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Weisfeiler Leman for Euclidean Equivariant Machine Learning 41

Weisfeiler Leman for Euclidean Equivariant Machine Learning

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41st International Conference on Machine Learning, ICML 2024

作者： Hordan, Snir Amir, Tal Dym, Nadav Faculty of Mathematics Technion - Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion - Israel Institute of Technology Haifa Israel

The k-Weisfeiler-Leman (k-WL) graph isomorphism test hierarchy is a common method for assessing the expressive power of graph neural networks (GNNs). Recently, GNNs whose expressive power is equivalent to the 2-WL test were proven to be universal on weighted graphs which encode 3D point cloud data, yet this result is limited to invariant continuous functions on point clouds. In this paper, we extend this result in three ways: Firstly, we show that PPGN (Maron et al., 2019a) can simulate 2-WL uniformly on all point clouds with low complexity. Secondly, we show that 2-WL tests can be extended to point clouds which include both positions and velocities, a scenario often encountered in applications. Finally, we provide a general framework for proving equivariant universality and leverage it to prove that a simple modification of this invariant PPGN architecture can be used to obtain a universal equivariant architecture that can approximate all continuous equivariant functions uniformly. Building on our results, we develop our WeLNet architecture, which sets new state-of-the-art results on the N-Body dynamics task and the GEOM-QM9 molecular conformation generation task. Copyright 2024 by the author(s)

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Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild 48

Quaternion Orthogonal Transformer for Facial Expression Reco...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhou, Yu Guo, Liyuan Jin, Lianghai Huazhong University of Science and Technology School of Computer Science and Technology China Huazhong University of Science and Technology Institute of Artificial Intelligence China

ISBN: (纸本)9781728163277

Facial expression recognition (FER) is a challenging topic in artificial intelligence. Recently, many researchers have attempted to introduce Vision Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional features extracted from raw images and requires a lot of computing resources. To overcome these problems, we propose a quaternion orthogonal transformer (QOT) for FER. Firstly, to reduce redundancy among features extracted from pre-trained ResNet-50, we use the orthogonal loss to decompose and compact these features into three sets of orthogonal sub-features. Secondly, three orthogonal sub-features are integrated into a quaternion matrix, which maintains the correlations between different orthogonal components. Finally, we develop a quaternion vision transformer (Q-ViT) for feature classification. The Q-ViT adopts quaternion operations instead of the original operations in ViT, which improves the final accuracies with fewer parameters. Experimental results on three in-the-wild FER datasets show that the proposed QOT outperforms several state-of-the-art models and reduces the *** are available at https://***/Gabrella/QOT. © 2023 IEEE.

关键词： Face recognition

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Classification of Land Cover using Machine Learning Models in Landsat Satellite Data 15

Classification of Land Cover using Machine Learning Models i...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Cmr Institute of Technology Department of Computer Applications Bengaluru India Srm Institute of Science and Technology Department of Dsbs Chennai Kattankulathur India

ISBN: (纸本)9798350370249

In this work, we examine the possibility of using cloud cover area analysis in Google Earth Engine (GEE) in conjunction with Landsat satellite data for classifying land cover in the Chennai region between 2023 and 2024. With the development of technology and the accessibility of satellite imagery, remote sensing data have provided invaluable insights into environmental dynamics and have become more important for tracking changes in land cover over time. The study area, Chennai, represents a rapidly urbanizing region where accurate land cover classification is crucial for urban planning, resource management, and environmental conservation efforts. To achieve this, we adopted a supervised classification approach, dividing the dataset into training and testing subsets with an 8 0% - 2 0% split. Three distinct classification models were developed and evaluated: Classification and Regression Trees (CART), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes (NB). Each model leverages the spectral information from Landsat satellite imagery to classify land cover types across the study area. Our analysis revealed varying degrees of accuracy among the models. The CART model achieved a classification accuracy of 7 5%, followed by the KNN model with 77.5% accuracy, NB achieved 8 0% and SVM performed with 7 2. 5%. Notably, the Random Forest model outperformed the others significantly, achieving an impressive accuracy of 8 2. 5%. This indicates the efficacy of ensemble learning techniques, such as Random Forest, in handling complex spectral patterns and improving classification performance in urban environments like Chennai. © 2024 IEEE.

关键词： GEE Land cover Landsat Satellite image

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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A Novel Hybrid Recommendation System for Riyadh Restaurants: Integrating Content-Based and Context-Aware Techniques 4

A Novel Hybrid Recommendation System for Riyadh Restaurants:...

引用

4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Rohilla, Vinita Kaur, Mandeep Chakraborty, Sudeshna Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India Galgotia University Department of Computer Science and Technology Greater Noida India

ISBN: (纸本)9798350364729

Recommendation systems are often used to deliver consumers a customised set of services. Their main purpose is to provide recommendations or concepts (such as restaurants, tourist attractions, medicines, movies, etc.) that effectively address consumer issues and may be used across several sectors. In today's environment, when there are so many eating alternatives, selecting the ideal restaurant to meet our interests may be difficult. Saudi Arabia's tourist industry has lately emerged as an important economic engine. It is becoming more difficult for customers to choose among the vast array of restaurants available in Riyadh due to the industry's fast expansion in recent years. This research presents a comprehensive recommendation system for Riyadh restaurants that integrates content-based filtering and context-aware location analysis to provide personalised suggestions. Utilising a dataset from Kaggle with information on 20,000 restaurants, the system employs a hybrid approach combining TF-IDF and cosine similarity for content-based filtering with geographic distance calculations for location-based recommendations. The hybrid model balances content relevance with geographic proximity, enhancing user satisfaction by delivering accurate and contextually relevant restaurant recommendations. The outcomes demonstrate an effectiveness of a hybrid method, outperforming individual techniques by generating more relevant and practical recommendations. The uniqueness of this study resides in the use of hybrid filtering, which has not before been used in comparable investigations. A more detailed and easily digestible list of suggested models is what this study report is aiming to provide. According to the findings and conclusion, the proposed strategy is effective in recommending restaurants in Riyadh. © 2024 IEEE.

