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Optimising Resource Sharing Algorithms for Efficient Cloud Computing 11

Optimising Resource Sharing Algorithms for Efficient Cloud C...

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11th IEEE international conference on Big data Security on Cloud, BigdataSecurity 2025

作者： Tyagi, Jayant Sharma, Mayank Castro Valley United States

ISBN: (纸本)9798331595104

Cloud computing dramatically enhances digital resource delivery as workload patterns keep changing and hinder effective resource sharing. this research develops an efficient resource management solution using machine learning algorithms, including a random forest classifier, a gradient boosting classifier, and a support vector classifier. this study analyses CPU usage, memory usage, and network traffic in the Cloud Computing Performance Metrics dataset to create precise demand forecasts. Our model results improved after applying data preparation steps to normalize and engineer the features. the new system dynamically allocates resources based on predictive models, helping the platform reduce cost and improve delivery speed. Our test results show that asset management performs better than fixed resource distribution and load-spreading systems, especially in demanding cloud environments. © 2025 IEEE.

关键词： Cloud Computing Cloud Infrastructure Load Balancing Machine learning Optimization Performance Metrics Resource Sharing Algorithms

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I. A Frame Work of a Smart therapeutic Insole System By Measuring Plantar Pressure For Early Diagnosis Of Diabetic Foot Ulcers Using Machine learning And thermal Imaging Techniques 11

I. A Frame Work of a Smart Therapeutic Insole System By Meas...

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11th international conference on Bio Signals, Images, and Instrumentation, ICBSII 2025

作者： Diviya, S. Sangamitra, N. Venuhasini, S.M. Kapali, B. Suresh Chander Saravan, A. SRM Easwari Engineering College (Anna University) Department of Biomedical Engineering Chennai India Sri Ramachandra Faculty of Engineering and Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331513863

Diabetic Foot Ulcers (DFUs) are a significant complication of diabetes, often resulting in severe morbidity, infections, and amputations. Early diagnosis of DFUs is critical in preventing these complications. this paper focuses on the frame work of a smart therapeutic insole system that integrates machine learning and thermal imaging techniques to measure plantar pressure and other foot parameters in diabetic patients. the system leverages pressure sensors, temperature sensors, and gait analysis to provide real-time data on plantar pressure distribution and potential risk areas. In this connection, many researchers have suggested a few techniques for early detection of DFUs. their proposed works have been reviewed outlining the technological advancements in smart insole systems, their effectiveness in monitoring plantar pressure, and the application of machine learning for predictive analytics. the combination of machine learning models and thermal imaging has shown great potential in enhancing the accuracy of early DFU detection, improving patient outcomes, and reducing healthcare costs. this paper proposed a frame work and also discusses the different sensor types, methodologies, and design considerations for early diagnosis of diabetic foot ulcers using machine learning and thermal imaging techniques. © 2025 IEEE.

关键词： Diabetic Foot Ulcers Infrared thermal imaging international Diabetics Federation Internet of Medical things Laser Induced Graphene Micro-Electromechanical Systems Multi-Walled Carbon nanotubes Polydimethylsiloxane

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Efficient Modeling and Calibration of Multi-Electrode Stimuli for Epiretinal Implants 11

Efficient Modeling and Calibration of Multi-Electrode Stimul...

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11th international IEEE EMBS conference on Neural engineering (IEEE/EMBS NER)

作者： Vasireddy, Praful K. Gogliettino, Alex R. Brown, Jeff B. Vilkhu, Ramandeep S. Madugula, Sasi S. Phillips, A. J. Mitra, Subhasish Hottowy, Pawel Sher, Alexander Litke, Alan Shah, Nishal P. Chichilnisky, E. J. Stanford Univ Stanford CA 94305 USA AGH Univ Sci & Technol Krakow Poland Univ Calif Santa Cruz Santa Cruz CA 95064 USA

ISBN: (纸本)9781665462921

Epiretinal implants are designed to restore visual function by direct stimulation of retinal ganglion cells (RGCs) with a multi-electrode array. However, the efficacy of presentday epiretinal implants is limited by indiscriminate, simultaneous activation of many RGCs of different types that normally convey distinct visual information, resulting in highly unnatural visual signals. Even with high-density multi-electrode arrays in a laboratory setting, single-electrode stimulation often cannot target an individual cell selectively. A possible solution is to use spatially patterned multi-electrode stimulation. Previous studies have demonstrated enhanced selectivity using such stimuli, but a general framework for characterizing the responses of RGCs to stimulation by multiple electrodes has yet to be realized. Here we propose a generalizable model for the response of a neuron to multi-electrode stimuli, and demonstrate that it can be used to systematically and efficiently improve the selectivity of epiretinal stimulation. the efficacy of the model and of multi-electrode stimuli is validated using three-electrode triplet stimulation. the model assumes that there are several possible activation sites on each cell where a spike can originate, and that the probability of activation at each site is determined by a weighted sum of the currents applied on the electrodes. the multi-site model provides excellent fits to experimental data as well as an interpretable mechanism of multi-electrode activation. Additionally, since the model has few parameters, it permits efficient closed-loop calibration. An optimal experimental design is presented and validated for quickly learning the model parameters and identifying multi-electrode stimuli that are more selective than can be achieved with single-electrode stimuli. the model and closed-loop algorithm could potentially be used to optimize the selectivity of other neural interfaces.

