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Using Big data mining Algorithm to Improve the Accuracy of Risk Assessment of Key Operating Vehicles

Using Big Data Mining Algorithm to Improve the Accuracy of R...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Xin Huang Yunpeng Wu Xin Zhao Jiuchuan Zhang Jie Li China Transport Telecommunications & Information Center Beijing China China Transport InfoJet Technologies Co. Ltd Beijing China Transportation Bureau of Xinjiang Production and Construction Corps Wulumuqi Xinjiang China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

Traditional risk assessment methods face challenges when dealing with massive data and complex operating environment. Therefore, this paper proposes a hybrid mining algorithm-Integrated Risk Decision Tree (IrdT) which integrates decision tree and random forest. IrdT algorithm combines the intuitive interpretation of decision tree and the integrated learning advantages of random forest, and improves the accuracy and stability of risk assessment through the training and integration of multiple decision tree sub-models. The experimental results show that the accuracy of IrdT algorithm on the test set reaches 90.40%, which is significantly higher than that of logistic regression model (76.80%) and support vector machine (81.20%). In addition, IrdT algorithm shows a fast response speed when dealing with real-time data, which verifies its effectiveness in complex and multi-dimensional risk assessment tasks. The risk assessment model based on IrdT algorithm constructed in this paper not only improves the accuracy of risk assessment of key operating vehicles, but also provides detailed risk classification and quantitative assessment methods, which provides strong support for the safety management of transportation industry. The research results show that big data mining algorithm has important application value in risk assessment of key operating vehicles.

关键词： Support vector machines Accuracy Transportation industry Safety management Big data Prediction algorithms Classification algorithms Risk management data mining Decision trees

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Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology

Change Detection Based on the Combination of Improved SegNet...

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international conference on Image, Vision and Computing (ICIVC)

作者： Bin Zhu Hongmin Gao Xin Wang Mengxi Xu Xiaobin Zhu College of Computer and Information Hohai University Nanjing China School of Computer Engineering Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9781538649923

Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F 1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and image morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F 1 index increased further by 1.4% on the basis of 8.6%.

关键词： Buildings Remote sensing Convolution Training machine learning Neural networks Morphology

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Coarse-To-Fine Key Frame Selection for Dynamic Facial Expression recognition

Coarse-To-Fine Key Frame Selection for Dynamic Facial Expres...

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Networking Systems of AI (INSAI), international conference on

作者： Shaoqi Yan Yan Wang Xinji Mai Qing Zhao Boyang Wang Wei He Wenqiang Zhang Academy for Engineering & Technology Fudan University Shanghai China Academy for Engineering & Technology School of Computer Science Fudan University Shanghai China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

In studies of in-the-wild Dynamic Facial Expression recognition (DFER), most studies have focused on exploring spatial-temporal features, but neglects expression peak frame (key frame) in video sequence, which is crucial for accurate expression recognition. These key frames, capturing the most intense moments of an expression, are essential for understanding the full context and nuances of facial expressions in a video. In this paper, we propose a dynamic facial expression recognition method called C2F-KFS, which extracts key frames from video sequences by extracting features from coarse to fine, in order to improve the robustness of classification results. We designed the coarse-level spatial key frames extraction (CSKFE) module to extract spatial features from different facial regions. In addition, the fine-level temporal key frames extraction (FTKFE) module to extract feature from temporal perspective and make weighted comparison, in order to select key frames. By extensive experimental on two popular in-the-wild datasets, DFEW and FERV39k, the results and visualization show that the C2F-KFS method achieves state-of-the-arts DFER. Meanwhile, we have conducted ablation experiments to prove the effectiveness of C2FKFS method.

关键词： Representation learning Visualization Accuracy Face recognition Video sequences Feature extraction Robustness data mining Artificial intelligence

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Length of Stay Prediction for Diabetes Mellitus Patients on Indonesia Health Insurance data using Logistic Regression and Process mining

Length of Stay Prediction for Diabetes Mellitus Patients on ...

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Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Fitri Eka Cahyanti Angelina Prima Kurniati Amirah Alharbi Adiwijaya School of Computing Telkom University Bandung Indonesia Computer Science Department Umm Al-Qura University Makkah Kingdom of Saudi Arabia

