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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： A. Sonya Neha Chhabra Abhishek Kumar Gupta P. Lakshmi Harika B. Aishwarya R. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Smart homes oriented towards reminders and monitoring for healthcare must be reasoned, definitely centered on matured home or disability in a house. Accurately predicting the movements of people would be essential to offer such level support for residents in this situation. Our study helps to cater this requirement by proposing an improved forecasting model which is both accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can be constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. So, finally this Recurrent Extreme Learning Machine model validates that for smart home technology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should be enough during the learning phase though; but it also allows machine to learn more about human behavior making them generally intelligent because now you have a pattern how humans behave. Advantages Of Sexual Health These are certainly rewarding Smart Home Systems..

关键词： Training Adaptation models Accuracy Extreme learning machines Smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

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Efficient Disease Detection in Wheat Crops: A Hybrid Deep Learning Solution 3

Efficient Disease Detection in Wheat Crops: A Hybrid Deep Le...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Konidena, Anuradha Shanbhog, Manjula Singh, Swati Sharma, Vikrant Jain, Anuj Kumar Sharma, Neha Iilm University Department of Cse Uttar Pradesh Greater Noida India Christ University Department of Computer Science Uttar Pradesh Ghaziabad India School of Computing MIT-ADTU Department of Information Technology Pune India Graphic Era Hill University Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350342338

Wheat rust disease poses a significant danger to global food security and requires rapid, precise diagnosis to be effectively managed. Using a hybrid deep learning (DL) model consisting of a convolutional neural network (CNN) and a decision tree (DT), a new method for classifying wheat rust illness across six magnitude scales has been described in the proposed study. For training and assessing the model, a dataset of 50,000 wheat leaf photos representing a wide range of disease magnitude has been amazing. The suggested work developed a hybrid CNN-DT model with an amazing overall accuracy of 93.47% by carefully analyzing the data and crafting the model. The model's resilience in identifying multiple levels of disease magnitude was proved by the performance metrics for each disease magnitude class. The proposed hybrid model also outperformed state-of-the-art models in terms of accuracy, as shown by the comparisons conducted. The findings provide important new information on the potential of DL methods for wheat rust disease classification, which can then be used as a trusted resource for early disease diagnosis and smarter agricultural policymaking. In the face of agricultural diseases, the suggested model has important implications for improving crop management, reducing yield losses, and guaranteeing food security. © 2023 IEEE.

关键词： Decision Tree Deep Learning Disease Magnitude Multi-classification Wheat Diseases Yellow Rust

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A Joint Scheme on Spectrum Sensing and Access with Partial Observation: A Multi-Agent Deep Reinforcement Learning Approach

A Joint Scheme on Spectrum Sensing and Access with Partial O...

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IEEE International Conference on Communications in China (ICCC)

作者： Yulong Zhang Xuanheng Li Haichuan Ding Yuguang Fang School of Information and Communication Engineering Dalian University of Technology Dalian China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science City University of Hong Kong Hong Kong China

Dynamic spectrum access (DSA) has been regarded as a promising solution to mitigate the serious spectrum shortage problem in the 6G networks, in which secondary users (SUs) are allowed to opportunistically access the licensed bands when primary users (PUs) are inactive. Due to the hardware limitation, partial spectrum sensing with a suitable sensing window (SW) is considered as an effective way to find the idle bands to access. It is noteworthy that the SW selection could determine how many bands are available to access, and the network performance after the access could be used to guide the SW selection. Thus, a sophisticated joint design on both spectrum sensing and access is necessary, which, however, is not an easy task considering the uncertainty and dynamics of the spectrum environment, and also the mutual impacts among SUs. In this paper, we propose a joint partial spectrum sensing and power allocation (PA) scheme to help each SU make the best SW and PA decisions that can optimize the network throughput. To achieve the best decision under the dynamic and uncertain of the environment, considering the mutual interference issue, we develop a multi-agent deep reinforcement learning approach to enable each SU to obtain the best SW and PA decisions autonomously and adaptively.

