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A Dual-View Fusion Network for Automatic Spinal Keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Dandan Guo, Lijun Zhang, Rong He, Xiuchao Wang, Qiang Wang, Jianhua Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

关键词： biplane X-ray images dual-view feature fusion spinal keypoint detection

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A Machine Learning Approach for offload Decision Making in Mobile Cloud Computing 7

A Machine Learning Approach for offload Decision Making in M...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Mathur, Robin Prakash Sharma, Manmohan Lovely Professional University School of Computer Science Engineering Punjab India Lovely Professional University School of Computer Application Punjab India

ISBN: (纸本)9781665405201

Mobile cloud computing encompasses a computational offloading framework that helps deploy the compute-intensive task to the remote server to save energy and increase the mobile device's performance. A decision engine is a significant component in the offloading framework, which helps decide when to offload the task to the remote or cloud server. The decision engine's accuracy should be high for the flawless execution of the application during the offloading process. A technique has been proposed by performing a stack ensemble approach on machine learning techniques like the Gaussian approach, multi-layer perceptron, k-nearest neighbors, and linear regression. It considers the various dynamics of the environment like task size, bandwidth, device battery, and device mobility. The proposed model performs better than other decision-making algorithms in terms of execution time and CPU utilization and achieves higher accuracy in making decisions while offloading the compute-intensive task to the remote server. © 2021 IEEE.

关键词： Machine learning

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Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnosis

Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnos...

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International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC)

作者： Parthasarathi Pattnayak Debaswapna Mishra Sanghamitra Patnaik Arpeeta Mohanty School of Computer Applications KIIT Deemed to be University Bhubaneswar India Department of Computer Science and Application OUAT Bhubaneswar India School of Law KIIT Deemed to be University Bhubaneswar India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

High blood glucose levels are a defining characteristic of diabetes, with type 2 diabetes being the most prevalent form. Early detection and treatment can prevent or delay complications. To precisely anticipate type 2 diabetes risk, we applied machine learning techniques to identify unidentified correlations with a person's health condition. In order to find potential nonlinear correlations between the beginning of type 2 diabetes with an ensemble of health factors, this research used a binary classifier that was trained from scratch. Three datasets—MIMIC-III, MIMIC-IV, and NHANES—were used. The equal number of people who had or did not have type 2 diabetes have been included in one data set that was created by combining the two sets of data. Reliability was the key metric, given the balanced dataset. The model achieved ROC AUC score of 0.934 with accuracy ratings up to 86%, which were positive results of this investigation. To increase the model's dependability, more research is required to take into account multiple readings taken throughout period for an identical person.

关键词： Measurement Correlation Accuracy MIMICs Machine learning Diabetes Glucose Delays Reliability Blood

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks

A Bayesian Optimization Framework for Adaptive Node Deployme...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Anish Kumar Vishwakarma Nikhil Dhengre Rakesh Ranjan Amit Verma Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Dehradun Uttarakhand India Department of ECE IIIT Nagpur Maharashtra India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. This paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. Through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. The results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. These findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations.

关键词： Learning systems Wireless sensor networks Energy consumption Adaptive systems Machine learning Energy efficiency Real-time systems Bayes methods Reliability Optimization

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Efficiency Beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency Beyond Limits: LSTM and k-NN Synergy for Forecast...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Amit K. Mogal Vaibhav P. Sonaje CH N Santhosh Kumar V. Chithra Devi Prasanthi Gottumukkala Satheesh Kumar D Department of Computer Science & Application School of Computer Science and Engineering Sandip University Nashik Maharashtra India Department of Computer Science & Applications School of Computer Science and Engineering Sandip University Nashik Maharashtra India Department of CSE Anurag Engineering College Kodada Telangana India PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R 2 ) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings.

关键词： Data centers Machine learning Predictive models Nearest neighbor methods Data models Resource management Energy management

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Groundwater Evaluation with Classification Techniques

Groundwater Evaluation with Classification Techniques

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Yakesh Dogra Mizan Ali Khan Department of Master of Computer Application Chandigarh School of Business Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

We has researched that your earth area contains % comprised of water and the remaining 28 part also with soil. From the 28 per cent, % water is seawater namely it is excessively salted for consuming, for agricultural production and more critical items. That's because the freshwater which seems utilised is extremely small in bulk and its condition is every day growing worse. The condition of the water must be kept since it is a crucial life factor not just the individuals as well as other creatures and flora. Humans demand a suitable level sustained liquid to consume and employ in varied daily tasks Assessments of groundwater recharge and assessment are thus essential for the development of economy and society. Photogrammetry is now a crucial technique for ground component assessment as a result of advances. So to assure if the groundwater we get is excellent or wouldn't we have gathered a set and used multiple Ml techniques to assess the auc. So, the research intends to determine the prognosis of water whether it is of good standard or not by utilizing two algorithms, rbfnn and perceptron and to demonstrate which outperforms.

