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Real-Time Water Quality Monitoring via Impedance Spectroscopy and machine learning

Real-Time Water Quality Monitoring via Impedance Spectroscop...

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international conference on Agro-Geoinformatics

作者： Oğuzhan Aybar Zeynep Kara Meriç Yücel Burak Berk Üstündağ Computer and Informatics Department Istanbul Technical University Istanbul Turkiye National Software Certification Research Center Istanbul Technical University Istanbul Turkiye

ISBN: (数字)9798350380606

ISBN: (纸本)9798350380613

Water quality is crucial for plant growth, with factors like salinity levels significantly impacting crop health. Variations in water quality, especially from wells, necessitate regular monitoring. Existing methods are often costly, time-consuming, or unsuitable for continuous monitoring. this study utilizes impedance spectroscopy for real-time water quality monitoring in irrigation systems and machine learning methods for prediction. the proposed method captures spectral features and employs a compact machine-learning model for efficient and accurate pattern recognition, outperforming traditional electric conductivity measurements. Experiments measured various spectral features from water with different concentrations of NaCl, MgSO 4 , and their mixtures, across 1 kHz to 1 MHz using an Analog Discovery 2 device. data from these experiments were used to estimate solute concentrations. machine learning methods, including Random Forest and Multilayer Perceptron, were employed to predict NaCl and MgSO 4 concentrations in mixed samples. Results demonstrate significant improvements over existing methodologies, supporting continuous monitoring. With 55.4 ppm MAE for NaCl and 121.5 ppm MAE for MgSO 4 , the prediction results are promising for real-time water quality monitoring. Implementing farmer-specific devices could enhance agricultural automation by setting thresholds, warnings, and enabling automatic responses.

关键词： Spectroscopy Irrigation Salinity (geophysical) Water quality Multilayer perceptrons Real-time systems pattern recognition

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Review on the Development of Few-Shot Specific Emitter Identification Technology

Review on the Development of Few-Shot Specific Emitter Ident...

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Electronic Information Engineering and Computer Science (EIECS), 2021 international conference on

作者： Dian Lv Zhiyong Yu Jiawei Xie Hao Zhang Rocket Force University of Engineering Xi'an China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the face of a complex and ever-changing electromagnetic environment, the rapid increase in the number and types of radiation sources has made traditional Specific Emitter Identification techniques, which rely on conventional machine learning and statistical features, difficult to apply. In recent years, deep learning models have demonstrated exceptional performance in areas such as image recognition, prompting many researchers to incorporate deep learning into radiation source identification tasks. However, deep learning requires vast amounts of high-quality data, and in practical electromagnetic environments, it is challenging to collect and annotate radiation source signals. As a result, few-shot specific emitter identificatio has become a new focal point of research. this paper first introduces the radiation source identification system and reviews the research on traditional radiation source identification techniques. Next, it provides a detailed overview of few-shot specific emitter identification techniques categorized based on the radiation source identification technology framework, including data augmentation, metric learning, and model-based methods. Finally, the paper analyzes the current challenges in Few-Shot Specific Emitter Identification technology and offers prospects for future research.

关键词： Deep learning Measurement Image recognition Reviews Radar Interference data augmentation data models Robustness Electromagnetics

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Research and Application of an Improved Apriori Algorithm in Market Basket data 23

Research and Application of an Improved Apriori Algorithm in...

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Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Xuexian Qiu Shiyong Ning Shihui Zhang Zhuorui Yang School of Computer and Information Engineering Harbin University of Commerce China

ISBN: (纸本)9798400709449

In data mining, association rule mining has been proven to be a valuable strategy. the Apriori algorithm, proposed in 1993, is one of the most classic association rule mining algorithms and has been widely used in various domains to date. However, the algorithm itself requires multiple scans of the dataset, making the efficiency improvement of the algorithm an important topic. In this study, focusing on the association rule mining problem in shopping basket datasets, we address the efficiency and accuracy issues of the traditional Apriori algorithm in this domain and propose a novel improved Apriori algorithm. this algorithm enhances the accuracy and time efficiency of rule mining by introducing deletion and identification modules that exploit the characteristics of shopping basket datasets. through mathematical reasoning and experimental validation, it is demonstrated that the improved Apriori algorithm achieves higher accuracy and time efficiency, thus proving its superior

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Research on Power Outage data mining pattern using Apriori-based Association Rule Algorithm

Research on Power Outage Data Mining Pattern using Apriori-b...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Xiang Chen Minyu Luo Weilang Song Fan Pan Hongling Chen Dan Lin State Grid Fujian Marketing Service Center (Metering Center) Fuzhou China Fuzhou University Fuzhou China Fujian Power Supply Service Company Limited Fuzhou China

