddings and then generates new binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively via shape guidance to better resemble the ligand shapes. It also tailors the generated molecules toward optimal binding affinities under the guidance of protein pockets. Here, we show that DiffSMol outperforms the state-of-the-art methods on benchmark datasets. When generating binding molecules resembling ligand shapes, DiffSMol with shape guidance achieves a success rate 61.4%, substantially outperforming the best baseline (11.2%), meanwhile producing molecules with novel molecular graph structures. DiffSMol with pocket guidance also outperforms the best baseline in binding affinities by 13.2%, and even by 17.7% when combined with shape guidance. Case studies for two critical drug targets demonstrate very favorable physicochemical and pharmacokinetic properties of the generated molecules, thus, the potential of DiffSMol in developing promising drug candidates. © 2025, CC BY-NC-SA.

关键词： Ligands

Novel Antenna Designs for High data Rate Body-Centric Communications in Wearable Devices

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Novel Antenna Designs for High Data Rate Body-Centric Commun...

Automation and Computation (AUTOCOM), International Conference on

作者： Ishika Soni Manjunatha R V Vijaya Baskar Daljeet Pal Singh Komal Parashar Niranjan Panda Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Data analytics and Mathematical Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

There is an ever-increasing demand for high data rate communication embedded in wearable devices owing to the exponential growth of smart devices and related technology. The human body is always around, which means that traditional antenna designs will not work well with body-centric communications operations, as signal loss and interference from the presence of the human body must be addressed. This project addresses this limitation by designing new body-centric communications antenna designs for incorporation into wearable devices. Antennas proposed in the literature can use novel techniques that reach high data rates while providing reliable communication links and features like compactness, low power consumption, and multi-band functionalities. The designs are optimized to ensure that the human body has a minimal effect on the signal enhancing performance and stability. The antennas are designed and simulated using advanced simulation tools and validated in the lab. These findings demonstrate the remarkable advantages of high-speed data transmission and link quality compared to traditional antennas. It contributes to developing novel antenna designs that wearable device manufacturers can incorporate into high data rate body-centric communications, enabling dramatically improved performance within tight space constraints. This will make it possible to create next-generation wearable devices with communication functions for applications ranging from healthcare and sports to virtual reality.

关键词： Performance evaluation Training Design methodology Transmitting antennas Clothing Virtual reality Stability analysis Reliability Wearable devices Antennas

Using a statewide health information exchange to Enhance COVID research and Surveillance: The impact of information fragmentation

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International Journal of Medical Informatics 2025年

作者： Katie S. Allen Seho Park John Price Xia Ning Titus Schleyer Center for Biomedical Informatics Regenstrief Institute Inc United States Department of Industrial and Data Engineering Hongik University South Korea Biomedical Informatics Computer Science and Engineering Translational Data Analytics Institute The Ohio State University United States School of Medicine Indiana University United States

Objective Use of electronic health data for public health surveillance and research has become almost ubiquitous. However, since many patients routinely receive care from multiple healthcare systems, not all information for a patient may be available in a single system. Leveraging data from single healthcare systems may unintentionally result in inaccuracies. This study compared the variation in the number of inpatient and emergency department encounters, diagnoses, and laboratory results, as well as COVID prevalence when data about patients was (1) drawn from a single, major health system; (2) augmented with health information exchange (HIE) data; or (3) retrieved entirely from the HIE. Materials and Methods This study leveraged a registry of 1,343,703 COVID positive patients covering the state of Indiana containing data from March 6, 2020 − February 14, 2022. The data included data from the state-wide health information exchange as well as granular data from an individual health system. data elements included encounters, diagnoses, laboratory testing, and death records. Results Enhancing the cohort with data from the HIE increased the average number of encounters per person from 22.03 to 37.05, unique diagnoses from 16.06 to 30.83, and laboratory results from 0.32 to 7.06. The estimated prevalence of COVID calculated using HIE data was 22.5% higher than using data from the single system. The number of all-cause hospitalizations increased by 14% in the HIE cohort, but emergency department visits were lower in the HIE cohort by 9.4%. Conclusion Information fragmentation can lead to an incomplete picture, or even errors, in research or clinical settings. This study demonstrates that combining clinical data sources creates variation in results. Understanding the issues presented by information fragmentation, such as bias it may create, is critical to enabling more accurate disease surveillance and crisis response planning.

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Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential

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arXiv 2025年

作者： Gao, Chenyu Xu, Kaiwen Kim, Michael E. Zuo, Lianrui Li, Zhiyuan Archer, Derek B. Hohman, Timothy J. Moore, Ann Zenobia Ferrucci, Luigi Beason-Held, Lori L. Resnick, Susan M. Davatzikos, Christos Prince, Jerry L. Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Translational Gerontology Branch National Institute on Aging National Institutes of Health BaltimoreMD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States AI2D Center for AI and Data Science University of Pennsylvania Philadelphia United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancements in deep generative models, the extent to which defacing can protect privacy is uncertain. Additionally, while the altered voxels are known to contain valuable anatomical information, their potential to support research beyond the anatomical regions directly affected by defacing remains uncertain. To evaluate these considerations, we develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded diffusion probabilistic models (DPMs). The DPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset. To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs. The results show that DPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face (p -4). For shin muscle, the correlation is statistically significant (p 0.05) when any defacing method is applied, suggesting that defacing might not only fail to protect privacy but also eliminate valuable information. We advocate two solutions for data sharing that comply with privacy: 1) share skull-stripped images along with measurements of facial and cranial features extracted before skull-stripping for public access, while acknowledging that this approach inherently compromises many research potentials;or 2) share the unaltered images with privacy enforced through policy restrictions. © 2025, CC BY-NC-ND.

关键词： Magnetic resonance imaging