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RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method

arXiv

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arXiv 2023年

作者： Peng, Wangzhen Zhou, Chenghao Hu, Runze Cao, Jingchao Liu, Yutao Department of Computer Science and Technology Ocean University of China Qingdao China School of Management Engineering Qingdao University of Technology China School of Information and Electronics Beijing Institute of Technology Beijing100080 China

Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and color distortion. In recent years, the application of deep learning has quietly revolutionized various areas of scientific research, including UIE. However, existing deep learning-based UIE methods generally suffer from issues of weak robustness and limited adaptability. In this paper, inspired by residual and attention mechanisms, we propose a more reliable and reasonable UIE network called RAUNE-Net by employing residual learning of high-level features at the network’s bottle-neck and two aspects of attention manipulations in the down-sampling procedure. Furthermore, we collect and create two datasets specifically designed for evaluating UIE methods, which contains different types of underwater distortions and degradations. The experimental validation demonstrates that our method obtains promising objective performance and consistent visual results across various real-world underwater images compared to other eight UIE methods. Our example code and datasets are publicly available at https://***/fansuregrin/RAUNE-Net. © 2023, CC BY.

关键词： Deep neural networks

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Testing External Polymer Insulation Materials for Resistance to Electrical Tracing and Degradation Using the Inclined Plane Method 6

Testing External Polymer Insulation Materials for Resistance...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Gupta, Ankur Kumar Padalia, Diwakar Gangwar, Arun Sharma, Sandeep Kamble, Sachin K Bhoite, Sonali Prashant School of Computer Science and Applications IIMT University Meerut India School of Basic& Applied Sciences Department of Physics K. R. Mangalam University Gurugram India Invertis University Department of Electrical Engineering Bareilly India School of Electronic and Electrical Engineering Dev Bhoomi Uttarakhand University Uttarakhand India Terna Engineering College Design Dept Navi Mumbai India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

An important step in creating outdoor insulators is to assess the polymeric housing materials'resistance to weathering and electrical tracking. The terms 'erosion'and 'tracking'refer to the weight loss of the housing material and the production of a surface carbonaceous trail, respectively. With the help of the common tracking and erosion tests, composites can only be ranked relative to one another in terms of their tracking and erosion resistance, which makes it impossible to quantify these properties in absolute terms. Failure due to the track was a major issue during the early stages of the usage of organic insulating materials. The introduction of tracking-resistant composites with significant inorganic filler content has increased the importance of erosion. © 2023 IEEE.

关键词： DC predisposed exam are some of the terms that may be used erosion Inorganic fillers monitoring parched curving polymer insulator predisposed testing silicone rubber

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Blockchain technology Based Privacy Protection Scheme for Large-Scale Distributed Systems

Blockchain Technology Based Privacy Protection Scheme for La...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： H. Azath G. Sathish Kumar M. Menaka S. John Justin Thangaraj School of Computer Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India Department of Information Technology Gojan School of Business and Technology Chennai Tamil Nadu India Department of Information Technology Kingston Engineering College Vellore Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

The cloud and mobile cloud deployment in large scale distributed systems offer excellent service that enhances both individual and organizational effectiveness. These cloud and blockchain based systems can provide resources at a very cheap cost since they are built on independent nodes except for any special centralized monitoring and controlling. As a result, the growth of mobile services and apps is unpredictable. Data-driven apps stand out among them in terms of popularities or revenue generation. However, the data-intensive applications have additional issues such as storage, big data filtering, and the critical tasks of securing confidential or sensitive data. In this study, a blockchain model using cloudlet concept is proposed. This model is then used in mobile cloud computing. The simulation of the proposed system is carried out in a mobile cloud computing simulator. According to the experimental analysis and latency result, the blockchain approach outperforms the conventional cloud models by up to 90%,

