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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是391-400 订阅

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Novel systems Based on Artificial Intelligence and Numerical algorithms for Predicting Laboratory Results: A Comparative Study of Original Automatic Prediction Model with Advances in the Field 18th

Novel Systems Based on Artificial Intelligence and Numeric...

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18th international conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Pawuś, Dawid Porazko, Tomasz Paszkiel, Szczepan Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska Street 76 Opole45–758 Poland Department of Internal Medicine and Nephrology Institute of Medical Sciences University of Opole Oleska 48 Street Opole45–052 Poland Department of Nephrology with Dialysis Unit Opole University Hospital Witosa 26 Street 46–020Opole Poland

ISBN: (纸本)9783031816840

This study develops and evaluates a predictive model for estimating glomerular filtration rate using advanced machine learning algorithms. We trained and tested seven models, including K-nearest neighbors, support vector machine, gradient boosting, AdaBoost, XGBoost, LightGBM, and linear support vector regression, with a focus on clinical parameters like creatinine, urea, sodium, potassium, age, and gender. Gradient boosting and XGBoost demonstrated superior performance, achieving over 96% accuracy and mean absolute percentage errors around 2.5%. Additionally, we reviewed recent advancements in the field and compared our findings with existing research. The comparative analysis highlights the effectiveness of our proposed models in accurately predicting laboratory test results. This underscores the potential of machine learning to enhance diagnostic accuracy and improve patient outcomes in nephrology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Intelligent Control Mechanisms for Renewable Energy Integration Using Machine Learning Models

Intelligent Control Mechanisms for Renewable Energy Integrat...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chahar, Suman Kaur, Kawaljit Kaswan, Kuldeep Singh Malik, Kiran University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering-AIML Greater Noida India

ISBN: (纸本)9798331508685

The deployment of renewable energy sources (RES) in power systems used to be a major challenge owing to their intermittent and uncertain nature. Therefore, in this paper, the use of ML models as an intelligent control system to improve the operation of RES is studied. We survey different types of ML approaches, covering supervised learning for energy predic- tion, unsupervised learning for clustering demand, reinforcement learning for energy storage management, and deep learning for real time data processing. The intent of these advanced control strategies are to balance and stabilize the grid, harmonize demand response, and augment energy storage. We highlight the use-cases and impacts of ML from real-world cases across various geographies, showing operational gains and peak-demand savings. While these technologies hold great promise, they also face challenges like data privacy, interpretability of models, and availability of high-quality datasets. Moving forward, there is a need to improve transparency of algorithms and to strengthen the collaboration of different stakeholders. Our work paves the way for a systematic and automated approach to renewable energy integration, which can serve as a critical factor to build a reliable and efficient future modern power systems with machine learning. © 2025 IEEE.

关键词： Adversarial machine learning

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Advanced remote sensing techniques for underwater Fin whale detection in the Indian Ocean 6

Advanced remote sensing techniques for underwater Fin whale ...

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6th IEEE international conference on Image Processing, Applications and systems, IPAS 2025

作者： Sanikommu, Vasavi Sura, Akshaya Chimirala, Pranavi Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331506520

The accurate detection and monitoring of whales in the Indian Ocean are vital for marine conservation but face significant challenges due to the complexities of underwater environments. Advanced remote sensing technologies offer potential solutions to overcome these limitations. This study integrates hyperspectral imaging, satellite imagery, and thermal imaging to develop a comprehensive whale detection system. Hyperspectral imaging captures the spectral reflectance of whale skin, enabling detection under varying water conditions. Satellite imagery aids in identifying subsurface features, while thermal imaging detects whales' thermal signatures. Machine learning algorithms process the combined data to enhance detection accuracy and reliability. The integrated system demonstrated effective detection of whales at various depths and environmental conditions in the Indian Ocean. Hyperspectral imaging proved sensitive to water turbidity and target reflectance, while the coastal blue band successfully identified subsurface whale features. Thermal imaging further enhanced detection capabilities in low-light conditions. The combined use of these technologies outperformed traditional methods, offering an accuracy of 0.96 and reliability for real-time whale monitoring. © 2025 IEEE.

关键词： Hyperspectral imaging

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Comparison in Detection of Shilling Attackers Using KNN and SVM

Comparison in Detection of Shilling Attackers Using KNN and ...

