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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

共 5105 条记录，以下是361-370 订阅

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Design and Implementation of a Personalized Recommendation System Based on Deep Learning distributed Collaborative Filtering Algorithm on Social Media Platforms 3

Design and Implementation of a Personalized Recommendation S...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Chen, Junyu Alexa Music Amazon SeattleWA United States

ISBN: (纸本)9798331508456

Social media platforms mainly provide personalized recommendation services for users, and usually adopt recommendation methods related to user or project similarity based on the characteristics of different platforms. However, current recommendation techniques mostly rely on simple similarity calculations, which can significantly reduce accuracy when dealing with large amounts of data. To address this issue, this paper proposes a collaborative filtering recommendation method based on social media gene maps. Specifically, this article draws on the concept of genes in biology, collects social media user behavior data, extracts biological genes, social genes, and network gene fragments, calculates the core gene matching degree of each gene fragment, and uses entropy weight method to assign different weights to gene fragments, thereby constructing a complete social media gene map and optimizing the recommendation mechanism. The experimental results show that this method outperforms traditional recommendation algorithms in terms of recommendation performance. Subsequently, this article proposes to combine social media gene maps with project similarity to further improve recommendation algorithms. When measuring project similarity, a fusion of scoring similarity and semantic similarity was used, combined with user gene matching degree, to optimize the weighted Slope One algorithm and achieve more accurate recommendation services. The results indicate that the algorithm proposed in this paper has significantly improved in evaluation metrics such as MAE and MRSE. © 2025 ieee.

关键词： Tweets

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Predictive Analytics in Mental Health: Machine Learning Models for Major Depressive Disorder Detection using sensor Data 3

Predictive Analytics in Mental Health: Machine Learning Mode...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Padmakala, S. Gopukumar, S.T. Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602 105 India Saveetha Medical College and Hospital Tamil Nadu Chennai602 105 India

ISBN: (纸本)9798331509675

The recent integration of Machine Learning (ML) and sensor technologies in healthcare, particularly for diagnosing Major Depressive Disorder (MDD), has paved the way for advanced predictive analytics in mental health. This study aimed to develop ML models to detect MDD using sensor-derived data, including accelerometer measurements, temperature, pulse rates, and SpO2 levels. The proposed methodology involved collecting and preprocessing a comprehensive dataset, imputing missing values, and splitting the data into training and test sets. Feature scaling and PCA were applied to enhance model efficiency. The performance of various ML models such as Random Forest, SVM, Gradient Boosting, XGBoost, LightGBM, and Logistic Regression was evaluated based on accuracy, precision, recall, and F1-score. Random Forest and XGBoost models performed exceptionally well, with accuracy rates nearing 97%. Ensemble methods, in particular, demonstrated superior predictive power. Logistic Regression showed significantly lower performance, highlighting the complexity of MDD beyond linear classification methods. Future directions include developing real-time analysis models, personalizing predictive algorithms, integrating ML into clinical practice, and conducting longitudinal studies to validate and refine these tools. Ethical and privacy considerations, as well as expanding predictive factors, are critical for advancing this research field. The study concluded that ML models are potent tools for early detection of MDD, which can significantly impact patient outcomes. The potential for these models to be integrated into clinical settings for personalized patient care could revolutionize mental health diagnostics and treatment strategies. © 2025 ieee.

关键词： Patient treatment

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2023 3rd international conference on Energy Engineering and Power systems, EEPS 2023

2023 3rd International Conference on Energy Engineering and ...

引用

3rd international conference on Energy Engineering and Power systems, EEPS 2023

ISBN: (纸本)9798350313857

The proceedings contain 207 papers. The topics discussed include: operation and configuration analysis of a power storage system with a high proportion of wind power;economic optimal operation of integrated rural single household energy systems;an adaptability hybrid-frequency interleaved control strategy for double-frequency converters with parameter uncertainties;simulation analysis of conductive carbon black and hydrogen prepared by partial oxidation of benzene-doped acetylene;reliability evaluation of power generation system considering planned maintenance and energy storage;new technology and integrated optimization of distributed energy storage power generation;regional short-term power load forecasting considering user electricity consumption and seasonal characteristics;disturbance identification of power quality based on Markov transition field and deep residual network;and effect of discharge amplitude on electrical trees based on three-capacitance model.