Diabetes Mellitus has a substantial impact on Indonesians, affecting 88.51% of those aged 44-94, as revealed by the Indonesia Health Insurance program. The urgency of this health concern is underscored by the international Diabetes Federations alarming statistic: one person succumbs to Diabetes Mellitus every five seconds, demanding immediate attention to diagnosis and risk reduction. This study focuses on predicting the Length of Stay (LOS) for Diabetes Mellitus patients, utilizing Logistic Regression and various machine learning models, particularly Random Forest. Concurrently, Process mining, encompassing Process Discovery and Conformance Checking, elevates model quality. Key factors influencing patient LOS, including the healthcare facilitys location, patient age, and arrival time, are unveiled. Logistic Regression exhibits commendable accuracy at 0.76 with rapid prediction times of 0.00082 seconds, while Random Forest excels with an impressive accuracy of 0.88 and a prediction time of 0.07 seconds. Specific medical procedures, such as those involving 'Diabetes and mild Nutritional/Metabolic Disorders,' reveal prolonged sojourn times, averaging 41.2 days. Best conformance-checking results emerge with a dependency threshold of 0.6 and a loop-two threshold of 32, yielding a Precision of 0.97, Trace Fitness of 0.66, and Generalization of 0.60. The finding of this study underscores the pivotal role of model selection and class distribution in LOS prediction. Despite Random Forest superior performance, Logistic Regression demonstrates notable accuracy and efficiency. Analysis of sojourn times underscores the importance of efficient healthcare services, particularly in cases involving 'Diabetes and Mild Nutritional/Metabolic Disorders.' Adjusting threshold values significantly impacts model accuracy, with lower threshold values potentially leading to overfitting.

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Application of RFID and IoT technology into specimen logistic system in the healthcare sector

Application of RFID and IoT technology into specimen logisti...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Mya Myet Thwe Chit Wattanasak Srisiri Atchasai Siritantikorn Narisorn Kongruttanachok Watit Benjapolakul Artificial Intelligence Machine Learning and Smart Grid Technology Research Unit Chulalongkorn University Bangkok Thailand Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781665408639

The invention and innovation of RFID technology changed the world and many sectors (such as logistics, railways, healthcare, and so on) are now deployed with RFID technology instead of using barcode systems. With the numerous advantages, Radio Frequency Identification (RFID) got many expectations in the healthcare sector. The main objective of this research work is to implement the RFID technology in Specimen collection in the healthcare sector and the IoT (Internet of Things) network supports the transaction while the specimen test box is being delivered. The system uses a Sparkfun RFID reader to read/write patient information to the Gen2 RFID tag, which is attached to the test tube collected from the patients. When the test box is delivered to another laboratory, we develop an IoT network to know the box’s temperature, humidity, and GPS location instantly, with the help of an NB-IoT shield. The major advantage of the combination between IoT technology and RFID is that the management of test box overall condition becomes much easier. To summarize, this method is highly competent in identifying the location of medical devices in real-time and reduces the time-consuming of data logging than the barcode system.

关键词： Technological innovation Medical devices Medical services Real-time systems Rail transportation Internet of Things Object recognition

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The Role of Comparative Opinions in Shaping Consumer Choices

The Role of Comparative Opinions in Shaping Consumer Choices

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Informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Akerke B. Togissova Aislu B. Kassekeyeva Department of Information Systems L.N. Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

With the growing number of social and online sales platforms, the comments and reviews posted on these platforms play a significant role in choosing products and services. Nowadays, consumers actively search for and read reviews left by other customers on online shopping sites before purchasing a particular product or service. And product manufacturers receive valuable feedback on the quality of their goods and services. Therefore, it is very important to take your place in the market and maintain it in a period when technologies are developing and rapidly evolving every day. Organizations do not need to conduct typical surveys to collect public opinion on their products and services, as this information is now available in open sources. To extract valuable information from customer reviews, opinion and sentiment analysis methods based on natural language processing are used. Opinions are divided into two types: non-comparative (regular) and comparative. Using advanced Natural Language Processing methods and machine and deep learning models, it is possible to automate the collection and analysis of comparative opinions, allowing businesses and researchers to make decisions based on comparable data. This paper provides a review of comparative opinion mining and sentiment analysis in the Kazakh language. Due to the limited availability of articles specifically addressing comparative opinion mining in Kazakh, we broadened our review to include related research in sentiment analysis overall. Additionally, the article discusses the structure of comparative sentences in both English and Kazakh, providing insights into linguistic frameworks.

关键词： Surveys Deep learning Sentiment analysis Reviews Organizations Linguistics data models Electronic commerce data mining Informatics

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Automatic recognition and Classification of Granite Tiles Using Convolutional Neural Networks (CNN) 19

Automatic Recognition and Classification of Granite Tiles Us...

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Proceedings of the 3rd international conference on Advances in Artificial Intelligence

作者： Muhammad Ather Babar Khan Zhijie Wang Guangxiao Song Iqbal Hussain College of Information Science & Technology Donghua University Shanghai China Department of Electrical Engineering Usman Institute of Technology Karachi Pakistan College of Information Science & Technology Donghua University Shanghai China

ISBN: (纸本)9781450372534

In recent years, the manual recognition and classification of natural stones have become a multifaceted challenge due to the similar patterns and visual appearance. Therefore in the current study, a robust and more effective system has been developed for an automatic classification of natural stones i.e. granite tiles using the Convolutional Neural Networks (CNNs). This approach is based on fine-tuning pre-trained networks such as AlexNet and VGGNet. The techniques of data augmentation such as reflection and rotation are incorporated to reduce the probability of overfitting. Efforts have been made to distinguish the performance of training from scratch and fine-tuning pre-trained models. It is observed that the classification, based on CNNs incorporating color and texture both at the same time, is highly reliable, effective and better than the other conventional methods using visual features separately. The research findings also demonstrated that transfer learning based on fine-tuning a network produced better results in accuracy for the classification task of granite tiles.

