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 min...
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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 set...
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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.
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 segme...
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An integral part of the Earth39;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 Con...
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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.
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 p...
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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.
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 pro...
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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.
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 system...
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.
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...
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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 grad...
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.
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 ...
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 machineintelligence 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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