关键词： Wiener filtering

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Analysis on Text Compression Algorithms 7

Analysis on Text Compression Algorithms

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Aruna, P. Devi, L Yamuna Danusri, E. Pragathy, V.Y. Sri Jaya Vaishnavi, K.S. Coimbatore Institute of Technology Department of Computing Coimbatore India Coimbatore Institute of Technology Department of Computer Applications Coimbatore India Coimbatore India

ISBN: (纸本)9798350341485

In the rapidly evolving digital age, the exponential growth of data accumulation has become a ubiquitous challenge, necessitating efficient file compression techniques for optimized data handling, transmission, and storage. The fundamental concepts and advancements in file compression are explored, shedding light on its significance in today's data-driven world, especially in the field of cloud computing. Cloud computing demands resource efficiency and cost-effectiveness, making compression crucial for data management. Among these techniques, Huffman Coding (Statistical Algorithm) and LZ77 (Dictionary-Based Algorithm) are two prominent algorithms that have found widespread applications in various fields, ranging from data storage and communication to multimedia compression. Additionally, the combination of both Huffman Coding and LZ77 (Deflate Algorithm) are examined, which explores the hybrid compression techniques that harness the strengths of both algorithms. Overall, a comprehensive overview of file compression techniques and their applications are provided, making it a valuable resource for professionals and researchers seeking to optimize data management in the face of mounting data volumes and cloud-based demands. © 2023 IEEE.

关键词： Cloud computing

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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Deep Learning based Abstractive text Summarization: A Survey 6

Deep Learning based Abstractive text Summarization: A Survey

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6th Parul International Conference on Engineering and technology, PICET 2024

作者： Kiran, G. Uday Gandi, Ramakrishna Lavanya, M. Desale, Girish Bhagwant Rao, Ch. Upendar Reddy, B. Veerasekhar B V Raju Institute of Technology Narsapur India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science India Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science IT JET'S Z. B. Patil College M.S. Dhule India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798350369748

The explosion in the amount of textual data in the last several years has created an abundant source of resources for data extraction and analysis. It is necessary to summarize this data in order to access relevant information in a fair amount of time. Here we take a look at some of the more current methods for summarizing abstractive texts with deep learning models. A hotspot for research in the field of Natural Language Processing (NLP) is summarization, the process of condensing a document without losing any of its significance. You can classify summarization methods as either "extractive" or "abstractive" depending on whether they use natural language processing to construct new phrases or keep the exact ones from the source text. Extensive research has been conducted on extractive summarization, and the issue has now attained maturity. At this point, the focus of the study is on providing an abstract summary. Abstractive Text Summarization (ATS) is a tough and complicated operation because of the inherent complexity of the natural language content. Researchers can get a thorough overview of Deep Learning based ATS in this work. This paper provides a comprehensive survey of deep learning models employed for abstractive summarization, delving into their architectures and methodologies. Furthermore, it scrutinizes prevalent datasets utilized for training and evaluation purposes, offering insights into their characteristics and suitability for benchmarking. The study evaluates the performance of various abstractive summarization methods on these datasets, shedding light on their effectiveness and limitations. Lastly, emerging themes in the field are discussed, along with unresolved challenges, aiming to foster deeper understanding and advancement in DL-based abstractive text summarization among researchers. © 2024 IEEE.

关键词： Contrastive Learning

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Music Emotion Classification using Harris Hawk Optimization based LightGBM Classifier 10

Music Emotion Classification using Harris Hawk Optimization ...

引用

10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Patil, Hemprasad Yashwant Bhagwan Patil, Priyanka Balasubramaniam, Anishkaa Shanmugasundaram, Lackesh Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350350951

This research investigates the relationship between music and human emotions, focusing on emotion classification in Turkish music and the role of acoustics in music emotion recognition (MER). Leveraging a unique dataset stored in the UCI-MLR, the study employs machine learning techniques, including Linear Discriminant Analysis and Gradient Boost Classifier, to build a robust model. The accuracy enhancement is primarily driven by the feature selection process, which is powered by the Harris Hawks optimizer. This optimizer, when used with the LightGBM model, enables a more precise selection of features, thereby significantly improving the model's accuracy. The research achieves notable accuracies - 81.25% for Linear Discriminant Analysis and 83.75% for the Gradient Boosting Classifier - surpassing previous records. The paper discusses findings in the context of affective computing, emphasizing potential applications in music recommendation systems and therapy, concludes with a comprehensive analysis, and outlines future possibilities. This research contributes to advancing MER, laying the groundwork for innovations in diverse domains, including the potential integration with IoT-based intelligent systems for real-time emotion analysis and personalized music experiences based on camera frames. © 2024 IEEE.

关键词： Emotion Recognition

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