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共 6821 条记录，以下是71-80 订阅

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A Review on Non-overlapping pattern Matching and Mining 24

A Review on Non-overlapping Pattern Matching and Mining

引用

3rd international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2024

作者： Li, Yongyi Shen, Cong Wu, Youxi Tianjin University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China

ISBN: (纸本)9798400707308

pattern matching under non-overlapping condition denotes to the identical character in any two occurrences that cannot occupy the position of the pattern twice in an equivalent manner. It has advantages in pattern mining as it considers both matching validity and efficiency. Non-overlapping pattern mining is viewed as a related operation that is conducive to excavating potential patterns under the non-overlapping conditions. However, since the flexible wildcard in non-overlapping pattern matching and mining is equipped with both the traditional wildcard properties and complicated constraint construction, it involves more challenges in the calculation process. Non-overlapping research has made significant promotion, including theory, algorithms, and applications in bioinformatics, finance, health care, cyber-security, etc. For brevity, state-of-the-art works focused on sequential and graphic pattern matching and mining, which deal with patterns in sequential form or image to handle the matching and mining straightforwardly. This review summarizes the development of non-overlapping pattern matching and mining in three aspects. 1) available algorithms and tools;2) challenges, including indices construction w.r.t. matching problem, reasonable pattern, and threshold selection, the derivative problem types based on sequential data, etc.;3) future development opportunities in the background of massive data, booming of artificial intelligence, and rapidly growing computational power. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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NeuroAqua: Developing an Optimized Artificial intelligence and Internet of Things-based Aquaponics System 3

NeuroAqua: Developing an Optimized Artificial Intelligence a...

引用

3rd international conference on Computing and machine intelligence (ICMI)

作者： Jiang, William Liu, Jayden Harker Sch San Jose CA 95124 USA

ISBN: (纸本)9798350372977;9798350372984

This paper aims to solve challenges concerning cost, manual labor, and efficiency in aquaponics by designing and implementing NeuroAqua, an optimized and automated AI and IoT-based aquaponics system, in both a lab setting (small-scale) and a field setting (large-scale) in Ouroboros Farms in Half Moon Bay, CA. In the system, wireless sensors monitor key factors for system effectiveness and send data to a cloud database. NeuroAqua integrates 6 types of sensors, collecting data regarding the environment, water, and nutrients, such as air temperature, humidity, pH, light, Nitrogen, Phosphorus, Potassium, and Total Dissolved Solids. Camera module sensors were also developed to capture and monitor plant growth automatically. These 6 types of sensors were implemented in both settings and successfully collected 8 plant growth samples (2 types of plants in 4 different settings) each across 2 weeks from NeuroAqua. NeuroAqua transformed the data from the sensors into a structured database, which was fed to the machine learning (ML) models for data analysis. It also feeds data to our online monitoring system and sends out alarms when the tested values fall below or exceed the normal range. NeuroAqua is the first aquaponics system to implement a comprehensive automatic machine learning system into a real farm setting.

关键词： Aquaponics AI Internet of Things Sustainable Agriculture Automatic Farming Smart Sensors

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Research on Automatic Workpiece Sorting System Based on machine Vision 3

Research on Automatic Workpiece Sorting System Based on Mach...

引用

2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Ji, Linfeng Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350341546

This paper expounds the automatic recognition method of parts based on computer vision. The feature database of the processed parts is constructed by using machine learning method. Image preprocessing, threshold segmentation, edge and contour extraction are used to analyze the features of the image of the inspected part to judge whether the inspected part is qualified or not. Aiming at the attitude error generated when the workpiece is placed, a method for positioning the center of gravity and rotation angle of the workpiece based on boundary detection and contour tracking is proposed. Use the similarity measure to determine the characteristics of the object to be processed. Simulation results show that the method proposed in this paper is effective. © 2023 IEEE.

关键词： image processing machine vision pattern recognition workpiece

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Predicting Earthquakes with Ionospheric Data: A Hybrid Approach Utilizing Deep AutoEncoder and LSTM Networks 3

Predicting Earthquakes with Ionospheric Data: A Hybrid Appro...