关键词： Liquids Sociology Soil Classification algorithms Rivers Reliability Task analysis

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Archer: Adaptive Memory Compression with Page-Association-Rule Awareness for High-Speed Response of Mobile Devices 23

Archer: Adaptive Memory Compression with Page-Association-Ru...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Li, Changlong Zhu, Zongwei Wang, Chao Liu, Fangming Xu, Fei Sha, Edwin H.-M. Zhou, Xuehai School of Computer Science and Technology East China Normal University China Jianghuai Advance Technology Center Hefei230026 China MoE Engineering Research Center of Hardware/Software Co-Design Technology and Application China School of Software Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Huazhong University of Science and Technology China Peng Cheng Laboratory China

ISBN: (纸本)9781939133458

In mobile systems, memory can be compressed page-by-page to save space. This approach is widely adopted because memory data is accessed by page. However, this paper shows that the system response speed is significantly limited by page-grained compression. In this paper, we observe that approximately a quarter of anonymous memory pages are highly correlated, even though the association is implicit. Inspired by this, we propose Archer, an association-rule-aware memory compression framework in mobile systems. Archer demonstrates that memory in mobile devices should be compressed using flexible granularity, rather than relying solely on traditional page compression. To further integrate association-rule mining techniques into system design, we redesign the LRU mechanism and propose an adaptive memory compression region. Experimental results show that the average app launching speed is 1.55x faster when enabling Archer, and the average photographic speed and frame rate increase by 1.42x and 1.31x, respectively, compared to the state-of-the-art. © 2025 FAST. All Rights Reserved.

关键词： Portable equipment

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An Analysis of Edge Computing with Multi Access in 5-G Technology

An Analysis of Edge Computing with Multi Access in 5-G Techn...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sitaram Gupta Sonia Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

The demands of the rapidly expanding internet. However, since most end users have confined storage space and limiting computing power, it lately evolved into a natural issue how to execute computationally complex apps on resource-bound consumers. The emerging fifth generation (5G) corporation can benefit from Mobile Edge Computing (MEC) innovation upgrade portable assets by facilitating figure concentrated applications, handling a lot of information prior to sending them to the cloud, finding distributed computing assets inside the radio access organization (RAN) close to versatile clients, and giving setting mindful administrations utilizing RAN information. In reality, the paradigm shift from 4G to 5G is accompanied by the introduction of new technical concepts. We first give a comprehensive introduction of MEC technology and some of its possible applications. The most recent research on MEC integration with upcoming technologies that will be employed in 5G and beyond is then is summarized.

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A Deep Learning Framework for Classifying Autism Spectrum Disorder from fMRI Images

A Deep Learning Framework for Classifying Autism Spectrum Di...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Rajat Kishor Varshney Alok Katiyar Prashant Johri School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The objective of this work is to propose a new machine learning based approach for ASD classification from fMRI data employing deep learning architecture and novel preprocessing method. Main datasets included in ABIDE I, ABIDE II, Human Connectome Project (HCP), and Social Functions and Autism Research Interface (SFARI). Many supplements include graph neural networks for brain segmentation, 3D-BRA for accurate mapping of the brain than just using conventional registration technique, EN-CC for feature selection. The approach incorporated graph neural network for segmentation of the brain, 3DBRA mapping that translates and rotates the brain precisely unlike the general technique of registration, EN-CC for selecting the features. The deep learning models under consideration were ResNet-50, VGG-16, Inception-v3, and Efficient Net which were improved through ABSC and MSTC. Classification accuracy of the proposed models was higher than 95% on all the datasets with Efficient Net scoring 99% on the SFARI dataset. This means that only the Stratified Group Cross-Validation (SGCV) was employed to make sure the results would be more reliable. The results present a considerable improvement in both accuracy and the interpretability of the derived connectivity profiles of the ASD-related brain.

关键词： Deep learning Autism Accuracy Three-dimensional displays Functional magnetic resonance imaging Brain modeling Feature extraction Graph neural networks Reliability Testing

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Resources in IoT Through Bctechnology

Resources in IoT Through Bctechnology

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Manish Kumar Goyal Karishma Sharma Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

With the exponential rise in IoTapplicationit difficult to keep up with the demand for services with lower latency, higher bandwidth, easier accessibility, and advanced intelligence. To meet such demand the distributed and decentralized computing architectureisproposed. Edge-centric computing resources don't need to be managed by a distant but centralized third party can managed locally and individually. However, because to a lack of trust between IoT devices and , obstacles (ECNs). The trust-absence issue can be effectively resolved by a blockchain, which is a decentralized, trustless, and immutable public ledger. discuss the concerns associated with we outline the main features of blockchains that make them ideal for edge-centric IoT applications. In addition, we offer a broad architecture that outlines the sequential steps required to complete for scenarios based on blockchain and edge computing that enable IoT. Additionally, to demonstrate how artificial intelligence (AI) and blockchains may function together, we create a We also go through the advantages of blockchains and AI coming together. The simulation's findings are then displayed.

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