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international conference on systems, Control and Automation, ICSCA 2023

作者： Gambhir, Sarika Dhawan, Sanjeev Singh, Kulvinder Department of Computer Engineering University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra136119 India

ISBN: (纸本)9789819773831

Recommender systems based on collaborative filtering are vulnerable to shilling assaults owing to its openness. Shiller’s introduce fictitious profiles into the database of the system with the goal of modifying the recommendations to their advantage. To make product recommendations more reliable and robust, it is essential to find potential attackers concealed in recommender systems. Several shilling attack detection techniques have been proposed thus far, and they all have advantages and disadvantages that are complementary to different attacks. We present a thorough experimental evaluation of detection of attackers using K-nearest neighbour and Support Vector machine in this paper. Amazon electronic dataset is used to simulate several attack models. In the meantime, for investigation attackers are produced in our dataset using average-filling, random-filling and bandwagon attack models. For detection of shilling attack two models i.e., K Nearest neighbour and Support Vector Machine are also compared. Precision, recall, F1-score and accuracy are used to analysis the performance of these two algorithms. The result of our experiments shows that SVM is more accurate than KNN in detection of shilling attackers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wiener filtering

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Customized IoT based Smart Irrigation for Terrace Plants 6

Customized IoT based Smart Irrigation for Terrace Plants

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Priya, S. Padma Pavalarajan, S. Abarna, P.S. Manjula devi, C. Abinaya, B. Sukaina, S. Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of Information Technology Velammal college of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

An Internet of Things based smart irrigation system for terrace plants is a cutting-edge solution designed to automate and optimize the watering process, ensuring efficient use of water and promoting healthier plant growthSmart irrigation system may vary from field with crops of same variety to terrace plantation with plants of different varieties. As the water requirement may vary based on plant type, environmental condition, growth stage, it is intelligent to provide varying water supply. This Smart irrigation system employs soil moisture to monitor the water content of the terrace garden in real time. Some variety of plants may require minimum quantity of water. The sensors continuously collect data and send it to a central IoT platform or a cloud-based server, where intelligent algorithms analyse the data and determine the precise amount of water needed by the plants. Based on this analysis, the system automatically activates the irrigation mechanism whether it's a drip system, sprinklers, or any other appropriate watering method only when required, thus preventing water wastage and avoiding overwatering or underwatering. © 2025 IEEE.

关键词： Sprinkler systems (irrigation)

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Automated diabetes diagnosis and risk assessment using machine learning 2nd

Automated diabetes diagnosis and risk assessment using machi...

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2nd international conference on Next-Generation Computing and Information systems, ICNGCIS 2023

作者： Mathukiya, Deep Kumar, Yogesh Koul, Apeksha Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India Punjabi University Punjab Patiala India

ISBN: (纸本)9781032738659

Currently, Diabetes is a prevalent issue worldwide, affecting millions of people. The detection machinery for this disease is only available at healthcare centers, so individuals with diabetes are only aware of their condition when they visit and undergo a checkup. Prolonged diabetes can lead to various complications like heart disorders, kidney disease, nerve damage, and diabetic retinopathy. However, the risk can be reduced if the disease at its early stage is predicted. By utilizing a deep learning approach, this model can easily determine if an individual is diabetic or not. The aim of the research is to develop machine learning model that can identify diabetic people on the basis of their clinical data. The machine learning algorithms utilized in this study include Naive Bayes (NB), k-nearest neighbor (KNN), Stochastic Gradient Descent (SGD), Decision tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). We have implemented efficient pre-processing techniques to detect as well as handle missing/ NAN and duplicate values and later trained our models with the cleaned dataset. During execution, we found that Random Forest as well as Decision Tree performed well for both diabetic and non-diabetic class by computing 100% and 99% accuracy respectively. © 2025 The Author(s).

关键词： Decision trees

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Enhancing Dogo Rover Capabilities with Integrated Aerial Surveillance and Autonomy 2

Enhancing Dogo Rover Capabilities with Integrated Aerial Sur...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Raja, M. Sakthi Adhavan, K. Pt, Sakthiathavan Savitha Dhivya, P. Harikrishna, N. Kongu Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Erode638060 India

ISBN: (纸本)9798331505745

This project aims to enhance the capabilities of the DOGO Rover, a robotic platform used for security and reconnaissance, by integrating advanced features for both ground-based and aerial surveillance. The upgraded system employs a Raspberry Pi 3B paired with a 5MP camera module, enabling real-time image processing for autonomous pathway monitoring and facial recognition. This allows the rover to detect and alert the control room of any unrecognized individuals. Additionally, aerial surveillance is achieved through the incorporation of a drone, which is remotely controlled and stabilized with two servo motors to ensure reliable performance over rough terrains. The drone extends the system's surveillance capabilities by providing a broader field of view from above, complementing the ground-level operations of the rover. The project also focuses on enhancing the rover's autonomy through intelligent path-following algorithms, enabling adaptive navigation in dynamic environments. By integrating ground and aerial perspectives with advanced autonomy, this upgraded DOGO Rover presents a comprehensive solution for modern security challenges, offering improved situational awareness, operational reliability, and versatility. The combination of robotics, aerial technology, and artificial intelligence demonstrates a robust approach to addressing complex security requirements in various settings. © 2025 IEEE.