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Modelling Virtual sensors for Indoor Environments with Machine Learning 18

Modelling Virtual Sensors for Indoor Environments with Machi...

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18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Polanski, Dawid Marek Angelopoulos, Constantinos Marios Bournemouth Univ Dept Comp & Informat Poole Dorset England

ISBN: (纸本)9781665495127

Virtual sensors model the sensing operation of physical sensors deployed in an area of interest by generating sensory data with accuracy and precision close to those collected by physical sensors. Their use in applications such as augmenting the infrastructure of IoT facilities and test beds, monitoring and calibrating the operation of physical sensors, and developing Digital Twins of physical systems have led virtual sensors to attract research attention. Machine learning provides methods for modelling patterns in complex and big data generated by IoT sensing devices, allowing to model the behaviour of these devices. In this work, we investigate ML methods as means of implementation for virtual sensors. In particular, we evaluate the performance of six ML methods in terms of their effectiveness, accuracy and precision in generating sensory data based on data from physical sensors. In our study, we use a multi-modal dataset comprising IoT sensory data for temperature, humidity and illumination collected over a period of two years in an office space at University of Geneva. Our results show that the best performing model at predicting an output of a missing sensor is the Random Forest method, achieving MAPE error below 3%, 5% and 18% respectively for temperature, humidity and illuminance. The worst performing models were the linear radial basis function neural network and linear regression. In future research, we plan to deploy the best performing models natively on IoT devices, making use of tinyML and extreme edge computing methods.

关键词： Internet of Things sensor Virtual sensor Machine Learning

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 ieee.

关键词： Artificial Intelligence Energy Efficiency Machine Learning Network Lifetime Reinforcement Learning Resource Allocation Wireless sensor Networks

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Effect of Environmental Temperature Rise on Optimal Places and Number of distributed Generators 3

Effect of Environmental Temperature Rise on Optimal Places a...

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3rd ieee international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Mahdavi, Meisam Awaafo, Augustine Jurado, Francisco Gopi, Pasala University of Jaen Department of Electrical Engineering Linares Spain Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India

ISBN: (纸本)9798350304732

Optimally siting distributed generations (DGs) is regarded as one of the most effective mechanisms for loss mitigation on a distribution network. Power loss curtailment on distribution networks is a very essential in power system operation and management since it constitutes the major share of the overall power loss on power systems. Losses on power systems lead to exorbitant network operation costs as well as the distortion of the system's voltage profile. In traditional DG placement, the optimal locations and the number of distributed generators are optimally determined for ensuring that losses are reduced to the barest minimum in order to meet the power demand of consumers. Load variation on distribution buses leads to different resultant losses in comparison with constant loads. Nevertheless, several proposed models for DG allocation problem ignored consumers' load variability. While the selected few that considered load variation in their proposed models neglected the impact of ambient temperature change on electrical energy usage which influence appropriate places and DG number. Accordingly, the current study evaluates environment's temperature change impact on DG allocation solutions to access environmental temperature change importance in loss optimization via DG placement. It is observed that ambient temperature variations significantly change the appropriate places and DG sources number. © 2023 ieee.

关键词： distributed generators distribution system environment load type temperature rise.

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Judicial System in Bangladesh Using distributed Blockchain Technology 3

Judicial System in Bangladesh Using Distributed Blockchain T...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Rakibul Hasan, M.M. Kashem, Mohammod Abul Fatin, Md. Al-Abrar Roni, Md. Mainul Islam Bangladesh University of Professionals Department of Information and Communication Technology Dhaka Bangladesh International University of Business Agriculture and Technology Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering and Technology Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350388282

The current judicial system process, which is conducted manually in Bangladesh, is characterized by a high degree of uncertainty and risk, as well as a wide range of possibilities for fraudulent documents and the preservation of the ownership details of cases. Therefore, the current system does not appear to be feasible, effective. We have created our proposed blockchain-based judicial management system with the SHA-256 data encryption technique. This system will ensure the safety of the case transactions and will be able to store, authenticate, and maintain entire pertinent information of the case document. The mentioned architecture will be able to revoke and to detect attempts to change the case by using a variety of technologies, including IPFS, Proof of Stack (PoS), and public-key cryptography. These techniques will lead to subsequent more efficient solutions than the existing systems and improve how the general public views Bangladesh's judicial system. © 2024 ieee.