引用

3rd international conference on Computing and machine intelligence (ICMI)

作者： Abri, Rayan Abri, Sara Artuner, Harun Cetin, Salih Dept Artificial Intelligence Mavinci Ankara Turkiye Hacettepe Univ Dept Comp Engn Ankara Turkiye Mavinci Cambridge England

ISBN: (纸本)9798350372977;9798350372984

An integral part of the Earth's atmosphere is driven by the ionosphere. Solar flares induce ionosphere anomalies as a result of coronal mass ejection, seismic activity, and geomagnetic activity. Total Electron Content is the primary metric used to study the ionosphere's structure (TEC). GPS-derived TEC values are useful for examining how the ionospheric response to earthquakes is affected. In order to identify earthquakes, this article examines the relationships between TEC data and earthquakes. Our aim is to suggest a classification strategy for identifying earthquakes that occurred in earlier days. This research discusses the ionospheric variability during moderate and severe earthquake events of varied intensity for the years 2012-2019. Deep Autoencoders are used by the suggested model to extract features from TEC data. A Stacked LSTM model was constructed using the features gathered to forecast the earthquakes that occurred in the preceding days. For evaluation, the suggested hybrid model is compared with the Support Vector machine (SVM) and Linear Discriminant Analysis (LDA) classifier models. According to the findings, the suggested hybrid model increases earthquake detection with an accuracy rate of roughly 0.84 and is a useful tool for identifying earthquakes based on prior days.

关键词： Total electron content Long short term memory Deep autoencoder Support vector machine Linear Discriminant Analysis

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Optimized Neural Networks for Diabetes Classification Using Pima Indians Diabetes Database 3

Optimized Neural Networks for Diabetes Classification Using ...

引用

3rd international conference on Computing and machine intelligence (ICMI)

作者： Ashour, Ahmed F. Fouda, Mostafa M. Fadlullah, Zubair Md Ibrahem, Mohamed I. Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Western Univ Dept Comp Sci London ON Canada Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA

ISBN: (纸本)9798350372977;9798350372984

Integrating artificial intelligence (AI) into the healthcare sector holds immense potential for transforming the industry, promising notable improvements in diagnostic precision, treatment effectiveness, and overall patient care. This paper explores the detection of diabetes using two types of neural networks - feedforward neural network (FNN) and convolutional neural network (CNN) - with the Pima Indians diabetes database (PIDD). To evaluate the efficiency of the proposed models in diagnosing diabetes, various essential metrics are utilized, including accuracy, precision, recall, F1-score, specificity, receiver operating characteristic - area under the curve (ROC-AUC), log loss, false positive rate (FPR), Youden's index, and Matthews correlation coefficient (MCC). The proposed FNN model achieves an impressive accuracy rate of 82%, outperforming previous methodologies, whereas the CNN displays commendable accuracy of 80.52%. Both models demonstrate superb performance in terms of accuracy, specificity, and AUC, highlighting their effectiveness in binary classification when compared to prior studies. This research provides valuable insights into utilizing advanced machine-learning techniques for the early detection of diabetes.

关键词： Neural network diabetes classification Pima Indians diabetes database artificial intelligence FNN CNN medical diagnosis machine learning

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A Unique Block-Based 3D U-Net Deep Learning Model for Brain Metastasis MRI Image Segmentation and Classification 3

A Unique Block-Based 3D U-Net Deep Learning Model for Brain ...

引用

3rd international conference on Computing and machine intelligence (ICMI)

作者： Zhou, Andrew Quarry Lane Sch Dublin CA 94568 USA

ISBN: (纸本)9798350372977;9798350372984

Brain metastasis (BM) is one of the primary neurologic complications of cancer, frequently originating in patients with lung cancer, breast cancer, colorectal cancer, and melanoma. Scarcity of data has limited the progression of deep learning studies to automate the classification and segmentation of BM magnetic resonance imaging (MRI) images. Furthermore, due to the nature of BM's, the available data is highly unbalanced with relatively diminutive cancer regions. This paper presents a novel 3D U-net deep learning model on the first public BM database. This study uses a novel technique to split the BM MRI images into smaller blocks, on which machine learning is performed primarily focused on cancer regions. By combining segmentation and classification loss functions, machine learning models can perform both BM segmentation and cancer region classification. two models are presented, the Aggressive model which is tailored towards precise segmentation that reached a Dice score of 0.88;and the Passive model which is suited towards detecting blocks that contain cancer with fewer false positives. Combining both models can detect 84% of all cancer cases. When both models agree, the prediction has a high positive predictive value (84.7% for cancer;99.3% for non-cancer) while when the models differ, the value is low (11.9% for cancer) and would merit further physician review.