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Power outage analysis is critical for ensuring grid reliability, optimizing infrastructure, and improving emergency response strategies. Traditional methods for analyzing power outage patterns often face inefficiencies due to the large-scale, real-time nature of outage data and the complex dependencies between events. this research introduces a data mining framework leveraging the Apriori algorithm within Association Rule mining to extract meaningful patterns from real-time power outage data. the proposed model utilizes the ODIN Real-time Outages dataset, which provides near real-time power outage information at the county level across the China. By applying the Apriori algorithm, the framework efficiently identifies frequent outage patterns, uncovering hidden relationships between occurrences, affected regions, and influencing factors. Experimental results validate the effectiveness of the approach, achieving a Rule Confidence of 92.4% and a Support threshold of 85.7%, ensuring high-quality pattern extraction. Additionally, a Lift Ratio of 1.8 and Association Rule Coverage of 89.6% confirm the model’s robustness and predictive capability. these findings highlight the potential of Apriori-based rule mining in transforming outage management by providing insights, enhancing predictive maintenance, and supporting intelligent decision-making.

关键词： Adaptation models Computational modeling Prevention and mitigation Predictive models Prediction algorithms Real-time systems Robustness Power system reliability Association rule learning Integrated circuit modeling

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Use of machine learning in Object Detection for Visually Impaired Person

Use of Machine Learning in Object Detection for Visually Imp...

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Intelligent Technologies (CONIT), international conference on

作者： Tahmina Haque Munia Kazi Abu Taher Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

thisreport presents an object detection model for visually impaired persons for object detection, face recognition, navigation and distance measurement. the system model uses a webcam to captures visual images. the images are compressed to a resolution of 300×300 pixels. the MobilenetV3 model is used for object detection which then are classified into human and non-human types. A face detection algorithm is used to recognize human faces. the distance measurement process uses the focal length of the camera and a calibration pattern to calculate the distance of the objects. the proposed object detection model achieved an impressive performance on the test dataset with an average precision of 92.5%, achieving an accuracy of more than 98% for human detection and approximately 91.7% for other objects. the proposed integrated model’s high accuracy and precision make it a promising solution for the visually impaired people

关键词： Visualization Accuracy Navigation Webcams Face recognition Computational modeling Object detection

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Peculiarities of organizing the workspace using machine learning methods 4

Peculiarities of organizing the workspace using machine lear...

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4th international Workshop of Modern machine learning Technologies and data Science, MoMLeT and DS 2022

作者： Basyuk, Taras Vasyliuk, Andrii Demkiv, Lyubomyr Lviv Polytechnic National University Bandera str.12 Lviv79013 Ukraine

the article analyzes the space organization topic, defines its characteristics, and gives the factors that contribute to the conduct of this research. As a result of the research, an analysis of the subject area was carried out, and it was shown that today there is a limited set of tools (libraries) used in the process of pattern recognition, among the most famous are: DeepFace, TensorFlow and OpenCV. A description of the concepts of machine learning and recommender systems is given, which makes it possible to determine the main principles of their construction. A systematic analysis of the subject area was carried out and a tree of goals was built, which showed the subordination and interrelationship of goals in achieving the general goal. A mathematical description of the subject area was carried out using the algebra of algorithms, which provided the means to minimize created models by the number of uniterms. the presented models give a complete idea of the system construction features. the design of the software system was carried out using an object-oriented approach and the display of created diagrams in accordance with the notation of the UML language. the study presents diagrams of use cases and classes, which simplified the understanding of the information system functioning features for organizing the workspace using machine learning methods. the result of the research was the design and implementation of the system using a relational database and the Android Studio environment. the created software product works in prototype mode and implements the described functionality. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Information systems

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Betterment of Classroom and Examination Performance Using Historical data - A Survey 4

Betterment of Classroom and Examination Performance Using Hi...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Rajaraman, Aakash Yashas, S. Ananya, A. Vivek, V. India

ISBN: (纸本)9781665474368

In higher education, understanding, modelling, and forecasting student performance presents considerable hurdles in terms of developing accurate and effective diagnostic models. Several researchers have attempted to construct intelligent classifiers for predicting student accomplishment, but they failed to recognise the necessity of identifying the main components that contribute to the obtained performance. Such recognition is vital for programme directors to evaluate the strengths and limitations of their academic programmes and, as a result, make the required corrections to improve student accomplishment. Consequently, there is room to present a model that improves the accuracy of predicting student academic performance, as measured by future grades in various courses, and, also an optimized multi-label classifier that predicts qualitative values for the influence of various factors on the obtained student performance, primarily using a feed-forward neural network. the goal of the experimental findings are to demonstrate significant gains over single baseline models (e.g., linear regression, matrix factorization), indicating the applicability of the suggested technique in identifying several factors influencing student performance. this study underlines the need of predicting student achievement of learning outcomes in future studies. Along with this, another model that proved to be precise and accurate in predicting the outcome in a similar but not identical scenario, was the KNN classifier, which used data to classify the graduation chances of students. Using the classifiers in the data sets, there are several analyses that can be covered in terms of question pattern, format, and more. this study aims to push a new service that can be established in several organizations and locations to benefit educational professionals and students. the datasets are naturally created in the course of the university's classes. this allows for a lot of flexibility in terms of o

