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MED-LINK: A Web-Based Solution for Innovative Medical Resource Allocation in Healthcare

MED-LINK: A Web-Based Solution for Innovative Medical Resour...

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2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Dutta, Subhojit Rajput, Yashika Nagendra Prabhu, S. Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331543617

Medical resource sharing is crucial for addressing shortages and ensuring the timely availability of essential healthcare resources, especially during emergencies. Increase in demands for efficient management and cost effective utilization of scarce medical resources has been occasioned by the recent COVID-19 pandemic with special reference to catering for emergent needs of patients. This research also presents MEDLINK a novel free web-based system that allows the 'real time' management and allocation of all necessary resources in the hospital with the support of integrated artificial intelligence. With the help of MEDLINK, resource requirements are predicted, and real-time data on their availability and needful is given;therefore, healthcare facilities can request a bed, equipment, and personnel help, if necessary. It thus avails resource management and the handling of emergent situations beyond the conventional paradigms of a traditional hospital management system that focuses on the decentralized Management Resource units of MEDLINK. It uses Advanced Encryption Standard, 256 bit for data security while it uses MongoDB for data scalability. Compared with previously developed static models, our machine learning approach yields a 30% improvement in response time and a 12% increase in matching accuracy. Stress testing and user polls prove the efficiency of MEDLINK as on comparison to other services it is faster, more accurate, scalable and secure. In addition, already implemented data security measures are presented in the design and its possible further developments are described, which include connection to national health care databases as well as more sophisticated AI-based decision making systems. The main focus of this research is to use automation and intelligent technologies to redesign the medical resource management and enhance healthcare service. © 2024 ieee.

关键词： Information management

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The Use of Power IoT technology in Power Grid Enterprises: Technological Methods and Empirical Research for Enhancing New Quality Productivity 5

The Use of Power IoT Technology in Power Grid Enterprises: T...

引用

5th international conference on Smart Grid and Energy Engineering, SGEE 2024

作者： Li, Meng Wang, Yibo Ma, Jiaxin Zuo, Jing Chen, Junshu Li, Xiaotian Guangdong Power Grid Co. Ltd. Guangzhou China Management Science Research Institute Guangdong Power Grid Corporation Guangzhou China

ISBN: (纸本)9798331530686

In the pursuit of new quality productivity within power grid enterprises, Power Internet of Things (IoT) technology has been widely adopted as a core tool. This work first analyzes the technological needs of power grid enterprises in enhancing new quality productivity, highlighting the roles of key technologies such as big data, edge computing, and intelligent devices. It then constructs a Power IoT architecture based on technologies like big data, edge computing, and cloud computing to improve the new quality productivity of power grid enterprises. An empirical study of a power grid enterprise is conducted to validate the effectiveness of this Power IoT architecture. The results indicate that after implementing Power IoT, the enterprise's energy efficiency improvement rate increases by 146.2%, equipment failure rate decreases by 52.3%, operation and maintenance costs are reduced by 30%, and load control response time is shortened by 46.7%. These outcomes demonstrate the significant impact of Power IoT technology on enhancing new quality productivity in power grid enterprises. The findings provide a solid basis for implementing intelligent technologies in power grid enterprises and promote the development of power systems towards greater efficiency and intelligence. © 2024 ieee.

关键词： Empirical Research New Quality Productivity Power Grid Enterprises Power IoT

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Software-Defined-Storage Performance Testing Using Mininet

Software-Defined-Storage Performance Testing Using Mininet

引用

2024 ieee international conference on Information technology, Electronics and intelligent Communication systems, ICITEICS 2024

作者： Naik, Deepak Ranoji Ghonge, Vilas D. Thube, Smita Mangesh Khadke, Abhijit Mali, Yogesh Kisan Borate, Vishal Kisan G.H. Raisoni College of Engineering and Management Department of Computer Engineering Pune India Viit Department of Computer Engineering Pune India Nutan Maharashtra Institute of Engineering and Technology Department of Computer Engineering Pune India G H Raisoni College of Engineering & Management Department of Aiml Pune India Dr D y Patil College of Engineering & Innovation Department of Computer Engineering Pune India

ISBN: (纸本)9798350382693

Customary capacity the board methods are turning out to be less powerful for dealing with this huge volume of information because of the improvement of server farms and the unforeseen ascent away requirements. By eliminating the capacity control exercises out of the genuine information stockpiling media and placing them in the product layer of a concentrated regulator, Programming Characterized Stockpiling offers an answer for this issue. An insightful programming stack named 'programming characterized foundation' may deal with reasonable product equipment. A software engineer known as programming characterized capacity controls information capacity assets without the utilization of real equipment. Making the capacity engineering of a genuine Programming Characterized Stockpiling framework with no reenactment or imitating is an exorbitant and unsafe technique. Subsequently, the framework should be recreated prior to being utilized, in actuality. In this manner, virtualized proving grounds for such frameworks should be reenacted before genuine execution and sending. In this article, we evaluate software-defined storage engineering using the Programming Described Association (PDA) test framework, Mini-net. This framework is designed, assembled, and ready to advance without revisiting the established assembly structure. The core components of Mini-net have been modified to address data distribution challenges in contemporary SD storage and to scale storage capacity appropriately within this simulated environment. © 2024 ieee.