关键词： U-Net Neural Network Brain Metastasis Segmentation Classification deep learning Artificial intelligence

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2024 3rd international conference on Embedded Systems and Artificial intelligence, ESAI 2024

2024 3rd International Conference on Embedded Systems and Ar...

引用

3rd international conference on Embedded Systems and Artificial intelligence, ESAI 2024

ISBN: (纸本)9798331522056

The proceedings contain 71 papers. The topics discussed include: artificial intelligence in quality 4.0;the impact of artificial intelligence on employee adaptability to remote work;robust control of mechanical systems applied on vehicle active suspension system;Moroccan Arabic Darija automatic speech recognition system using CNN model for drone control application;enhancing encryption of an RGB image by combining the Lorenz chaotic system and Arnold’s cat map;a multi-layered approach;efficient implementation of codebook algorithm for background subtraction;design and implementation of a low energy consumption data logger for a nano-grid applied to a solar smart greenhouse;study of motor oil adulteration employing a novel neural network approach for processing laser-induced fluorescence spectra;and enhancing photovoltaic maintenance with real-time data integration: a sensorless and scalable approach validated by intelligent correlation analysis.

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machine Learning Diagnosis of Lumpy Skin Disease in Cattle Herds 3

Machine Learning Diagnosis of Lumpy Skin Disease in Cattle H...

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Krishna, Nimmagadda Kumar Benjamin, Ruth Moly Tejaswi, Nimmagadda Institute of Technology and Sciences Division of Computer Science and Engineering Karunya Coimbatore India Shri Vishnu Engineering College for Women Division of Artificial Intelligence and Data Science Bhimavaram India

ISBN: (纸本)9798350375190

In order to diagnose lumpy skin disease in cattle herds, machine learning techniques such as Support Vector machine (SVM), Gradient Boosting, and Random Forest algorithms were used in this research work. The objective of the study is to create a diagnostic tool that would be both accurate and effective to identify and treat this infectious disease early on. The algorithms were trained to classify cases with high accuracy using a dataset that included different clinical and demographic characteristics related with the presence or absence of Lumpy Skin Disease. SVM is used because of its capacity to manage intricate and non-linear correlations in the data, and Random Forest and Gradient Boosting algorithms were chosen due to their predictive performance. © 2024 IEEE.

关键词： Support vector machines

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Proceedings of 3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

Proceedings of 3rd International Conference on Advanced Comp...

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3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

ISBN: (纸本)9798350348347

The proceedings contain 118 papers. The topics discussed include: hate speech detection using transformers;tweet analysis: what changed over time?;analysis of transfers learning techniques for early detection and grading of diabetic retinopathy on retinal images;FarmWise: crop cultivation analysis using image recognition and ArcGIS mapping;systematic literature review of rainforest surveillance;flood location, management and solution(FLMS): a flood prediction and management system for Kurla;machine learning technique for crop selection and prediction of crop cultivation;vision based floating garbage classification using SIFT;is LinkedIn going to become the next Facebook?;MSMEs readiness for adopting artificial intelligence and machine learning;and multimodal emotion recognition in video, audio, and text using deep and transfer learning.

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Proceedings - 2024 3rd international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2024

Proceedings - 2024 3rd International Conference on Artificia...

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3rd international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2024

ISBN: (纸本)9798350376173

The proceedings contain 179 papers. The topics discussed include: signal detection and demodulation algorithm based on deep learning in communication network;design and implementation of intelligent control algorithm for energy power system combined with reinforcement learning;application of artificial intelligence technology in flood early warning system;implementation of a machine intelligence pronunciation system based on Mul Tran to improve the basic English speaking skills;design and optimization algorithm of interactive education system based on virtual reality technology;application of artificial intelligence technology in intelligent environment design system;intelligent anti-collision algorithm of electric bicycle helmet based on Arduino and sensor data;and neural network algorithm in automatic allocation system of water resources.

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