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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是4721-4730 订阅

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SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems 4th

SHAPRFs: SHapley Additive eXplanation-Based Random Forests A...

引用

4th international conference on Frontiers in computing and Systems, COMSYS 2023

作者： Jain, Nishant Shukla, Shipra Department of Computer Science and Engineering School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9789819726103

"Tree-based ensemble algorithms" (TEAs) are extensively employed for classification and regression problems. However, existing TEAs lag behind the trade-off between TEA interpretability and achieving cutting-edge accuracy when applied to real-time applications. This paper proposes a new TEA for bridging the gap between real-time applications and the trade-off between TEA interpretability and cutting-edge accuracy. The proposed algorithm is based on the SHapley Additive exPlanation (SHAP) value, which is one of the most advanced cooperative game solutions for quantifying each feature’s contribution to the model prediction. As a result, we call our proposed algorithm the SHapley additive explanation-based Random Forest algorithm (SHAPRFs). The proposed SHAPRFs compute the dominion of feature alliances using the SHAP value to determine the significance of each feature in the dataset. Following suit, the SHAPRFs divide the features into two subsets based on the importance of each feature. Then it employs the Roulette wheel selection algorithm to generate decision trees (DTs) in the forest. Five benchmark datasets are used to test the SHAPRFs algorithm. The experimental results show that the proposed SHAPRFs outperform three well-known and benchmark classifiers by a significant margin, namely, Breiman random forest (BRF), deep forest, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Filtering Influential Features for Adolescent Positive Mental Health

Filtering Influential Features for Adolescent Positive Menta...

引用

international conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Cao Yi Cai Shimin Zhou Tao Complex Lab University of Electronic Science and Technology of China Chengdu China Big Data Research Center University of Electronic Science and Technology of China Chengdu China

Adolescents' positive mental health is deeply associated with their growth. Identifying the factors that contribute to the positive mental health of junior and senior high school students is crucial for supporting youth development in the long run. Leveraging questionnaire data, this paper utilizes machine learning algorithms and fusion methods for feature selection to examine the factors that influence the onset of adolescent positive mental health. The influential features are ranked by importance. Then, machine learning algorithms in a heuristic form are employed to assess the predictive power of the fused features. The best f1-score of the classifiers reaches 0.844. It illustrates the effectiveness of feature filtering and provides valuable references for educational administrators to potential interventions.

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IoT, Heterogeneous data processing, and AI Automation Synergy for Improved Efficiency and Maintenance: Revolutionizing Hospital Operations

IoT, Heterogeneous Data Processing, and AI Automation Synerg...

引用

IEEE international conference on Signal processing, Communications and computing (ICSPCC)

作者： Niu Muqing Liu Wenting Gao Xiaole Zhao Zhiyuan Wang Jingjuan First Affiliated Hospital Henan University of Science and Technology Luoyang China Business School Henan University of Science and Technology Luoyang China

Modern healthcare is going through a big change because of the way that new tools are proposed. This paper works at how the Internet of Things (IoT), heterogeneous data transfer and processing, and Artificial Intelligence (AI) robotics could completely change how hospitals work. IoT technology has become a significant tool because it makes it possible to watch and collect data in realtime from hospital properties like medical equipment, wearables for patients, and smart sensors. This steady flow of different kinds of data brings both possibilities and problems. In our study, we talk about how significant it is to have strong data transmission and processing systems that can handle different data sources well. Intelligent automation that is led by AI is at the heart of this change. AI systems can work with the huge amount of data created by IoT devices and use machine learning, predictive analytics, and cognitive computing to improve different parts of hospital management. This paper gives case studies and real-world examples of how IoT, diverse data processing, and AI automation have been used successfully in hospitals to show how these ideas can be used in the real world. IoT technology, diverse data processing, and AI automation are assembled to change the way hospitals work by making them more effective and easier to maintain. This study offers the significance of using these tools to keep up with the varying needs of current healthcare. As hospitals continue to change, this relationship will play an even bigger role in making sure that patients get superior care while making the best use of their resources.

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Using data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

引用

6th international conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Spark-KNN Parallel Pattern Recognition Technique for Substation Secondary Cable data

Spark-KNN Parallel Pattern Recognition Technique for Substat...

引用

Electrical Engineering and Control Science (IC2ECS), international conference on

作者： Weizhou Lu Haiying Huang Huan Luo Guangdong Power Grid Maoming Power Supply Bureau China

With the rapid development of smart grid, the substation secondary cable condition monitoring data is growing exponentially and gradually constitutes the secondary circuit condition monitoring big data. The traditional computing architecture can no longer meet the computing performance demand. Combining Spark big data processing technology and AliCloud E-MapReduce cloud computing platform, we propose a parallel pattern recognition method for substation secondary cable condition monitoring big data, aiming to improve the ability of the secondary cable online monitoring system to quickly batch analyze the alarm monitoring data that suddenly increase in a short period of time. Spark-KNN, a parallelized K-nearest neighbor classification algorithm based on Spark, is designed to realize parallel pattern recognition of massive secondary cable monitoring data. The experimental results show that the average performance of Spark-KNN is 3.17 times higher than that of Hadoop MapReduce implementation, which is more suitable for performing real-time processing tasks of secondary cable monitoring big data.

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Performance Evaluation of KNN & Decision Tree in Prediction of Heart Disease

Performance Evaluation of KNN & Decision Tree in Prediction ...