关键词： pattern recognition convolutional neural networks transfer learning granite tile

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Advanced Feature Extraction Techniques for machine learning-Based Fault Analysis in Power Grids

Advanced Feature Extraction Techniques for Machine Learning-...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Pratham Tatraiya Himanshu Priyadarshi Manipal University Jaipur Jaipur India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

As the infrastructure develops and more elements are added in the daily livelihood of humankind, dependable electrical power distribution is critical for keeping society and economies running smoothly. Modern power grids are complex, with wide networks and diverse components, requiring advanced problem detection and management systems for its upkeep. The growing demand of reliable power supply, as well as the necessity for long-term power infrastructure, emphasize the importance of reliable fault detection systems. This work aims to improve the resilience and reliability of electricity grids by using machine learning (ML) techniques to detect and analyze faults in power grids, using extensive feature extraction. The original base dataset, generated on an IEEE 6-bus grid system for consistent benchmarking, of three phase voltages and currents will be transformed into a dataset inclusive of time and other derivations, namely, voltage and current unbalance factor, volt-age and current harmonic distortion and the root-mean-square current and voltages. The results show a massive improvement in accuracy and related metrics between the base dataset, hovering between 34%-45% for various ML techniques to the improved dataset, yielding accuracy in the range of 87% -92% for respective techniques.

关键词： Analytical models Accuracy Computational modeling Sensor phenomena and characterization Feature extraction Power grids Sensor systems Reliability data mining Load modeling

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Automated Medical Recommendation System using machine learning Techniques & Natural Language Processing

Automated Medical Recommendation System using Machine Learni...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Likith V P M. Farida Begam Magadum Neeraj Shashikant Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (数字)9781665474979

ISBN: (纸本)9781665474986

The suggested work in this paper relates to the automation in the medical field in terms of how the diagnosis of irregularities has been going on in the industry and what the future in the said field looks like with the advancing technology. With the advent of application of machine learning approaches, we have observed a significant rise in the performance of detection and prediction with each passing year. With the current norms being that a patient is to perform the necessary tests and wait for a significant period of time to gain a consultation with an esteemed doctor. The methodologies used by other authors from the research papers that we gathered were either using image corpus or using machine learning techniques to detect and identify cancer, while others have used Natural Language Processing techniques to extract information from handwritten or printed texts and have developed models based on it. We have addressed the problem with a unified approach that combines both machine learning and Natural Language Processing techniques in tandem to extract data and correlations which may have been ignored by the other models. The image corpus and handwritten notes would together give better insights and help improve the accuracy of the model. Bearing in mind the objective of relieving novice doctors from feeling perplexed, in addition to answering questions that patients might have, our model aims to allow such medical professionals make better informed decisions in their diagnosis and prognosis procedures. The chatbot, to serve this purpose, will be based on Bidirectional Encoder Representations from Transformers (BERT), which has demonstrated ground-breaking performance in tasks involving natural language understanding, including sentiment analysis, semantic role labelling, sentence classification, and the disambiguation of polysemous words. We aim to not only successfully diagnose and provide prognosis for cancer, but also to detect the chances of remission. Our

关键词： Object oriented modeling Semantics machine learning Medical services Feature extraction Transformers data mining

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Training Linear Discriminant Analysis in Linear Time

Training Linear Discriminant Analysis in Linear Time

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IEEE 24th international conference on data Engineering (ICDE 2008), vol.1

作者： Deng Cai Xiaofei He Jiawei Han Department of Computer Science University of Illinois Urbana IL USA Yahoo Inc. Burbank CA USA

ISBN: (纸本)9781424418367;1424418364

Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices eigen-decomposition which can be computationally expensive both in time and memory. Specifically, LDA has O(mnt + t{sup}3) time complexity and requires O(mn + mt + nt) memory, where m is the number of samples, n is the number of features and t = min(m,n). When both m and n are large, it is infeasible to apply LDA. In this paper, we propose a novel algorithm for discriminant analysis, called Spectral Regression Discriminant Analysis (SrdA). By using spectral graph analysis, SrdA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques. Our theoretical analysis shows that SrdA can be computed with O(ms) time and O(ms) memory, where s(≤ n) is the average number of non-zero features in each sample. Extensive experimental results on four real world data sets demonstrate the effectiveness and efficiency of our algorithm.

关键词： laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Linear discriminant analysis discriminant analysis Information Processing information retrieval Memory Memory dataset pattern recognition Theoretical analysis

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