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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification 4

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Ananthi, K. Balakrishnan, M. Pandi, M. Senthil Madasamy, N. Dr.Mahalingam College of Engineering and Technology Artificial Intelligence &data Science Tamil Nadu Coimbatore642003 India Dr.Mahalingam College of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore642003 India

ISBN: (纸本)9781665474368

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. the tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. the problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. the experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library. © 2022 IEEE.

关键词： Support vector machines

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Mobile Banking Transaction Authentication using Deep learning 5

Mobile Banking Transaction Authentication using Deep Learnin...

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5th international conference on Information Technology for Education and Development, ITED 2022

作者： Oguntimilehin, A. Akukwe, M.L. Olatunji, K.A. Abiola, O.B. Adeyemo, O.A. Abiodun, I.A. Afe Babalola University Department of Computer Science Ado-Ekiti Nigeria

ISBN: (纸本)9781509064229

the use of smartphones is growing in tandem with the rapid advancement of mobile technologies. the most critical key to future mobile banking and market activation is a safe financial transaction. this study offers a safe authentication solution to strengthen the security of mobile banking applications using Convolutional Neural Network model to provide an embedded facial recognition model in mobile bank application. the System works with two computer vision models working together, the Firebase machine learning vision model to perform the face detection and preprocessing of the image, and the MobileFaceNet model to process, classify and transform into a data structure 'savable' by a database (an array of numbers). data were collected by taking four pictures of each user, recording key facial features such as the eyes, brows, nose, lips, and ears in real-time with a smartphone's front facing camera. the System provides a user-friendly interface that was developed using Java programming language and Dart programming language. After being tested in a variety of scenarios, the system achieved very encouraging authentication accuracy on real faces. It is a promising application that can reduce insecurity in banking sector. © 2022 IEEE.

关键词： Deep learning

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Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification 25

Soft Label and Discriminant Embedding Estimation for Semi-Su...

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25th international conference on pattern recognition (ICPR)

作者： Dornaika, F. Baradaaji, A. El Traboulsi, Y. Univ Basque Country UPV EHU San Sebastian Spain Basque Fdn Sci Ikerbasque Bilbao Spain

ISBN: (纸本)9781728188089

In recent times, graph-based semi-supervised learning proved to be a powerful paradigm for processing and mining large datasets. the main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. the scarcity of labeled data may affect the performance of the semi-learning. this paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. the proposed method simultaneously estimates a discriminant transformation and the unknown label by exploiting both labeled and unlabeled data. In addition, the unknowns of the learning model are estimated by integrating two types of graph-based smoothness constraints. the resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on six public image datasets. these experimental results show that the performance of the proposed method can be better than that of many state-of-the-art graph-based semi-supervised algorithms.

关键词： Graph-based embedding discriminant embedding soft labels semi-supervised learning pattern recognition image categorization

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A 2-stages feature selection framework for colon cancer classification using SVM 5

A 2-stages feature selection framework for colon cancer clas...

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5th international conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Touchanti, Kaouthar Ezzazi, Imad El Bekkali, Moulhime Maser, Said Laboratory of Artificial Intelligence Data Science and Emerging Systems Morocco

ISBN: (纸本)9781665495585

As the colon cancer gene expression dataset is of high dimension, many irrelevant, redundant and noisy features might be included which may cause unprecedented challenges for data mining and machine learning algorithms. In this paper, we have proposed a new feature selection based method for colon cancer classification. First, we have used the ReliefF filter technique to provide a ranking in terms of the discriminatory ability of each feature. Second, since ReliefF cannot handle feature redundancy as well as feature interaction, another step is performed to select the best subset of gene expression profiles from the available 2K subsets. the proposed method has efficiently reduced the dimensionality of the colon dataset and increased the classification accuracy. the results from the Colon Cancer Gene Expression data Set confirmed the effectiveness of the proposed method compared to advanced techniques. © 2022 IEEE.

关键词： data mining

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2022 5th international conference on Computational Intelligence and Networks, CINE 2022

2022 5th International Conference on Computational Intellige...

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5th international conference on Computational Intelligence and Networks, CINE 2022

ISBN: (纸本)9781665464659

the proceedings contain 29 papers. the topics discussed include: a distributed software agent-oriented traceability milieu for blockchain-enabled supply chain;distributed hash table with extensible remote procedure calls;an approach for word segmentation from a line segment in Odia text using quartiles;improving learner's comprehension using entailment-based question generation;breast cancer prediction using long short-term memory algorithm;multiple feature selection frameworks based on evolutionary computing and ensemble learning for software defect prediction;futuristic opportunities and challenges for cognitive tutoring systems;smart prediction of severity in vehicular crashes: a machine learning approach;continuous cloud user authentication by efficient facial recognition;diagnosing skin lesion using multi-modal analysis;Hindi document extractive summarization: neural method on a new data set;outlier and trend detection using approximate median and median absolute deviation;and a lightweight DoS and DDoS attack detection mechanism-based on deep learning.

