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A Two-Stage Hybrid GA-Cellular Encoding Approach to Neural Architecture Search

A Two-Stage Hybrid GA-Cellular Encoding Approach to Neural A...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Londt, Trevor Gao, Xiaoying Andreae, Peter Mei, Yi Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

ISBN: (纸本)9781665430654

Neural Architecture Search (NAS) aims to automate the creation of Artificial Neural Networks, including Convolutional Neural Networks (CNN), lessening the reliance on labour-intensive manual design by human experts. A CNN architecture can be decomposed into a micro- and macro-architecture, each influenced by distinct design and optimisation strategies to con-tribute to the overall construction and performance of the CNN. Cellular Encoding (CE), an evolutionary computation technique, has been successfully used to represent diverse network topologies of varying complexities. Recently, CE has been applied to evolve CNN architectures, showing promising results. However, current CE-based NAS approaches focus on evolving either the micro-or macro-architectures without considering the evolution of both in the same algorithm. Evolving the micro- and macro-architecture together can increase the performance of evolved CNN architectures. This research introduces a novel two-stage hybrid approach, combining Genetic Algorithms (GA) and CE to evolve both the micro- and macro-architectures to synthesise CNNs for classification tasks. Candidate macro-architectures are evolved using a CE approach, while a GA approach is used to explore the micro-architecture search space. The proposed algorithm is evaluated across four commonly used datasets and compared against six NAS peer competitors and five state-of-the-art manually designed CNN architectures. The results validate the approach's high competitiveness, outperforming several peer competitors on image and text classification tasks. © 2023 IEEE.

关键词： Cellular Encoding Convolutional Neural Networks Neural Architecture Search

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Pure Message Passing Can Estimate Common Neighbor for Link Prediction 38

Pure Message Passing Can Estimate Common Neighbor for Link P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Kaiwen Guo, Zhichun Chawla, Nitesh V. Computer Science and Engineering University of Notre Dame United States Lucy Family Institute for Data and Society University of Notre Dame United States

Message Passing Neural Networks (MPNNs) have emerged as the de facto standard in graph representation learning. However, when it comes to link prediction, they are not always superior to simple heuristics such as Common Neighbor (CN). This discrepancy stems from a fundamental limitation: while MPNNs excel in node-level representation, they stumble with encoding the joint structural features essential to link prediction, like CN. To bridge this gap, we posit that, by harnessing the orthogonality of input vectors, pure message-passing can indeed capture joint structural features. Specifically, we study the proficiency of MPNNs in approximating CN heuristics. Based on our findings, we introduce the Message Passing Link Predictor (MPLP), a novel link prediction model. MPLP taps into quasi-orthogonal vectors to estimate link-level structural features, all while preserving the node-level complexities. We conduct experiments on benchmark datasets from various domains, where our method consistently outperforms the baseline methods, establishing new state-of-the-arts. © 2024 Neural information processing systems foundation. All rights reserved.

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Analysis of Multi-Class Convolution Neural Network on Garbage Classification System

Analysis of Multi-Class Convolution Neural Network on Garbag...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vikas, K.R. Ullas, P. Sri Sai Teja, M.S. Kumar Swamy, S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

The buildup of solid waste in metropolitan areas is a major worry that, if not effectively handled, might lead to environmental contamination and be dangerous to human health. To manage a range of waste products, it's crucial to have an advanced/intelligent waste management system. The act of separating garbage into its many components is one of the most crucial parts of waste management, and it is often carried out manually by hand-picking. To simplify this, we suggest a customized architecture for multiclass Convolutional Neural Networks (CNNs). The design includes elements that are individual-specific, enabling the model to adjust to the traits of each class. Extensive evaluation has shown that our CNN architecture, utilising a personalised approach, outperforms traditional CNN models like ResNet50 and VGG16 in multiclass classification situations. The effectiveness and resilience of our suggested architecture are demonstrated by experimental results on our dataset, opening the path for increased classification accuracy of 71.15%. © 2023 IEEE.

关键词： Classification (of information)

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Prior-Guided Parallel Residual Bi-Fusion Network in USV Obstacle Detection

Prior-Guided Parallel Residual Bi-Fusion Network in USV Obst...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Chih-Chung Yang, Sophia Hou, Xiu-Yu Jhang, Yu-An Institute of Data Science National Cheng Kung University Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Taiwan

ISBN: (纸本)9798350324174

In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F-avg score of 0.514. © 2023 IEEE.