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Gain and Phase: Decentralized Stability Conditions for Power Electronics-Dominated Power Systems

arXiv

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arXiv 2023年

作者： Huang, Linbin Wang, Dan Wang, Xiongfei Xin, Huanhai Ju, Ping Johansson, Karl H. Dörfler, Florian The Department of Information Technology and Electrical Engineering ETH Zürich Switzerland The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden The College of Electrical Engineering Zhejiang University China

This paper proposes decentralized stability conditions for multi-converter systems based on the combination of the small gain theorem and the small phase theorem. Instead of directly computing the closed-loop dynamics, e.g., eigenvalues of the state-space matrix, or using the generalized Nyquist stability criterion, the proposed stability conditions are more scalable and computationally lighter, which aim at evaluating the closed-loop system stability by comparing the individual converter dynamics with the network dynamics in a decentralized and open-loop manner. Moreover, our approach can handle heterogeneous converters' dynamics and is suitable to analyze large-scale multi-converter power systems that contain grid-following (GFL), grid-forming (GFM) converters, and synchronous generators. Compared with other decentralized stability conditions, e.g., passivity-based stability conditions, the proposed conditions are significantly less conservative and can be generally satisfied in practice across the whole frequency range. © 2023, CC BY.

关键词： Power converters

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AvTF: A visual-tactile fingertip with active sensing and manipulation

AvTF: A visual-tactile fingertip with active sensing and man...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Jie Zhao Sicheng Wang Jianhua Shan Yuhao Sun Shixin Zhang Fuchun Sun Bin Fang School of Mechanical Engineering Anhui University of Technology Ma’Anshan China School of Artificial Intelligence Beijing University of Posts and Telecommunications Tandon School of Engineering New York University School of Engineering and Technology China University of Geosciences (Beijing) Beijing China Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

In the interaction between dexterous hands and the environment, perception and operation are inseparable. Perception provides information and feedback for operations, and operations in turn affect the perception. Current VTS(visual-tactile sensors) lack initiative and cannot sense the state of the operated object in the world coordinate system. The AvTF(Active visual-tactile Fingertip) introduced in this article is a vision-based fingertip (finger-shaped full-field sensing) tactile sensor that uses a camera to capture tactile images. Sensing and operating are integrated by adding active DoF (Degree of Freedom). Camera modules under different coordinate systems can provide various perspectives for sensors. Through a series of experimental verifications, AvTF has achieved real-time feedback on the grasping and manipulation of objects in the hand, providing a new solution for dexterous hands to fuse sensing and operation better.

关键词： Manufacturing processes Tactile sensors Reinforcement learning Production Cameras Sensor systems Real-time systems

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Migration of Knowledge Base Articles between Salesforce Cloud Instances

Migration of Knowledge Base Articles between Salesforce Clou...

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Emerging technology (INCET), International Conference for

作者： Saideep Sunkari Priya Parameswarappa Govinda Rajulu Lanke Rahul Bejgam Venkat Tulasi Krishna Gannavaram Kakatiya Institute of Technology and Science Warangal India School of Information Technology University of the Cumberlands Williamsburg KY USA Birla Institute of Technology & Science Pilani Rajasthan India School of Electrical Computer and Energy Engineering Arizona State University Tempe USA

The data migration across platforms has tremendously increased from past decades due to the business acquisitions and emerging start-ups. During these acquisitions all the assets, clients, customers, policies have to be migrated or transferred to the acquired company. One of such challenging migration is data migration from one cloud to cloud. There exist millions of records hosted in the servers, clouds' that have to be migrated without any data loss, data breaches in a valid notation and format. All the business these days are relying on the Customer Relationship Management (CRM) tools in improving the customer services and building stronger customer interactions. One of such CRM is Salesforce. The information present in the source salesforce org which includes metadata, flows, validations, workflows, triggers will have to be migrated to the destination salesforce org. Unlike other objects, the knowledge base articles can’t be migrated using the common migration process. Knowledge articles needs a unique way of exporting, formulating the data, importing the data of same or cross knowledge experience between salesforce instances. The study, analysis and migration of salesforce knowledge base is interpreted in this paper.

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