关键词： Students

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the Effective Detection of Alzheimer in the System via ML Algorithm

The Effective Detection of Alzheimer in the System via ML Al...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： R. Kishore Kanna Kapil Joshi Gunjan Chhabra Ambali Pacholi X.S. Blessing NimaSajai Flosy R. Menezes Department of Biomedical Engineering Jerusalem College of Engineering (Autonomous) Chennai India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand School of Nursing Science ITM University Gwalior (MP) India College of Nursing Khamis Mushayt King Khalid University Kingdom of Saudi Arabia Institute of Nursing Education Bambolim(Goa) India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Effective therapy for Alzheimer’s disease depends on early diagnosis. Key results from four research that used machine learning to detect Alzheimer’s disease during its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. the second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer’s defect conditions are detected using machine learning algorithms. they stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. the soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. these papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer’s disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data.

关键词： Neuroimaging machine learning algorithms Accuracy Sensitivity machine learning Classification algorithms Alzheimer's disease

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Advances in Big data and data mining: Techniques and Applications in data Fusion for Enhanced Insights and Decision-Making

Advances in Big Data and Data Mining: Techniques and Applica...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Meenakshi Maindola Ramy Riad Al-Fatlawy Rakesh Kumar Dr. Nandini Shirish Boob S P Sreeja N Sirisha Department of Computer Science & Eng. Graphic Era Deemed to be University Dehradun Department of Computers Techniques Eng. College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Eng. & Applications GLA University Mathura Saveetha School of Eng. Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Master of Computer Application New Horizon College of Engineering Bangalore Department of Computer Science and Eng. MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Big data's explosive growth has transformed several businesses by making it possible to glean insightful information from big, complex databases. this study examines new developments in big data and data mining methods, concentrating on data fusion as a means of improving decision-making. data fusion, the act of combining many data sources to get more accurate and thorough insights, is becoming essential in industries including cybersecurity, healthcare, finance, and marketing. the study looks at how cutting-edge data mining methods, such as anomaly detection, classification, and clustering, might increase the efficiency of data fusion. Important issues are also covered, including managing heterogeneous data, guaranteeing data quality, and scalability. this paper shows how data fusion approaches may enhance decision-making processes by offering more comprehensive perspectives and useful insights via an analysis of real-world applications. the paper's conclusion discusses potential future developments, emphasising how machine learning and artificial intelligence may be used to improve data mining and fusion procedures, potentially leading to even higher levels of precision and effectiveness in decision-making across a range of industries.

关键词： Industries Soft sensors Decision making data integration Medical services machine learning Big data Real-time systems data mining Anomaly detection

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7th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2021

7th EAI International Conference on e-Learning, e-Education,...

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7th EAI international conference on e-learning, e-Education, and Online Training, eLEOT 2021

ISBN: (纸本)9783030843823

the proceedings contain 104 papers. the special focus in this conference is on e-learning, e-Education, and Online Training. the topics include: data mining Method of English Online learning Behavior Based on machine learning Technology;intelligent Recommendation Model of Distance Education Courses Based on Facial Expression recognition;online Teaching Method of Conductive Characteristics of Graphene Materials Based on Big data mining;distance Teaching Method of Accounting Informatization Course Based on Big data mining;University Ideological and Political Multimedia Network Teaching Based on MOOC;an Efficient Teaching Method for Physiological Basic Course of Online and Offline Integration;decision Support Model for Online Education of Logistics Management Major from the Perspective of "Credit Bank";comprehensive Control Method of Network Teaching data Scheduling Based on Fuzzy Mathematics;design of Personal learning Model recognition Model for Online Teaching of Ideological and Political theory Course;reform and Practice of Digital Teaching of Engineering Mechanics Course Under the Background of "Double High Program";Research on the Ideological and Political Teaching Mode of Dual System Curriculum in Colleges and Universities Based on MOOC;pattern recognition Method of Training Japanese Talents in Online Education;research on Hybrid learning Mode of Financial Management Course in Cloud Classroom Environment;research on pattern recognition Method of Online English Education Based on Feature Self learning;pattern recognition Method of English Distance Online Education Based on Big data Algorithm;big data Based Human Resources Professional Distance Training Platform;design of Personalized Employment Guidance System for College Students Based on Big data.

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