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Expression of Concern for: Exploring the Potential Benefits of machine learning for Optimizing Real-Time data Analysis

Expression of Concern for: Exploring the Potential Benefits ...

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

作者： Ravindra Kumar Nitin N. Sakhare N. Thangarasu K.K. Senthilkumar Pawan S. Wawage A Rengarajan Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This studies paper examines the capability blessings of implementing device getting to know algorithms to optimize real-time information evaluation. The subjects mentioned consist of techniques for predicting traits in actual-time information, the ability of gadget studying algorithms to discover anomalies and outliers in datasets, and how using records mining and herbal language processing (NLP) can improve the accuracy of real-time facts insights. It also evaluates the demanding situations that can arise while applying machine gaining knowledge of algorithms to actual-time statistics, along with scalability problems and overfitting of records. In end, the authors argue that system studying has notable potential to enhance the accuracy and timing of actual-time facts analysis, and that they provide steerage on choosing and enforcing appropriate gadget getting to know algorithms.

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Study on the Type of Campus One Card Student Consumption Based on Clustering Algorithm

Study on the Type of Campus One Card Student Consumption Bas...

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2017 3rd international conference on Future Mechatronics and Automation (ICMA 2017)

作者： Yantao Lan Jinshuai Qu Meina Zhao University Key Laboratory of Wireless Sensor Networks in Yunnan Province Yunnan University of Nationalities

With the rapid development of information technology, people are facing more and more data problems. The world of big data has quietly come to the world. data analysis and processing has become one of the most important research topics at present. In the process of data research, the processing method of large data is particularly important, especially the data mining technology has become the most important. In the processing of large data, clustering analysis is an indispensable means. It is widely used in many fields, such as medical diagnosis, image processing, pattern recognition, data analysis, data mining, decision support and marketing. Because the clustering algorithm is relatively simple, easy to realize and has good application effect in practical application, it has also attracted extensive attention of many experts and scholars. In this paper, based on the basic principle and application of clustering algorithm under the background of big data on the previous experience, the students of Yunnan Minzu University consumption expenditure data, and cluster analysis was performed on the data using Matlab software to analyze the consumption level of different types of students and what the students the consumption level.

关键词： Large data data mining Clustering algorithm Matlab Cluster analysis

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Graph-Theoretic Measures for Supporting Diagnosis and Identification of Biomarkers for Borderline Personality Disorder

Graph-Theoretic Measures for Supporting Diagnosis and Identi...

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data, Decision and Systems (ICDDS), IEEE international conference on

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

Borderline Personality Disorder (BPD) is a mental health disorder which renders an individual a victim of severe mood swings, impulsive behaviour, self-destructive behaviour and a distorted self-image. The diagnosis of BPD is usually conducted based on subjective assessments. However with the growing developments in neuroimaging, computational techniques can be developed leading to faster and more accurate diagnosis. In our study, we utilize neuroimaging techniques, specifically functional Magnetic Resonance Imaging (fMRI), to explore how different regions of the brain communicate. We employ graph theoretical methods to model this intricate neural data with the aim of objective diagnosis of the disorder and identifying neurological markers that could improve the diagnostic process for BPD. To achieve this, we make use of publicly available, annotated task-fMRI datasets designed to differentiate between individuals with BPD and healthy controls (HC). Through rigorous statistical analysis, correlation studies, and the application of graph-based metrics, we aim to not only identify whether an individual is suffering from the disorder or not, but also uncover specific brain regions that exhibit distinct activity patterns in individuals with BPD compared to HC. This research seeks to deepen our comprehension of BPD and potentially enhance diagnostic procedures by acting as an aid to health professionals.

关键词： Neuroimaging Measurement Statistical analysis Mood Mental health machine learning Functional magnetic resonance imaging Graph theory data models Object recognition

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Tracked-Vehicle Retrieval by Natural Language Descriptions With Domain Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions W...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Vuong Ai Nguyen Thong Duy-Minh Nguyen Nhat Minh Chung Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.

关键词： Training Representation learning Visualization Adaptive systems Uncertainty Computational modeling Urban areas

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A machine learning Approach for Detection Plant Disease: Taking Orchid as Example 2019

A Machine Learning Approach for Detection Plant Disease: Tak...

