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learning Different Separations in Branch and Cut: A Survey 4th

Learning Different Separations in Branch and Cut: A Survey

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Amar, Chaima Ayachi Bouanane, Khadra Aiadi, Oussama LINATI Lab Ouargla Algeria Univ Kasdi Merbah Ouargla Ouargla 30000 Algeria

ISBN: (纸本)9783031821554;9783031821561

Selecting cutting planes for solving Mixed-Integer Linear Programming (MILP) is a critical task as it directly impacts the efficiency and effectiveness of the optimization process. By carefully choosing a set of cuts, it becomes possible to significantly reduce the size of the search space, thereby enhancing the algorithm's ability to find optimal or near-optimal solutions within a reasonable computational time frame. In this paper, we examine recent advancements in utilizing machine learning (ML) techniques for the selection of cutting planes (separation) in MILP. Despite the existence of various types of separations, the task of selecting a set of cuts to augment the Linear Programming (LP) relaxation at a particular node of the Branch-and-Bound (B&B) tree remains a challenge, both in terms of formal methods and heuristic approaches. ML presents a promising direction for enhancing the cut selection process by leveraging data to identify promising cuts that expedite the resolution of MILP problems. Focusing on recent developments in research, we investigate common methodologies for data gathering, evaluation techniques, and ML model architectures. We review empirical findings in the literature to assess the extent of progress achieved and conclude by suggesting potential directions for future investigation. In conclusion, the integration of ML techniques holds great promise for advancing the field of MILP by improving the efficiency and effectiveness of cut selection processes, ultimately leading to better solutions for complex optimization problems

关键词： Combinatorial Optimization Mixed Linear Integer Programming MILP Cutting plane machine learning

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Novel Hybrid Feature Selection Method and Globalization Technique for Text Classification 4th

Novel Hybrid Feature Selection Method and Globalization Tech...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mekhzoumi, Dalila Boulkaboul, Sahar Amroun, Kamal CERIST Res Ctr Algiers Ben Aknoun Algeria Abderrahmane Mira Univ Bejaia Algeria

ISBN: (纸本)9783031821523;9783031821530

Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. this process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby undermining the performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, the simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy.

关键词： Features selection CHI-Squared machine learning Globalization Text classification Support Vector machines

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A comprehensive review on hate speech recognition utilizing natural language processing and machine learning 4th

A comprehensive review on hate speech recognition utilizing ...

引用

4th Annual international conference on data Science, machine learning and Blockchain Technology, AICDMB 2023

作者： Shilpashree, S. Ashoka, D.V. Department of CSE-JSSATEB Visvesveraya Technological University Belagavi India Department of ISE JSS Academy of Technical Education Visvesveraya Technological University Belagavi Bengaluru India

ISBN: (纸本)9781032426853

Many social media sites offer a low-cost mode of communication that makes it possible for anybody to immediately connect with countless individuals. Because of this, anybody can produce anything on these networks, and everyone who is intrigued by the imaterial can acquire it, which represents a revolutionary transformation in our community. On the other hand, it has resulted in the emergence of a number of problems, one of which is the propagation and distribution of materials that contain hateful rhetoric. the recognition and surveillance of inciting hatred are becoming an increasingly difficult societal problemas a whole, as well as for individuals, strategists, and scholars. In spite of attempts to leverage automated processes for identification, the capabilities of these methods remain a long way from being adequate, which consistently demands further study on the subject. this study presents a comprehensive assessment of the previous research that has been done in this area, with a particular interest in natural language processing (NLP) algorithms. Our goal is to gain an understanding of the prevalence of offensive speech in social media platforms found online, the most popular manifestations of hatred, as well as the impact obscurity has on hateful texts. © 2024 the Author(s).

关键词： machine learning

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Multidimensional Time Series Analysis for Anomaly pattern Detection and Interpretation 4

Multidimensional Time Series Analysis for Anomaly Pattern De...

