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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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A Combination Prediction Model Based on SVM and Its application in Grain Output

A Combination Prediction Model Based on SVM and Its Applicat...

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2010 The 3rd international conference on Computational Intelligence and Industrial application (PACIIA2010)

作者： BU Hui and LIU Ran School of Information Engineering North China University of Water Conservancy & Electric Power Zhengzhou,Henan Province,China

Grain output forecast plays an increasingly important role in national *** grain output forecast is a complex nonlinear system and the historical data is typical small sample,the current methods cannot meet the accuracy *** get better prediction results,a new combination model based on support vector machine (SVM) and BP neural network is *** this model,firstly we use SVM to classify the initial sample ***,the processed data is used in neural network in selecting the model *** this way the learning speeding can be improved *** the forecast operation,the core modules of the two models are organized together organically and the control information can be passed to each other to adjust model *** organization and working pattern of the combination model enhances the stability and accuracy of forecast results.

关键词： SVM neural network combination forecast model production forecast wavelet network.

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New Parallel Decision Fusion Models for Improving Multiclass Object Detection in Drone Images

New Parallel Decision Fusion Models for Improving Multiclass...

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Computing and machine Intelligence (ICMI), international conference on

作者： Allison Nicole Paxton Abhishek Verma Department of Computer Science California State University Northridge CA USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Automatic object detection in drone images is an important area of research due to its application such as surveillance and security, reconnaissance, search and rescue mission, land monitoring, self-navigating drones. It is considered a challenging task due to the distance, camera angle, and complex surroundings. Filtering predictions from object detection models and combining the results can help improve the performance of individual models. Selecting the best predictions from predictions of multiple models is called ensemble parallel decision fusion. We augmented the large-scale grand challenge VisDrone2019-DET image dataset to create VisDrone-split and VisDrone-overlap datasets. We trained seven level-0 large deep learning models on the drone datasets and propose four Parallel Decision Fusion (PDF) deep learning models that improve in terms of the multiclass object detection precision upon their corresponding level-0 models. The seven models were split into two sets of object detection models. The first set of level-0 models includes Faster R-CNN, RetinaNet, and YOLOv5s. The second set consisted of much larger level-0 models namely state of the art YOLOv5x, YOLOv7x, YOLOv8x, and EVA02L. All proposed PDF models improved upon their corresponding state-of-the-art level-0 models on VisDrone-split. The PDF model with the highest metrics was YOLOv8x-s + EVA02L-s, which scored an mAP50 of 0.601 and improved 2.39% upon the state-of-the-art level-0 EVA02L model.

关键词： Deep learning YOLO Measurement Computational modeling Surveillance Predictive models Reconnaissance

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Harnessing Big Data for Economic Transformation in a Digitalized World

Harnessing Big Data for Economic Transformation in a Digital...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Riswan Efendi Tarigan Triyono Mahyuni Harry Agustian Rifky Nurfaizi Achani Rahmania Az Zahra Dept. of Information Systems Pelita Harapan University Tangerang Indonesia Dept. of Information Technology University of Raharja Tangerang Indonesia Dept. of Teacher Training and Education Science University of Mataram Mataram Indonesia Dept. of Computer Systems Sesindo Tangerang Indonesia Dept. of Information Technology Bosindo Tangerang Indonesia Dept. of Information Systems Pandawan Sejahtera Indonesia Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

Air is essential to life and health, with oxygen (20.94%) and the ozone layer protecting against ultraviolet radiation. Depletion of oxygen reduces the ozone layer, increasing UV exposure. Good air quality is crucial for living organisms. In 2022, Indonesia’s PM 2.5 pollution ranked 26th worst globally, exceeding WHO standards, yet awareness and trust in air quality devices remain low. This study aims to implement and evaluate an AI-based machine learning system to predict the Air Quality Index (AQI) in healthcare services. Using Structural Equation Modeling with Partial Least Square analysis via SmartPLS 4.0, the research involves 120 participants and historical air quality data. Comparative analysis of machine learning algorithms assesses accuracy, robustness, and efficiency, highlighting their potential in forecasting AQI and its implications for public health and urban planning. The study demonstrates the effectiveness of these algorithms in predicting AQI, with a significant value of 0.874 for forecasting accuracy.

关键词： machine learning algorithms Accuracy Urban planning Loading Air quality Mathematical models Indexes Forecasting Public healthcare Uninterruptible power systems

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Construction and application of Intelligent Sports CAI System Based on K-Means Clustering Algorithm 2021

Construction and Application of Intelligent Sports CAI Syste...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Cuiping Wu Liaokun Ye Han Wu Kunming University China WUHAN SPORTS UNIVERSITY China

ISBN: (纸本)9781450385046

Under the background of the rapid development of information technology, my country's sports industry is facing huge challenges. K-means clustering algorithm is a typical optimization method based on data characteristics and original sample data. Faced with the quantitative data of sports and the development of Internet technology, the design of intelligent sports teaching auxiliary system becomes more and more important. For this reason, this paper studies the intelligent sports CAI system based on k-means clustering algorithm, the purpose is to provide a way to assist sports teaching through system design. This article mainly uses the investigation method and the interview method to understand the students' opinions of the intelligent sports CAI system. And applied the data method to research and describe the K-means clustering algorithm, and analyze the existing problems of the computer-aided teaching system. The survey results show that 59.1% of people attach great importance to the teaching and learning of intelligent sports.

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Logging Curve Reconstruction Based on Multivariate Feature Fusion of Attribute Co-occurrence Relationships and BiLSTM

Logging Curve Reconstruction Based on Multivariate Feature F...