关键词： Software testing

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Pedestrian Behavior Prediction Method for intelligent Vehicles Based on Convolutional Neural Network

Pedestrian Behavior Prediction Method for Intelligent Vehicl...

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2022 ieee international conference on Unmanned systems, ICUS 2022

作者： Gao, Hongbo He, Xi Wang, Liuchang Zhang, Fei Cai, Kaiquan Fang, Xiaozhao University of Science and Technology of China Department of Automation Hefei China College of Mathematical Sciences Anhui University Hefei China School of Electronic Information Engineering Beihang University Beijing China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning. © 2022 ieee.

关键词： Trajectories

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Development of intelligent Fault Diagnosis and Predictive Maintenance Algorithm in Photovoltaic Power Generation System

Development of Intelligent Fault Diagnosis and Predictive Ma...

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2024 international conference on Power, Electrical Engineering, Electronics and control, PEEEC 2024

作者： Guan, Junning Lei, Guosheng Jiang, Daofang Zhou, Yifan Zhuhai Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangdong Zhuhai519000 China

ISBN: (纸本)9798350378917

With the global awareness of renewable energy and environmental protection rising, photovoltaic power generation system (PPGS), as an important part of clean energy, its stable operation and fault management has become a research hotspot. Aiming at the problem of fault diagnosis and predictive maintenance in PPGS, this paper proposes an intelligent fault diagnosis and predictive maintenance algorithm based on deep learning and time series analysis technology. By monitoring the operation data of key equipment in PPGS in real time, the algorithm uses features extraction techniques such as wavelet transform (WT) and deep neural network (DNN) model to automatically learn the deep-level features in the data, thus realizing accurate fault prediction and health status assessment of photovoltaic inverter. In order to quantitatively evaluate the health status of inverter, this study constructs the health status index (HSI) based on the output of deep learning model, and uses ARIMA model to predict the trend of HSI, so as to warn the possible faults in advance. The experimental results show that the proposed algorithm can effectively reflect the health state changes of the inverter, which provides strong support for preventive maintenance. This algorithm is superior to the comparison algorithm in the accuracy and timeliness of fault prediction, and has high feasibility and practical application value. © 2024 ieee.

关键词： Predictive maintenance

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intelligent Filament Production: Real-Time control and Optimization of Recycled Plastic Extrusion for 3D Printable Filaments 12

Intelligent Filament Production: Real-Time Control and Optim...

引用

12th RSI international conference on Robotics and Mechatronics, ICRoM 2024

作者： Patel, Manush Veronica, Andrea Varadharajan, Pranav Kumar National Institute of Technology Tiruchirappalli Department of Electrical and Electronics Engineering Tiruchirappalli India National Institute of Technology Tiruchirappalli Department of Mechanical Engineering Tiruchirappalli India National Institute of Technology Tiruchirappalli Department of Production Engineering Tiruchirappalli India

ISBN: (纸本)9798331529734

This paper introduces a novel system for the efficient conversion of plastic waste, including Polystyrene (PS), into 3D printable filament. Through systematic experimentation and optimisation, the system demonstrates its adaptability to various plastic waste materials while ensuring precise control and user-friendliness. Utilizing a Gradient Boosting Regressor model in the Internet cloud, the system predicts and optimizes extrusion temperature and speed based on real-time sensor data, ensuring precise control for various waste materials. It integrates embedded systems and the Internet of Things (loT) which automates key processes, enabling user-friendly monitoring and control. By uniting electronics with sustainability, this system addresses the global plastic waste challenge, streamlining recycling while making 3D printing technology accessible. This system advances sustainable waste management, emphasising plastic recycling and broadening access to 3D printing technology. © 2024 ieee.

关键词： Plastic recycling

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Optimizing Solar Energy Harvesting: A Comprehensive Review of intelligent MPPT For Battery Charging Circuits 3

Optimizing Solar Energy Harvesting: A Comprehensive Review o...

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3rd international conference on Power Electronics and IoT Applications in Renewable Energy and its control, PARC 2024

作者： Bharti, Kamlesh Kumar Pratima Patel, Anugya Jaiswal, Yatik Shekhar, Yogesh Rajkiya Engineering College Department of Electrical Engineering U.P. Kannauj India

ISBN: (纸本)9798350309409

In this paper an extensive literature review on a renewable energy problem has been highlighted. Solar photovoltaic (SPV) systems have garnered considerable interest in the energy sector owing to the rising demand for sustainable energy sources. To boost the efficiency of SPV systems, Maximum Power Point Tracking (MPPT) methods are extensively utilized. This DC-DC Buck-boost converter, which operates within the system, elevates the output voltage level and effectively monitors the maximum power point (MPP) across fluctuating temperature and irradiance conditions. The most commonly used MPPT technique is Type-1 Fuzzy Logic (T-1 FLC), but with the continuing development of technologies, we study that the Interval Type-2.0 Fuzzy Logic controller (IT-2.0 FLC) in SPV systems for battery charging offers more robust and accurate control compared to the T-1 FLC. The MPPT technology optimizes power output and prolongs battery lifespan, making, the solar PV system more reliable and sustainable. © 2024 ieee.