关键词： neural prosthetics closed-loop active learning

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intelligent Integration of Diversified Retirement Information Based on Feature Weighting 5th

Intelligent Integration of Diversified Retirement Informatio...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Wang, Ye Zhang, Yuliang Wuxi Vocational Institute of Commerce Wuxi214153 China

ISBN: (纸本)9783031505737

When carrying out intelligent integration of retirement information, the existing data processing and analysis architecture can no longer meet the current requirements for the storage and processing of massive text data, which reduces the efficiency and accuracy of intelligent integration of diversified retirement information. therefore, a feature-weighted intelligent integration method of multi-element retirement information is proposed. Use the packet capture mechanism to collect diversified retirement information samples, and complete the pre-processing of initial retirement information through information filtering, normalization and other steps. intelligent calculation and distribution of diversified retirement information weights, extraction of mutual information, information gain and other characteristics of diversified retirement information, use of feature weighting algorithm to determine the type of retirement information, and complete the intelligent integration of diversified retirement information. through the performance test experiment, it is concluded that compared with the traditional integration method, the integrity coefficient of the retirement information integration result obtained by the optimization design method has increased by 2.4%, and the information redundancy coefficient has been effectively controlled. It is applied to the retrieval of retirement information, effectively improving the retrieval speed of information. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Information filtering

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Robust automated Event Detection from Machine-learning Analysis of Network data 28th

Robust Automated Event Detection from Machine-Learning Analy...

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28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Rodda, Matthew Chambers, Alexander Rohl, Alexander Defence Science and Technology Group Department of Defence Australia

Visibility of network communications is critical for security analysts to understand, monitor, and secure computer systems. this visibility is difficult to achieve in the context of modern industrial control systems (ICSs) however, as these may contain a multitude of unique sensors, actuators, and controllers;each communicating via bespoke and diverse network protocols. We are the first to show that machine-learning algorithms, trained with logs of observable (and irregular) system events, can serve as accurate black-box network-monitoring tools for unknown protocols, and are robust to mislabelling and non-uniform timing lag. Using a newly-curated ICS analogue dataset, available here, we analyze a suite of classical and deep-learning models for an event-detection task, showing that decision trees can achieve F1macro = 99.9% with perfectly labeled training data, gradient-boosted decision-tree ensembles can achieve F1macro = 99.0% with mislabeled training data, and a 1D-CNN-LSTM architecture can achieve F1macro = 92.1% with non-uniform timing lag. We validate these techniques on existing ICS and operational technology datasets, achieving F1macro = 99.0%. Our results are a starting point for future work to enhance existing intrusion-detection systems and forensic analyses. © 2024 the Authors.

关键词： Deep Packet Inspection Event Detection Network data Classification

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A Robust EfficientNet Architecture for Brain Tumor Classification and Identification Using MRI Image 11

A Robust EfficientNet Architecture for Brain Tumor Classific...

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11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Pathak, Manisha Chaudhary, Deevesh Sharma, Kratika Sharma, Akhilesh K Gupta, Ashish Sharma, Brij Kishore Mits Dept. of Cse Gwalior India Manipal University Jaipur Dept. of Data Science & Engineering Jaipur India

ISBN: (纸本)9798350318159

the most common method for classifying brain tumors is by using biopsy, but this procedure is only carried out after surgery in which a tiny piece of brain tissue is takeout as well as analyzed under an electronic microscope to see a tumor. Since technology has advanced, particularly in ML and AI, we are now able to identify and categorize tumors without undergoing surgery by feeding the machine's classification engine the current data from various MRI images. By doing our study, we present a approach for the identification of tumors utilizing EfficientNet, an architecture that has already been trained utilizing the transfer learning methodology. this study emphasizes on the EfficientNet-B2, EfficientNet-B3, as well as EfficientNet-B4 algorithms from the group of EfficientNet approach. the suggested approach makes use of thresholds to enhance the dataset for more effective accuracy as well as data augmentation to improve the variety of brain images in the data in addition to the previously-trained approach to enhance the effectiveness of training an improved the model. Because it utilizes the compound coefficient to scale all three dimensions of an image with a fixed value of ratio, depth, width, and resolution - EfficientNet relatives of models outperform earlier CNN architecture, according to preliminary results. the outcomes also showed that scaling the previous approaches, enabled the model to capture complex features, improving the accuracy of the model. © 2023 IEEE.