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Proceedings of the 3rd international conference on Vision, Image and Signal Processing

作者： Li-Hua Li Yu-Sheng Chu Jui-Yuan Chu Shian-Hau Guo Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9781450376259

The export value of flower production is very high in many countries. Among these export flowers, orchids are considered one of the most valuable flowers. In order to increase orchid production, it is important to take closely care of orchid in the cultivation period. Due to the plant diseases may cause the economic losses in agricultural industry, the daily inspection and early recognition of plant diseases are *** and prevention of plant diseases are a worldwide agricultural problem. Many researchers have proposed biocontrol or IT technology to handle plant disease problems. Some researchers applied image recognition to find out leaf problems such as banana leaves, alfalfa leaves, and citrus leaves. However, these researches all focus on trees or fruits in the lab. They did not provide the long-distance leaf identification for orchid flowers. To help flower farmers and to enhance the quality of production, this research has proposed a machine learning method to capture the image of orchid leaf and to identify the leaf *** research analyzes the leaf image with feature space, i.e., HSI, RGB, and grayscale. We use histogram to analysis the leaf color and we analyze the green color threshold so that the image can be classified into various color zones. Based on the generated threshold, this research is able to segment the leaf image into healthy area and unhealthy area. Finally, we use the Artificial Neural Network (ANN) and Deep learning ANN to learn the image patterns of orchid leaf. Our proposed method is then applied to identify the orchid leaves and to determine whether the orchid is healthy or sick. With our proposed model, the accuracy of recognizing the leaf disease can achieve 100% for training data and 90% for testing data. This research enables flower farmers to recognize the orchid disease and can prevent the disease in early stage. As a result, the farmer can take better care to the orchid plants and enhance the cultivation of orc

关键词： machine learning Orchid Image Processing Neural Network Plant Disease Deep learning

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Frequency Band Power-based EEG Channel Selection for Emotion recognition

Frequency Band Power-based EEG Channel Selection for Emotion...

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Advancement in Technology (ICONAT), international conference for

作者： Priyam Kar Jupitara Hazarika Electronics and Instrumentation Engineering National Institute of Technology Silchar Silchar India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Effective management of human emotion is of vital importance in maintaining general health and well-being. Consequently, with the growing frequency of human-machine interaction in everyday life, the presence of a computer system capable of adapting to human emotions could be of immense importance. Furthermore, an emotion-adaptive Brain-Computer Interface (BCI) system has the capacity to not only improve the healthcare system but also boost the overall quality of life for individuals. Currently, most emotion-adaptive Brain-Computer Interface (BCI) systems primarily rely on Electroencephalogram (EEG) components due to their non-invasive nature and greater temporal resolution. However, most of the recent efforts in the area of EEG-based emotion detection are primarily focused on improving the effectiveness of the classifier for data collected from a substantially large number of EEG channels. Nevertheless, choosing a smaller set of EEG channels that are pertinent to different emotional states can result in a more resilient and less computationally demanding emotion-adaptive BCI system. Thus, in this work, we have introduced a channel selection technique based on EEG band power to classify positive and negative emotions. To assess the effectiveness of the proposed technique, we used a publicly available dataset and conducted a comparison with the approach that does not include channel selection and also with state-of-the-art literature. The results showed that the suggested method outperformed the state-of-the-art approaches.

关键词： Deep learning Human computer interaction Emotion recognition Correlation Neural networks Medical services Electroencephalography Brain-computer interfaces Complexity theory Character recognition

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Research on Seismic Signal Classification and recognition Based on EEMD and CNN

Research on Seismic Signal Classification and Recognition Ba...

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IEEE international conference on Electronics and Communication Engineering (ICECE)

作者： Bingjun Li Hanming Huang Tingting Wang Mengqi Wang Pengfei Wang College of Computer Science and Information Engineering Guangxi Normal University Guilin China Institute of Geophysics China Earthquake Administration Beijing China

ISBN: (数字)9781728192581

ISBN: (纸本)9781728192598

The accurate classification of seismic signals is of great significance for updating clear earthquake catalog, early-warning the arrival of strong earthquake, and further researches in the field of seismology. This paper first preprocesses the seismic signal data of two types of events: earthquakes and explosions, and then uses the ensemble empirical mode decomposition (EEMD) method to decompose these preprocessed seismic data. A set of intrinsic mode functions being distributed from high-frequency to low-frequency are obtained from each seismic waveform, and then corresponding Hilbert spectrum is obtained by applying Hilbert transform to each of these intrinsic mode functions (IMFs). The image of Hilbert spectrum is scaled to a suitable and comparable size, by repeated trial-and-error experiments, to the size of 32*32 pixels, and then the 32*32 pixels gray-scale image is fed to a convolutional neural network(CNN) to classify the types of seismic signals. In this paper, 1674 waveforms of 54 earthquake events and 1509 waveforms of 63 explosion events are processed as above procedures. By five-fold cross-validation, 80% of the data is used as the training set, remaining 20% data as testing set. The highest classification accuracy rate of testing set is 94.98% with the average accuracy rate 94.41%,this results are significant higher than those of multi-layer perceptron(MLP) and Support Vector machine(SVM), which implying stronger classification capability of the scheme unifying EEMD and CNN proposed in this paper.