关键词： Unmanned surface vehicles

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AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage Cost KV-Separated LSM Store with SmartSSD-based GC Offloading 23

AegonKV: A High Bandwidth, Low Tail Latency, and Low Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Duan, Zhuohui Feng, Hao Liu, Haikun Liao, Xiaofei Jin, Hai Li, Bangyu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133458

The key-value separation is renowned for its significant mitigation of the write amplification inherent in traditional LSM trees. However, KV separation potentially increases performance overhead in the management of Value region, especially for garbage collection (GC) operation that is used to reduce the redundant space occupation. In response, many efforts have been made to optimize the GC mechanism for KV separation. However, our analysis indicates that such solution based on trade-offs between CPU and I/O overheads cannot simultaneously satisfy the three requirements of KV separated systems in terms of throughput, tail latency, and space usage. This limitation hinders their real-world application. In this paper, we introduce AegonKV, a "three-birds-one-stone" solution that comprehensively enhances the throughput, tail latency, and space usage of KV separated systems. AegonKV first proposes a SmartSSD-based GC offloading mechanism to enable asynchronous GC operations without competing with LSM read/write for bandwidth or CPU. AegonKV leverages offload-friendly data structures and hardware/software execution logic to address the challenges of GC offloading. Experiments demonstrate that AegonKV achieves the largest throughput improvement of 1.28-3.3 times, a significant reduction of 37%-66% in tail latency, and 15%-85% in space overhead compared to existing KV separated systems. © 2025 FAST. All Rights Reserved.

关键词： Digital storage

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Comparative Study of Customized CNN Model and Transfer Learning for Brain Tumor Classification

Comparative Study of Customized CNN Model and Transfer Learn...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Vaishnavi, N. Sinchana, K. Kodipalli, Ashwini Rao, Trupthi Kumarswamy, S. Global Academy of Technology Department of Computer Science and Engineering Karnataka Banglore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300826

Brain tumors are abnormal cell growths in the brain, which can be cancerous or non-cancerous. They have diverse effects, including cognitive and neurological impairments, as well as personality and behavioral changes. This study proposes a multiclass classification framework utilizing a plain CNN architecture and Transfer learning to accurately identify brain tumor types: meningioma, glioma, pituitary tumor, and no tumor. We have explored transfer learning models such as VGG16, AlexNet, and ResNet50, these are initialized with pre-trained weights and fine-tuned on a comprehensive brain tumor dataset which has an equal distribution among classes. Optimizers such as Adam, AdaDelta, and SGD are modified to enhance performance. AdaDelta was used as the VGG16 optimizer, and it helped us achieve the highest accuracy of 99.83% with a loss of 0.74%. ResNet50 on the other hand, had the lowest accuracy 70% and highest loss 66.68%. While AlexNet without dropouts and AdaDelta as optimizer gives highest loss of 92.83%. The custom CNN model's loss was 0.04%, which is the lowest of any of them. The findings contribute to robust tools for accurate brain tumor diagnosis and treatment planning. © 2023 IEEE.

关键词： Tumors

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Analysis and Prediction of Individual Carbon Footprints: An Integrated Machine Learning and Web Application Approach for Sustainable Behavioural Change

Analysis and Prediction of Individual Carbon Footprints: An ...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Baswaraju Swathi Rohan M Khushi Nagaraj Computer Science Engineering (Data Science) New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Climate change has accelerated due to unprecedented increases in greenhouse gas emissions, driven by both industrial activities and individual behaviour. In response, there is a growing need for accurate, personalized carbon footprint assessment tools that can empower users to adopt sustainable practices. In this work, we present an integrated framework combining robust data preprocessing, advanced ensemble machine learning techniques, and an interactive web application to predict individual carbon footprints. Our system utilizes a multi-dimensional dataset—including transportation, dietary choices, household energy consumption, and social activities—to train a series of predictive models such as Linear Regression, Decision Trees, Random Forests, Support Vector Regression, XGBoost, and CatBoost. The CatBoost Regressor achieved superior performance (MAE = 68.64, RMSE = 95.49, R 2 = 0.991). Comprehensive feature analysis reveals that transportation habits and energy usage are the predominant drivers of emissions. Our approach is contextualized within a broad literature review that covers evaluations of carbon footprint calculators, ICT-enabled reduction strategies, advanced predictive and retrieval methods, global emission analyses, reviews on ICT and sustainability, usability studies, and further project and code insights. The developed system not only provides accurate predictions but also offers actionable sustainability recommendations. Future enhancements include incorporating regional emission factors and real-time data integration.