关键词： Tumors

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Identification and Detection of Leaf Miner, Pests Infestation in Cucurbitaceae Family in Real Time Infield Scenarios using YOLOv5s Object Detection Model 18

Identification and Detection of Leaf Miner, Pests Infestatio...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Keerthi Prasad, M.A. Shobha Rani, N. Sangamesha, M.A. Vinay, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India The National Institute of Engineering Department of Chemistry Mysuru India The National Institute of Engineering Department of Computer Science Mysuru India

ISBN: (纸本)9789380544519

this study contributes to technological developments in precision agriculture by developing a YOLOv5sbased model with a high F1 score and mAP, highlighting its potential for real-world use in wax gourd plants. In precision agriculture, early disease identification is crucial for effective harvesting. the present research focuses on the creation and evaluation of a YOLOv5s-based model for detecting pests and leaf miner infections in wax gourd plants in real time infield conditions at various stages of plant growth. Using a dataset of over 2000 samples collected in a variety of in-field settings, the model was trained for 200 epochs with a low learning rate (0.00001) and a batch size of 32. the achieved F1 score of 0.96 demonstrates the model's remarkable combination of precision and recall, ensuring precise disease detection. Furthermore, a mean Average Precision (mAP) greater than 0.45 demonstrates the model's proficiency in precise leaf localization and overall detection ability. the model's ability to detect numerous occurrences of disease in different leaves simultaneously is a crucial strength, reflecting the complexities common in real-world agricultural scenarios. the dataset's quantity and diversity help the model be robust and generalizable across diverse infection types and growth stages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： crop health monitoring data-driven agriculture object detection precision agriculture Smart agriculture sustainable farming

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Design of UAV-Based Information Acquisition and Environmental Monitoring System for Dangerous Goods Warehouse 11th

Design of UAV-Based Information Acquisition and Environmenta...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Yao, Jiachen Gao, Xinyu Liu, Huaqiong Department of Transportation and Logistics Engineering Shandong Jiaotong University Jinan250300 China

ISBN: (纸本)9789819796434

Due to the special nature of the dangerous goods warehouse itself, for safety reasons, the monitoring requirements for the dangerous goods storage environment are more stringent than those for ordinary warehouses. However, the internal environment of the dangerous goods warehouse is highly hazardous to human beings, prone to personal injury and death accidents, and not suitable for personnel on-site task operations. this paper intends to combine the characteristics of UAV space flight flexibility, easy to carry ancillary equipment, as well as the wireless transmission of data from wireless sensor networks, low power consumption and other characteristics, the application of UAV technology, Internet of things technology, research and design a set of UAV cargo information collection and environmental monitoring system solutions suitable for dangerous goods warehouses, to carry out real-time recording and monitoring of a variety of environmental parameters for the dangerous goods in the storage of the warehouse, and realize the intelligent collection of dangerous goods information and real-time monitoring and management of environmental parameters in dangerous goods warehouses, thereby improving the safety of dangerous goods storage, and at the same time improving the efficiency of storage inventory and inspection and the intelligent level of environmental monitoring. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Zigbee

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learning CONTROLLABLE ADAPTIVE SIMULATION FOR MULTI-RESOLUTION PHYSICS 11

LEARNING CONTROLLABLE ADAPTIVE SIMULATION FOR MULTI-RESOLUTI...

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11th international conference on learning Representations, ICLR 2023

作者： Wu, Tailin Maruyama, Takashi Zhao, Qingqing Wetzstein, Gordon Leskovec, Jure Stanford University United States NEC Corporation United States

Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learning-based surrogate models use a uniform spatial scale, which needs to resolve to the finest required scale and can waste a huge compute to achieve required accuracy. In this work, we introduce learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We introduce learning techniques that optimizes LAMP with weighted sum of error and computational cost as objective, allowing LAMP to adapt to varying relative importance of error vs. computation tradeoff at inference time. We evaluate our method in a 1D benchmark of nonlinear PDEs and a challenging 2D mesh-based simulation. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error: it achieves an average of 33.7% error reduction for 1D nonlinear PDEs, and outperforms MeshGraphNets + classical Adaptive Mesh Refinement (AMR) in 2D mesh-based simulations. Project website with data and code can be found at: http://***/lamp. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Errors

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A Novel Transfer learning Approach for Detecting Partial Shading in Photovoltaic Systems 11

A Novel Transfer Learning Approach for Detecting Partial Sha...

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11th IEEE/ACM international conference on Big data Computing, Applications and Technologies, BDCAT 2024

作者： Teta, Ali Medkour, Maissa Chennana, Ahmed Chouchane, Ammar Himeur, Yassine Atalla, Shadi Mansoor, Wathiq Belabbaci, El Ouanas University of Djelfa Applied Automation and Industrial Diagnostics Laboratory Department of Electrical Engineering Djelfa Algeria LI3C Laboratory University of Biskra Biskra Algeria University Center of Barika Batna05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates Faculty of Technology Bejaia06000 Algeria

ISBN: (纸本)9798350367300

In Photovoltaic Systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. this paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. the proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. this approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems. © 2024 IEEE.

关键词： Solar power generation

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