关键词： Training Earthquakes Seismology Support vector machine classification pattern classification Explosions Testing

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Decision Fusion in Cooperative Adaptive Systems

Decision Fusion in Cooperative Adaptive Systems

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The 8th international conference on Computer Supported Cooperative Work in Design(第八届计算机支持的协同工作设计国际会议)(CSCWD2004)

作者： Mohamed Kamel Pattern Analysis and Machine Intelligence Lab.University of Waterloo Waterloo OntarioCanadaN2L 3G1

Research on cooperative, adaptive intelligentsystems, involves studying, developing and evaluatingarchitectures and methods to solve complex problemsusing adaptive and cooperative systems. These systemsmay range from simple software modules (such as aclustering or a classification algorithm) to physicalsystems (such as autonomous robots, machines orsensors).The main characteristic of these systems is that theyare adaptive and cooperative. By adaptive, it is meantthat the systems have a learning ability that makes themadjust their behaviour or performance to cope withchanging situations. The systems are willing tocooperate together to solve complex problems or toachieve common *** pattern recognition, there are notable contributionson the use of multiple classifiers. The most dominantdecomposition model used is an ensemble of classifiers(identical structures) that are trained differently. Mostof the innovations are in the combining methods. Thereare weighting and voting approaches, probabilisticapproaches and approximate and fuzzy logicapproaches. In the area of sensor fusion, there havebeen some interesting ideas for fusing the data anddecisions of the sensors. However, most of thesecombining schemes are usually applied as a postprocessing *** this work are concerned with investigatingarchitectures and methods of aggregating decisions in amulti-classifier or multi-agent environment. Newarchitectures that allow active cooperation will bedeveloped. The classifiers (or agents) have to knowsome knowledge about others in the system. Differentforms of cooperation will be reported. In order for thesearchitectures to allow for dynamic decision fusion, theaggregation procedures have to have the flexibility toadapt to changes in the input and output and adjust toimprove on the final output. Changes will be learned bymeans of extracting features using feature *** of these architectures to problems inclassification of data, distributed data mining a

关键词： Adaptive systems Intelligent sensors Sensor fusion data mining Feature extraction Computer vision Intelligent systems Intelligent robots machine intelligence Computer architecture

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Smart Crop Cultivation: Harnessing machine and Deep learning for Informed Agricultural Practices

Smart Crop Cultivation: Harnessing Machine and Deep Learning...

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Smit Meghani Jay Vele Kunal Madrecha Abhijeet Salunke Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Amongst various occupations in India, farming is the most common. It boosts the Indian economy significantly and provides Indians with a wide range of job opportunities. The ideal crop for each type of soil is no longer chosen by farmers. This has an immediate effect on agricultural yields. It is causing serious financial losses for the farmers in the area. Cultivating a certain crop on a particular type of soil now involves several factors to take into account. The optimal crop to plant is determined by taking into account a number of soil specifications. This novel opinion mining method will consider the following parameters: temperature, air humidity, soil pH, nitrogen, potassium, phosphorus, and annual rainfall. The model aids in determining the kind of crop that has to be planted by taking into account the data collected from prior years. In order to support the mission, various algorithms are analyzed and evaluated on multiple fronts, including comparative research and analysis. In addition, the ideal fertilizer will be recommended depending on crop-related environmental factors, temperature, pH level, and soil moisture content.

关键词： Temperature dependence Sentiment analysis Temperature Rain Soil moisture Crops Phosphorus

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Optimizing Potato Disease Detection in Pakistan with machine learning: A Comparative Analysis

Optimizing Potato Disease Detection in Pakistan with Machine...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), international conference on

作者： Irfan Ahmed Manahal Khalil Safoora Ghulam Jabar Mahmood Khadija Batool Nibtal Khalid Pakiza Arshad Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity.

关键词： Support vector machines Productivity Adaptation models Accuracy Biological system modeling Computational modeling Crops Food security Random forests Classification tree analysis

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