关键词： Accuracy Calculators data preprocessing Transportation Predictive models Real-time systems Sustainable development Usability Carbon footprint Systematic literature review

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Personality Prediction Using Machine Learning 1

Personality Prediction Using Machine Learning

引用

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Pooja, K. Rakshitha, G.H. Spoorthi, S. Kamal, Shoaib Rao, Trupthi Ashwini, K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350339727

With the growth of social networks, a wide range of methodologies have been developed to describe users' personalities only based on their language and social media use habits. Persona prediction is very popular these days. It examines consumer behaviour and records the user's ideas, emotions, and so on. There has been a sufficient amount of research in this field over the years. This survey provides an overview of the numerous methods tried to predict personality and behaviour from the use of the social media content. The capacity to anticipate the personality attributes of users can help create a variety of specialised goods or services. The concluding phase then provides the future characteristics and directives. © 2023 IEEE.

关键词： Machine learning

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A Comprehensive Analysis of the Internet of Things (IOT) in the Context of 5G Wireless Systems 4

A Comprehensive Analysis of the Internet of Things (IOT) in ...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mohseni, Milad Kanwer, Budesh Singh, Navdeep Krishna, M Hari Rajalakshmi, B. Alkhafaji, Mohammed Ayad Islamic Azad University Department of Computer and Engineering Tabriz Iran Poornima Institute of Engineering & Technology Department of Artificial Intelligence & Data Science India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad India New Horizon College of Engineering Department of Computer Science Bangalore India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350307757

The fast development of 5G wireless technologies (IoT) has been a major contributor to the rapid growth and expansion of the Internet of Things! This in-depth study investigates the mutually advantageous relationship between 5G and the Internet of Things (IoT), focusing on how the combination assists in the development of massive device networks, enhances connectivity, and enables the development of real-time applications. There are now new opportunities for innovative applications in smart cities, industrial automation, healthcare, and other industries owing to 5G, which enables Internet of Things devices to communicate with one another at a speed and efficiency that has never been seen before. In spite of the benefits, however, there are challenges to be faced, such as the construction of infrastructure to support the expansion of the Internet of Things and the assurance of robust security. This article provides an analysis of these components and a blueprint for leveraging the advantages of Internet of Things (IoT) with 5G while minimizing any potential downsides. © 2024 IEEE.

关键词： Internet of things

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SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries 61

SConE: Simplified Cone Embeddings with Symbolic Operators fo...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Nguyen, Chau Duc Minh French, Tim Liu, Wei Stewart, Michael ARC Centre for Transforming Maintenance through Data Science Department of Computer Science and Software Engineering School of Physics Mathematics and Computing The University of Western Australia

ISBN: (纸本)9781959429623

Geometric representation of query embeddings (using points, particles, rectangles and cones) can effectively achieve the task of answering complex logical queries expressed in first-order logic (FOL) form over knowledge graphs, allowing intuitive encodings. However, current geometric-based methods depend on the neural approach to model FOL operators (conjunction, disjunction and negation), which are not easily explainable with considerable computation cost. We overcome this challenge by introducing a symbolic modeling approach for the FOL operators, emphasizing the direct calculation of the intersection between geometric shapes, particularly sector-cones in the embedding space, to model the conjunction operator. This approach reduces the computation cost as a non-neural approach is involved in the core logic operators. Moreover, we propose to accelerate the learning in the relation projection operator using the neural approach to emphasize the essential role of this operator in all query structures. Although empirical evidence for explainability is challenging, our approach demonstrates a significant improvement in answering complex logical queries (both non-negative and negative FOL forms) over previous geometric-based models. © 2023 Association for Computational Linguistics.

关键词： computer circuits

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