关键词： Motion analysis

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Evaluating Agricultural Soil GHG Emission Models: A Critical Analysis of Identification and Future Challenges

Evaluating Agricultural Soil GHG Emission Models: A Critical...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Pathak, Arnima Sharma, Monika Kumar, Devendra Amity School of Engineering Greater Noida India Amity University Noida India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798331508685

This study focuses on the increasing global challenge of the need for efficacy assessment of techniques and the critical problem of GHG emissions from agrosystems. It addresses their processes, input variables, and simulation results in different cropping systems to present a comprehensive analysis of four well- known process models: DNDC, DayCent, STICS, and APSIM. These may include model structure, management practices, weather conditions, and the level of data available at the time the simulation was built that influence the model's accuracy in the study. Development of appropriate measurement and reduction tools in agriculture is quite essential because, as the IPCC noted, agriculture plays a vital role in global anthropogenic emissions of greenhouse gases. This study puts forward an integrated hybrid framework, which includes both process-based models and geospatial techniques to increase the precision of emission assessments over varied agricultural systems. The proposed method showed promising improvement in the scalability and robustness of models to enable effective tailoring of mitigation strategies toward sustainable agricultural practices. This paper aims to enhance model accuracy and applicability by offering a methodology focusing on increasing the model's applicability area, unifying the data analysis, and improving the model's algorithms. This project aims to facilitate sustainable food production methods and climate change adaptation by providing better tools and methods to stakeholders by tackling the problem of identifying and managing soil GHG emissions. © 2025 IEEE.

关键词： Greenhouse gas emissions

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Privacy Preserving Attribute based Access Control with Data Deduplication using RSA-BLAKE3-OCO for Secure Cloud Storage 1

Privacy Preserving Attribute based Access Control with Data ...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Gayathri, G.N. Channakrishnaraju Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumkur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Nowadays, cloud storage is one of the popular application models in various fields, where the security and access permission of storage data are widely considered. De-duplication algorithms are designed to mitigate redundant storage of data in the cloud. However, the main problem was data duplication which occupied the storage in many cloud storage systems that require an appropriate data security model. To overcome this problem, Rivest-Shamir-Adleman (RSA) - BLAKE3 based Optimal Cache Oblivious (OCO) demonstrates ensured access and privacy while removing the duplicates in cloud computing. The combination of RSA and BLAKE-3 is used to maintain efficient attribute exchange and prevent data leakage and privacy violations. In addition, the OCO technique is used to hide the access pattern in the cloud storage and copy the data by matching the random pattern. This process ensures data privacy and secure data sharing in cloud storage by avoiding data corruption. The result analysis clearly shows that the proposed RSA-BLAKE3-OCO attains a communication overhead of 11MB, a data integrity rate of 93% and a data confidentiality rate of 95% from 50 files which is better when compared with the existing methods. © 2025 IEEE.

关键词： Data integrity

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Design home security using artificial intelligence 1st

Design home security using artificial intelligence

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1st international conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Gautam, Nikhil Mojahid, Md. Murari, Krishna Sazid, Mohd Department of Electronic and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032874036

The advent of artificial intelligence has revolutionized various aspects of daily life, including home security systems. In this paper, we present a novel approach to designing a comprehensive home security system using Artificial intelligence technology. The system integrates various components such as Arduino Uno -ESP32 for data transmission, AWS IoT Core for cloud connectivity, a solenoid lock, and Wi-Fi modules for internet connectivity. Additionally, the system incorporates AI algorithms to monitor and control household devices like lights and fan, enhancing both security and energy efficiency. A key highlight of our approach is the use of AI- powered smart door locks, key, which distinguish between authorized and unauthorized individuals. This paper discusses the design, implementation, and evaluation of our system, highlighting its effectiveness in providing robust home security while minimizing false alarms. New home security systems consolidate with keen home advanced help. Lights can partner with self-observed home security system, turning on a proprietor’s lights in the event of a break-in. Things like Amazon’s Alexa, Apple’s Siri, or Google Assistant are additionally adequate with different alarms and security associations. © 2025 the Author(s).

关键词： Wi-Fi

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