引用

Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Aniket Kumar Jobin Thomas Mathew Neha Gupta Vivek Malhotra Faculty of Computer Applications Manav Rachna International Institute of Research Studies Faridabad India

Heart disease has been the leading cause of death in recent decades. Every five minutes, one person dies unexpectedly from heart disease. Researchers from all over the world are assisting doctors in diagnosing heart problems. However, machine learning techniques can substantially minimize the number of tests required. The main objective of this paper is to predict the heart disease of a person well in advance to minimise the need for expensive treatment and medicines. The present papers have used some of the finest approaches of machine learning like decision tree and KNN classifier for prediction of heart diseases. These algorithms are quite useful in diagnosing cardiac illness without the use of machinery or labs, by figuring out the problem at a far lower cost. These algorithms are very widely utilized and helpful in other domains. The dataset was obtained from the Kaggle website and contain data of 303 patients and 76 attributes. The accuracy of the KNN along with Decision tree algorithms have been compared with existing work of researchers and found to be more in the detection of the heart disease. These algorithms certainly help in reducing the number of deaths which are happening all around the world.

关键词： Heart Performance evaluation Costs Machine learning algorithms Machine learning Medical services Prediction algorithms

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Hybrid ML Classifier for Loan Prediction System

Hybrid ML Classifier for Loan Prediction System

引用

Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： M N Kavitha S S Saranya E Dhinesh L Sabarish A Gokulkrishnan Department of Computer Science and Engineering Kongu Engineering College Erode Department of Computer Technology Kongu Engineering College Erode

Banks make the majority of their income from loans. A lot of individuals apply for loans, and it is difficult to choose the real candidate who will repay the loan. A lot of misunderstandings may occur when selecting the real applicant when the process is done manually. As a result, a loan prediction system based on machine learning is developed, in which the system will automatically identify the qualified candidates. This is beneficial to both the bank personnel and the applicant. The loan approval process will be greatly shortened. The loan data is predicted by using the hybrid model of Naive Bayes (NB) and Decision Tree (DT) algorithms. First, the dataset is given to the three classification algorithms– Support Vector Machine (SVM), NB and DT algorithms and the prediction is done with these three algorithms. The accuracy of each of these three is used to assess performance. The creation of the hybrid model increases accuracy. The dataset is given to NB for training and the prediction of NB is given to DT Algorithm for training. Test data are sent to the model for prediction after training. The model is evaluated, and the performance is measured in terms of different metrics form sklearn metrics. This prediction of loan range is useful for bank staff to give the loan amount accordingly. The NB algorithm checks for equality and independence of all the features in the dataset. In DT algorithm, the tree is constructed based on the information gain value. The attribute with high information gain value is placed as the root node and also the other nodes are constructed based on information gain value. The proposed hybrid model predicts - yes or no, and based on the prediction, whether the loan is to be sanctioned or denied for the applicant is specified.

关键词： Training Support vector machines Measurement Machine learning Predictive models Prediction algorithms data models

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Multi-source data Cooperative Analysis and Integrated Monitoring Technology

Multi-source Data Cooperative Analysis and Integrated Monito...

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Cloud computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Honghong Yao Zhao Hao Chinese Flight Test Establishment Xi’an China

Telemetry and monitoring are important means to ensure the safety of flight test. In order to solve the problem of multi-source data stream time synchronization and integrated monitoring under the multi-airplane cooperative flight test scenario, a multi-data stream time synchronization processing algorithm is designed, and a data and video correlation storage and synchronous playback scheme is proposed. Multidimensional integrated monitoring and online playback have been achieved by adopting the concept of componentization design. The system has been applied to the flight test mission of multi-aircraft cooperation, and the actual application results show that the multi-source data time synchronization algorithm is effective, which greatly improves monitoring experience, and effectively ensures the development of flight test program of the multi-aircraft cooperation.

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data Mining and It's Applications in Healthcare

Data Mining and It's Applications in Healthcare

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international conference on Contemporary computing and Informatics (IC3I)

作者： Priyanka Kumari Baljinder Kaur Manik Rakhra Arun Singh Anil Kumar Rawat Department of Computer Science and Engineering Lovely Professional University Phagwara India

Digitalization has resulted in accumulation of gigantic amount of data and that too at an alarming rate. processing such a large amount of data is the biggest challenge of the recent world. Extracting meaningful information and ensuring correctness and preciseness of data is the core of current requirements of digital world. The process involving preprocessing of raw data, extracting meaningful information and deducing conclusive evidence for decision making lead to the field named as data mining. data mining is a framework of patterns and rules aiming at extracting the relationship or hidden information from the enormous set of databases. The paper targets the healthcare sector and the role of data mining in it. It also presents the challenges pertaining to the healthcare industry. The current trends and the future scope have also been illustrated.

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Network Traffic Classification Techniques: A Survey

Network Traffic Classification Techniques: A Survey

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international conference on Image Information processing

作者： Krishan Kumar Mamta Punia Vandana Chandigarh University

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

By evaluating received data packets, network traffic categorization (NTC) identifies distinct categories of applications or traffic data. It is a significant technique in today's communication networks. data input, preprocessing, feature extraction, classification, and performance analysis are all processes in the network traffic classification process. The utility of learning approaches to categorize traffic over a network has been pushed by rapid improvements in machine learning. In the event of consistent datasets, inherent properties of internet networks cause uneven class distributions. This phenomenon is known as class imbalance, and it is gaining popularity in a variety of fields of study. Various network traffic classification and data balancing algorithms are discussed in this study.

关键词： Surveys Machine learning algorithms data preprocessing Intrusion detection Telecommunication traffic Machine learning Information processing

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