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13th international conference on machine learning and data mining in pattern recognition, MLDM 2017

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13th international conference on machine learning and data mining in pattern recognition, MLDM 2017

ISBN: (纸本)9783319624150

the proceedings contain 31 papers. the special focus in this conference is on machine learning and data mining in pattern recognition. the topics include: An information retrieval approach for finding dependent subspaces of multiple views;predicting target events in industrial domains;importance of recommendation policy space in addressing click sparsity in personalized advertisement display;global flow and temporal-shape descriptors for human action recognition from 3D reconstruction data;reverse engineering gene regulatory networks using sampling and boosting techniques;detecting large concept extensions for conceptual analysis;qualitative and descriptive topic extraction from movie reviews using LDA;detecting relative anomaly;optimization for large-scale machine learning with distributed features and observations;a scalable and noise-resistant closed contiguous sequential patterns mining algorithm;sparse dynamic time warping;over-fitting in model selection with Gaussian process regression;machine learning-as-a-service and its application to medical informatics;anomaly detection from kepler satellite time-series data;prediction of insurance claim severity loss using regression models;a spectral clustering method for large-scale geostatistical datasets;vulnerability of deep reinforcement learning to policy induction attacks;mobile robot localization via machine learning;an analysis of the application of simplified silhouette to the evaluation of k-means clustering validity;summarization-guided greedy optimization of machine learning model;clustering aided support vector machines;mining player ranking dynamics in team sports and personalized visualization based upon wavelet transform for interactive software customization.

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Research on Task Offloading Based on Deep Reinforcement learning for Internet of Vehicles 5

Research on Task Offloading Based on Deep Reinforcement Lear...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Zeng, Yaoping Hu, Yanwei Yang, Ting School of Artificial Intelligence Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Mobile Edge Computing (MEC) is a promising technology that facilitates the computational offloading and resource allocation in the Internet of Vehicles (IoV) environment. When the mobile device is not capable enough to meet its own demands for data processing, the task will be offloaded to the MEC server, which can effectively relieve the network pressure, meet the multi-task computing requirements, and ensure the quality of service (QoS). Via multi-user and multi-MEC servers, this paper proposes the Q-learning task offloading strategy based on the improved deep reinforcement learning policy(IDRLP) to obtain an optimal strategy for task offloading and resource allocation. Simulation results suggest that the proposed algorithm compared with other benchmark schemes has better performance in terms of delay, energy consumption and system weighted cost, even with different tasks, users and data sizes. © 2022 ACM.

关键词： Reinforcement learning

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Metal and Metal Oxide Nanoparticle Image Analysis Using machine learning Algorithm 5th

Metal and Metal Oxide Nanoparticle Image Analysis Using Mach...

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5th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Bannigidad, Parashuram Potraj, Namita Gurubasavaraj, Prabhuodeyara Department of Computer Science Rani Channamma University Karnataka Belagavi India Department of Chemistry Rani Channamma University Karnataka Belagavi India

ISBN: (纸本)9783031283239

Nanomaterials are used in almost every field of engineering. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. the advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. the proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. the classification is done by using PNN and K-NN classifiers. the K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Synthesis (chemical)

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Sentiment Analysis of Tibetan Short Texts Based on Graph Neural Network with Keyphrases Integration

Sentiment Analysis of Tibetan Short Texts Based on Graph Neu...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Jing Zhang Hang Ren Jin Yang Nuo Qun Shengqiao Ni School of Information Science and Technology Tibet University Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region Lhasa China School of Information Science and Technology Tibet University Lhasa China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Sentiment analysis is a core task in the field of natural language processing. Despite considerable development over time, existing sentiment classification techniques for Tibetan texts still face challenges such as insufficient text comprehension and low recognition rates of emotional words. To address these issues, this paper proposes a sentiment analysis method for Tibetan short texts based on the fusion of keyphrases with graph neural networks. this method employs graph neural networks to model the textual structure and enhances sample representation by extracting keyphrases. It also utilizes an iterative heterogeneous sample alternating training strategy to further improve the model's understanding of textual semantics. Experimental results confirm that this method has improved both accuracy and F1 score by 1.7% compared to the baseline, demonstrating its effectiveness in the sentiment analysis of Tibetan short texts.

关键词： Training Sentiment analysis Emotion recognition Text recognition Face recognition Semantics machine learning Graph neural networks Robustness Iterative methods

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Applied Health Informatics for Diabetes Risk Prediction from Food Consumption Behavior of Rural Communities in Northern thailand with data Analytics 5th

Applied Health Informatics for Diabetes Risk Prediction from...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Nuankaew, Pratya Kadkasame, Apatcharaporn Sawasit, Kunthida Nasa-Ngium, Patchara Sararat, thapanapong Nuankaew, Wongpanya S. School of Information and Communication Technology University of Phayao Phayao56000 Thailand Faculty of Science and Technology Rajabhat Maha Sarakham University Maha Sarakham44000 Thailand

ISBN: (纸本)9789819747108

this research has three critical research objectives. the first objective is to study the context and risk of diabetes from the food consumption behavior of thai people in the northern region. the second objective is to develop a model for predicting diabetes risk from the food consumption behavior of thai people in the northern region. the last objective is to evaluate the efficiency of the model for predicting diabetes risk from the dietary behavior of thai people in the northern region. the population and sample in this research were 370 people in Chiang Muan District, Phayao Province, aged between 20 and 99 years. the research tools include an IOC-approved questionnaire and data mining analysis using machine learning and artificial intelligence tools known as CRISP-DM. Model performance was evaluated using data partitioning and testing known as split-validation methods and confusion matrix performance. the research results found that the context of rural people in northern thailand toward food consumption behavior was normal. Overall, the risk of people developing diabetes was at an acceptable level. the developed model has the highest level of accuracy with an accuracy value of 81.80%. It is given in Tables 11 and 12 that people have good food consumption habits. Moreover, the model for predicting the risk of diabetes in rural communities was found to have a high level of accuracy by comparing various techniques, as given in Table 4. this research, therefore, concludes that all objectives were achieved. the results and expectations from the research are to know the food consumption context of rural people in the northern region of thailand, which can be used to design and plan strategies to prevent the increase in people at risk of diabetes in thailand. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Applied informatics Diabetes risk prediction Food consumption behavior Health forecasting model Health informatics

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