关键词： Blockchain

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distributed pseudonym mechanism based on Consortium Blockchain 23

Distributed pseudonym mechanism based on Consortium Blockcha...

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Baohui Wang, Miaomiao Chen, Shiyou Rui, Lanlan Yang, Yang Gao, Zhipeng Beijing University of Posts and Telecommunications National Engineering Laboratory for Disaster Backup and Recovery Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9781665494571

Crowd sensing is a way to obtain multiple sensing data onto users or mobile devices and is widely used in industrial Internet, smart city, smart medical, etc. However, when users upload sensing data involving sensitive information such as a user identity and location, there will be risks of data privacy leakage and user tracking. For the problem of this scenario, we propose a blockchain-based pseudonym generation mechanism, which uses pseudonyms to realize user anonymity and user identity authentication. The average speed of pseudonym generation and authentication in the simulation experiment is about 0.1s, the experimental results verify the availability and effectiveness of the distributed pseudonym mechanism. © 2021 ieee.

关键词： Privacy Data privacy Smart cities distributed ledger Authentication Mobile handsets sensors

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A new single-phase grounding fault location method of distribution networks with the influence of distributed generators and loads 3

A new single-phase grounding fault location method of distri...

引用

3rd international conference on Intelligent Power and systems, ICIPS 2023

作者： Yang, Lang Fan, Xinqiao Zhang, Li Li, Hui Beijing Information Science and Technology University School of Automation Beijing China

ISBN: (纸本)9798350328691

Aiming at the single-phase grounding fault location problem of neutral point ungrounded distribution network system with distributed generators, a single-ended impedance method fault location method based on zero-sequence current is proposed. Firstly, the circuit voltage equation is written based on the line impedance parameters and the collected bus voltage, line bus current, end loads current and the distributed generator outlet current data, as well as the fault current series circuit voltage equation constructed by the zero-sequence component of the post-fault loads current, the zero-sequence component of the capacitor current to the ground and the zero-sequence component of the current at the head of the line. Secondly, the fault distance is given as a variable for the fault location equation of each line segment in turn, and finally the fault distance is obtained by solving the equation. In the process of fault location calculation, the problem of line-to-ground capacitor current compensation is considered, and the node voltage is used to calculate the capacitor current, the node voltage between the fault location and the bus end is calculated by the electrical quantity at the bus end, and the node voltage between the fault location and the end is calculated by the forward pushback generation method. An improved ieee 34-node model with distributed power source and loads is constructed by PSCAD/EMTDC and the fault location simulation is verified, and the results show that the fault location of the distribution network with distributed generators and loads can be accurately located by the method proposed in this paper. ©2023 ieee.

关键词： distributed power generation

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Implementation of QOS in SDN and distributed Networks for mitigation of DDOS based attacks using Machine Learning 3

Implementation of QOS in SDN and Distributed Networks for mi...

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3rd ieee international conference on Blockchain and distributed systems Security, ICBDS 2024

作者： Kumar, Subhash Kolhe, Kishor Mit World Peace University School of Computer Engineering and Technology Pune India

ISBN: (纸本)9798350354348

distributed Denial Service of Service (DDoS) is very sophisticated attack which brute-force packet jamming to a network to render it useless, if done with large number of nodes. It can be easily countered by a number of techniques such as load balancing, rate limiting and many newer intelligent systems techniques but attackers are continuously developing new techniques to circumvent traditional defense mechanisms. The Sophistication of the attack is determined by current defenses in place and duration of attack. The use of DDoS attack is done on distributed Network traffic by an attacker which might send more traffic than a network card can handle or overwhelm an application with more requests than it can process which might led to not able to add legitimate user to the application With coming of Web 3.0 the DDoS attack grown exponentially , So we are proposing a Machine Learning based traffic filtering system by which we can determine the legitimate user entering to our application resources with encrypted HTTPS traffic. Pirated and duplicate IP events filtering will lead to clean resources inside Web3.0 and gaming environment which will improve the efficiency, latency and performance of game and similar resources. We will use QoS parameters in SDN parameters to define the network governance and tuning mechanism so that application traffic is correctly routed and prioritized. One of the major reasons to propose this research is because right now the major rdDoS attacks shifted from L3/L4 Network Layer to L7 Application layer causing major increase in attack size ratio. © 2024 ieee.

关键词： HTTP

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