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4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Deng, Ziling Kang, Jiahua Wang, Xiaoxiao University of Electronic Science and Technology of China Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350359589

the interpretability of multivariate time series anomaly detection is crucial for understanding the reasons behind anomalies, enhancing the usability and credibility of models, and ensuring successful real-world applications. Although there has been considerable research on the interpretability of time series, most are based on black-box models and have certain limitations in revealing the underlying patterns of anomalies. To address this issue, this paper focuses on identifying anomaly patterns in multivariate time series using a white-box machine learning approach, which overcomes the interpretability limitations of traditional methods in high-dimensional dynamic data processing. this approach innovatively enhances the application of Dynamic Time Warping (DTW) in higher dimensions to effectively capture anomaly patterns of any length, while identifying both short-term and long-term anomalies. Experiments on the Yahoo dataset demonstrate its high accuracy and reliability in anomaly pattern detection, particularly in explaining the reasons and impacts of identified anomaly patterns. this innovation marks a breakthrough in anomaly pattern interpretation, showing significant practicality and interpretability in real-world applications, and provides vital support for decision-making and predictive modeling in time series analysis. © 2024 IEEE.

关键词： Anomaly pattern Extraction data Interpretability Multidimensional Time Series pattern recognition

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

4th International Conference on Computational Methods in Sci...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

ISBN: (纸本)9781032911571

the proceedings contain 167 papers. the special focus in this conference is on Computational Methods in Science and Technology. the topics include: the proposed framework for hybrid multilevel optimized machine learning classification in apple leaf disease detection;estimation of sediment load in Mahanadi River, India using artificial neural networks;prediction of water discharge in Mahanadi River basin, India using artificial neural networks;online misinformation detection using deep learning on social media: A survey and analysis;deep insight of medical images segmentation and classification by deep learning;human intrusion detection by optimize dragon deep belief network;human activity recognition using residual deep network with extreme learning;enhancing lung cancer diagnosis: A comprehensive analysis of machine learning and deep learning approaches;advanced perspectives on big data security: Navigating challenges in cloud computing;improved segmentation and classification of breast cancer using mammogram images with residual network based deep learning;analysis of early detection and prediction of diabetic retinopathy by deep learning;mustard and wheat mildew disease classification using deep learning;machine and deep learning approaches for heart disease risk assessment;uncovering agile software frameworks;calculate the asphalt pavement’s binder activity level using machine learning techniques;empowering healthcare with Swin Transformer V2: Advancing pneumonia diagnosis through deep learning;object detection in videos using deep learning methods: A survey;analysis of machine learning and deep learning techniques for credit card fraud detection in class imbalanced datasets;enhancing data security through an integrated approach of multiple key cryptography and steganography in the wavelet domain.

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

4th International Conference on Computational Methods in Sci...

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4th international conference on Computational Methods in Science and Technology, ICCMST 2024

ISBN: (纸本)9781032911571

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Ensemble learning with Mixup Style for Ultrasound Image Classification 4th

Ensemble Learning with Mixup Style for Ultrasound Image Clas...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Alblwi, Abdalrahman Barner, Kenneth E. Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9783031821493;9783031821509

Early detection of breast cancer has become an essential medical procedure to reduce mortality rates in patients. Although various deep learning methods enhance cancer analysis and detection, including feature extraction techniques, transfer learning, image fusion, and data augmentation, challenges such as limited data and class imbalance impede progress in early cancer detection. this study introduces a new approach that employs a dual ensemble learning and MixUp data techniques to simultaneously (1) tackle unbalanced classes and (2) improve diversity in ultrasound images. the proposed training methodology involves various strategies, integrating original and augmented datasets to maintain model training and increase performance. through comprehensive evaluations using benchmark breast ultrasound images (BUSI), the proposed approach demonstrates a substantial improvement and feasibility in the multi-classification of breast cancer.