关键词： Buck-Boost converter

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Event-Triggered Decentralized Guaranteed Cost control for Interconnected Large-Scale System Using Adaptive Dynamic Programming

Event-Triggered Decentralized Guaranteed Cost Control for In...

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2023 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2023

作者： Shao, Zhi Liang, Yuling Xing, Jin Su, Hanguang School of Artificial Intelligence Shenyang University of Technology Liaoning Shenyang China School of Information Science and Engineer Northeastern University Liaoning Shenyang China

ISBN: (纸本)9798350309003

A novel decentralized guaranteed cost control (GCC) method for interconnected large-scale systems is proposed by utilizing the event-triggered adaptive dynamic programming (ADP) approach. First, the solution of GCC issue can be achieved by converting it into zero-sum differential game problem of sub-systems based on the ADP algorithm. An improved value function is designed and the guaranteed cost (GC) can be acquired by solving the Hamilton-Jacobi-Bellman (HJB) equation. Second, to reduce the execution frequency of control tasks and decrease the consumption of communication and computing loads, an event-triggered ADP-based decentralized GC approach is developed for interconnected systems with uncertainties. Finally, simulation results are given to show the feasibility of the proposed algorithm. © 2023 ieee.

关键词： Large scale systems

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Innovative Solutions for Crop Health: A Hybrid Approach to Cotton Root Rot Severity Assessment

Innovative Solutions for Crop Health: A Hybrid Approach to C...

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2024 ieee international conference on Information technology, Electronics and intelligent Communication systems, ICITEICS 2024

作者： Banerjee, Deepak Sharma, Neha Chauhan, Rahul Singh, Mukesh Kumar, Bura Vijay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350382693

The proposed study aims to enhance our comprehension and control of the gravity of cotton root rot by integrating agricultural practices with technological advancements. The combination of a CNN, which possesses knowledge, with an SVM greatly enhances its ability to detect root rot. It possesses great strength, exhibits high adaptability, and demonstrates exceptional speed in task execution. The test results indicate that Cotton, a significant component of global agriculture, is currently confronted with a persistent issue caused by detrimental root rot. The cause of this issue is Phymatotrichopsis omnivora, which has the potential to negatively impact productivity if not well managed. Effectively managing illnesses relies on meticulous and prompt attention to the severity of root rot. Executing this challenging undertaking requires innovative concepts to achieve success. This study used a mixed model as a response. The method combines the properties of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) to develop a sophisticated approach for quantifying the severity of cotton root rot infections. Our inquiry starts with a substantial collection of 9,000 dispersed photographs. These high-resolution photographs depict the several manifestations of root rot in plants in proximity. Demonstrating the effectiveness of our blend model, we present a comprehensive accuracy rate of 98.56%. This demonstrates its proficiency in providing accurate and comprehensive threat assessments. The model's accuracy, memory recall, and F1 score serve as indicators of its performance. © 2024 ieee.

关键词： Cotton

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Resource Allocation and Optimization for Federated Learning in Vehicular Edge Caching

Resource Allocation and Optimization for Federated Learning ...

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2024 ieee international conference on Unmanned systems, ICUS 2024

作者： Yin, Chenlong Li, Xuebin Wang, Xiaowei Li, Yang Xu, Biao Bian, Yougang College of Mechanical and Vehicle Engineering Hunan University Changsha China Wuxi Intelligent Control Research Institute Hunan University Wuxi China

ISBN: (纸本)9798350384185

Edge caching enables vehicles to access content directly from roadside units (RSUs), which can effectively improve the user experience in the current era of content explosion. However, due to the limited cache capacity of RSUs, it is of great importance to cache hotspot content on RSUs and improve the hit rate of cached content. Many researchers employ federated learning to train models for content prediction, which protects user privacy and utilizes user information data. However, few studies have considered the distribution and aggregation of federated learning models in the context of restricted RSU communication resources. In this paper, we investigate the use of federated learning for edge caching and propose a Resource Allocation for Federation Learning in Content Caching (RAFLCC) scheme. The problem is modeled as an energy minimization problem under time constraints. Based on the modeled problem, a reasonable allocation of resources is achieved, allowing more vehicles to participate in federated learning training. The experimental results demonstrate that RAFLCC outperforms other baseline caching schemes. © 2024 ieee.

关键词： Federated learning

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