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Cloud Computing, Big Data applications and Software Engineering (CBASE), international conference on

作者： YunPeng Song ZhangLin Li XiaLin Zhang ZhengPing Weng School of Computer Science China University of Geosciences Wuhan Hubei

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Logging data play an important role in resource exploration, but in actual production, the logging attributes are often distorted or missing, which affects the geological work based on logging data. In this paper, a multivariate feature fusion model is proposed to address the problem of insufficient extraction of the intrinsic correlation between logging attributes in the traditional logging curve reconstruction method. Analogous to the method of extracting texture features in images, the Gray- Level Co-occurrence Matrices (GLCM) and Gradient Co-occurrence Matrices (GCM)are used to extract some kind of co-occurrence relationship that inherently exists between attribute values and trends, the fused features are fed into the BiLSTM network as a priori features in conjunction with the original attribute features, and the missing data are reconstructed using the trained model. The purpose of using this hybrid architecture is to extract the correlations between logging attributes of human interest in a previous step of network training, and to translate this type of important information into a form that can be learnt by the neural network, helping the network to learn more directly about the effective features in the logging data.

关键词： Training Knowledge engineering Correlation Neural networks Fitting machine learning Feature extraction Data models Data mining Image reconstruction

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Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

Emotion Representation in Whole-brain Functional Connectivit...

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2017 3rd international conference on Artificial Intelligence and Industrial Engineering (AIIE2017)

作者： Yin LIANG School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjin University

The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging(f MRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist f MRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions.

关键词： Emotion Functional magnetic resonance imaging Functional connectivity Multivariate pattern analysis machine learning

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Expression of Concern for: Utilizing machine learning to Develop an Improved Real-Time Data Analysis System

Expression of Concern for: Utilizing Machine Learning to Dev...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Harsimrat Khandari Girija Shankar Sahoo Raja Praveen K N Ritu Shree Umamaheswari B Yashwant V Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

With the constantly growing volume of facts being produced, conventional data evaluation structures are regularly insufficient in terms of presenting an correct overview of the most modern-day data. The utilization of system mastering provides the potential to create an improved real-time records evaluation device this is more able to making experience of and knowledge the records. The development of an progressed actual-time statistics evaluation device utilizing system studying can hugely enhance statistics analysis techniques by means of integrating relevant data and structured analyses of the identical. this will include strategies inclusive of natural language processing and clustering algorithms to perceive trends, sentiment evaluation to understand customer feedback, and predictive analytics to better perceive areas for improvement. Moreover, the realtime statistics analysis system can be further enhanced by means of incorporating AI to make extra correct predictions, as well as reinforcement studying to evolve present models as new statistics is integrated. this would allow the system to learn more efficaciously and modify its algorithms to continually enhance its overall performance. Universal, by using machine studying to develop an advanced actual-time statistics evaluation system, extra correct and comprehensive insights can be generated from information, and groups can use this facts to better tell their choice making. moreover, temporal records evaluation can be advanced with faster response times as a result of the device being able to process records

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application of K-Nearest Neighbor Algorithm Based on Orthogonal Wavelet Feature Extraction In Fault Diagnosis 2021

Application of K-Nearest Neighbor Algorithm Based on Orthogo...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Weipeng Li Yan Cao Jie Hu Lijuan Li Xi'an Technological University China and Nanyang Institute of Technology China Xi'an Technological University China Xi'an Technological University China and Norinco Group Test and Measuring Academy China

ISBN: (纸本)9781450385046

K-Nearest Neighbor (KNN) learning, a commonly used supervised learning method, finds the k closest training samples in the training set based on distance measurements of a given test sample. The learning of these k "neighboring" training samples reveals the intrinsic nature and laws of the data, and thus distinguishes the operating state of the machine. In this paper, an Orthogonal Wavelet Transform K-Nearest Neighbor (OWTKNN) diagnosis method has been proposed to extract the peak-to-peak values of each detailed signal as training samples using the Orthogonal Wavelet Transform (OWT) method and classify them by the KNN method. The classification results of the data obtained through rolling bearing tests show that the method can handle complex mechanical vibration signals in a more effective way than KNN without extracted eigenvalues, as it significantly improves the classification effects of unknown fault data, shortening classification time, and improving intelligent diagnosis efficiency.

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application of AI techniques for Face Mask Detection

Application of AI techniques for Face Mask Detection

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Metrey Arora Yogesh Arora Mamta Arora Computer Science Department Manav Rachna University Faridabad Haryana

Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we’ll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine.

关键词： COVID-19 Additives Gears Neural networks Software algorithms Detectors Regulation

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A New Approach using machine learning and Data Fusion Techniques for Solving Hard Combinatorial Optimization Problems

A New Approach using Machine Learning and Data Fusion Techni...

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3rd international conference on Information and Communication Technologies, vol.4

作者： Mahmoud Zennaki Ahmed Ech-cherif Computer Science Department Science Faculty USTO-MB Oran Algeria

We investigate the possibility of using kernel clustering and data fusion techniques for solving hard combinatorial optimization problems. The proposed general paradigm aims at incorporating unsupervised kernel methods into population-based heuristics, which rely on spatial fusion of solutions, in order to learn the solution clusters from the search history. This form of extracted knowledge guides the heuristic to detect automatically promising regions of solutions. The proposed algorithm derived from this paradigm is an extension of the classical Scatter Search and can automatically learn during the search process by exploiting the history of solutions found. Preliminary results, with an application to the well-known vehicle routing problem (VRP) show the great interest of such paradigm and can effectively find near-optimal solutions for large problem instances.

关键词： machine learning Kernel clustering Reactive Search Data fusion Unsupervised Support Vector machine Scatter search Vocabulary building

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