关键词： Ensemble learning Breast cancer Class imbalance, Multi-Classification MixUp

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Comparative Analysis on Prediction Model of Hate Speech Detection Based on Social Media data Using Artificial Intelligence 4

Comparative Analysis on Prediction Model of Hate Speech Dete...

引用

4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Kaware, Pravin D. Raut, Anjali B. Department of Computer Science & Engineering H.V.P.M's College of Engineering & Technology Amravati India

ISBN: (纸本)9798331530402

the exponential expansion of social media networking platforms has magnified the dissemination of hate speech, thereby requiring strong recognition systems to minimize its influence. the present work offers a comparative examination of many prediction models for the recognition of hate speech using social media data, employing Artificial Intelligence (AI) methodologies. the paper intends to assess the efficiency of several machine learning (ML) and Deep learning (DL) algorithms for accurately detecting hate speech. the various strategies are evaluated using known datasets and measures including accuracy, recall, precision, and Fl-score. In addition, useful insights on the most effective methods for identifying hate speech are presented. the findings suggest that deep learning approaches are exceptionally effective at catching the contextual nuances of hate speech. Al-based hate speech detection apps improve accuracy and efficiency. Such systems are critical for creating a safer and more inclusive online. © 2025 IEEE.

关键词： Deep learning

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Use of machine learning in Object Detection for Visually Impaired Person 4

Use of Machine Learning in Object Detection for Visually Imp...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Munia, Tahmina Haque Taher, Kazi Abu Bangladesh University of Professionals Dhaka Department of Information and Communication Technology Bangladesh

ISBN: (纸本)9798350349900

thisreport presents an object detection model for visually impaired persons for object detection, face recognition, navigation and distance measurement. the system model uses a webcam to captures visual images. the images are compressed to a resolution of 300×300 pixels. the MobilenetV3 model is used for object detection which then are classified into human and non-human types. A face detection algorithm is used to recognize human faces. the distance measurement process uses the focal length of the camera and a calibration pattern to calculate the distance of the objects. the proposed object detection model achieved an impressive performance on the test dataset with an average precision of 92.5%, achieving an accuracy of more than 98% for human detection and approximately 91.7% for other objects. the proposed integrated model's high accuracy and precision make it a promising solution for the visually impaired people © 2024 IEEE.

关键词： Adversarial machine learning

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A Framework for Cognitive Load recognition Based on machine learning and Multimodal Physiological Signals by Wearable Sensors 4

A Framework for Cognitive Load Recognition Based on Machine ...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Wang, Chenwei Li, Xinyu Huang, Jinyang Zhao, Peng Wang, Meng Zhuang, Guohang Sun, Xiao Hefei University of Technology School of Computer and Information Hefei China

ISBN: (纸本)9798350324303

Cognitive load recognition is of great significance to the study of emotional health, as excessive cognitive load not only impairs work and learning efficiency but also leads to mental pressure and fatigue, affecting overall well-being. Advances in wearable technology and understanding the role of physiological signals in assessing human health have enabled the possibility of cognitive load recognition using these signals. this work proposes a framework that combines multiple physiological signals (ECG, EDA, and PPG) collected through wearable sensors for cognitive load recognition. Heart rate variability (HRV) features are extracted from ECG signals, while statistical characteristics are extracted from EDA and PPG signals. the RFECV feature selection algorithm is utilized in the feature selection module to achieve optimal feature subset selection and redundant feature removal. the classification of cognitive load states is implemented based on the ASAPSO-LightGBM(Adaptive Simulated Annealing Particle Swarm Optimization-Light Gradient Boosting machine) machine learning model. We validated the effectiveness of the framework on two publicly available datasets (MAUS and WESAD). Experimental comparisons of feature combinations and machine learning models demonstrate that the proposed framework achieves superior performance, with recognition accuracy of 98.52% and 88.38% on the two datasets, respectively. © 2023 IEEE.

关键词： Feature Selection

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