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1st IEEE International Conference on computing and data science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance 39

Just What You Desire: Constrained Timeline Summarization wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qorib, Muhammad Reza Hu, Qisheng Ng, Hwee Tou Department of Computer Science National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Given news articles about an entity, such as a public figure or organization, timeline summarization (TLS) involves generating a timeline that summarizes the key events about the entity. However, the TLS task is too underspecified, since what is of interest to each reader may vary, and hence there is not a single ideal or optimal timeline. In this paper, we introduce a novel task, called Constrained Timeline Summarization (CTLS), where a timeline is generated in which all events in the timeline meet some constraint. An example of a constrained timeline concerns the legal battles of Tiger Woods, where only events related to his legal problems are selected to appear in the timeline. We collected a new human-verified dataset of constrained timelines involving 47 entities and 5 constraints per entity. We propose an approach that employs a large language model (LLM) to summarize news articles according to a specified constraint and cluster them to identify key events to include in a constrained timeline. In addition, we propose a novel self-reflection method during summary generation, demonstrating that this approach successfully leads to improved performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks 13

Metaheuristic Optimization for Path Planning in UAV Networks...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Akshya, J. Manochitra, M. Indu Poornima, R. Jeevitha, T. Saravanakumar, M. Sundarrajan, M. Choudhry, Mani Deepak Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Coimbatore India Institute of Technology Department of Information Technology KGiSL Coimbatore India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Deportment of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

With broad applicability in surveillance, delivery, and environmental monitoring, multi-drone path planning has received a great deal of attention recently. So far, wide applicability has been explored by various applications of Genetic Algorithms, Particle Swarm Optimization, and Ant Colony Optimization, which have already established reliable solutions to navigation and coverage through drone path optimization. However, in dynamic, obstacle-dense environments, these algorithms fail to adapt in real-time with energy efficiency. Traditional GA and PSO approaches can be effective for generating an initial path but fail to respond to collision avoidance and efficient resource management. In complex and unpredictable conditions, ACO fails to converge rapidly, which leads to suboptimal paths and increased energy consumption. To overcome such barriers, we present here an HGARL for enhanced multi-drone path optimization. The proposed algorithm thus combines the population-based search capability of GA with RL's adaptive learning ability, therefore providing an algorithm that in addition to finding optimal paths adjusts the paths according to changing environmental situations. HGARL presents better performance compared to existing methods. In path convergence, it reaches up to 15%, and in energy consumption, it reduces up to 20% with safe collision-free navigation. The strong potential of HGARL for robust, efficient, and adaptive solutions of real-time multi-drone operation in complex and dynamic environments has set a new benchmark for UAV path planning. © 2024 IEEE.

关键词： Genetic algorithms

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Introduction to the Minitrack on Business Process Technology

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2022年 2022-January卷 6195-6196页

作者： Leopold, Henrik van der Aa, Han Reijers, Hajo A. Department of Operations and Technology Kühne Logistics University Hamburg Germany Data and Web Science Group University of Mannheim Mannheim Germany Department of Information and Computing Sciences Utrecht University Utrecht Netherlands

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Warts Disease Treatment Suggestion Using Images and Numerical data

Warts Disease Treatment Suggestion Using Images and Numerica...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Daketi Yatin Challa Avinash D Hemavathi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

To classify wart treatment methods, this research paper examines the effectiveness of using machine learning (ML) and deep learning algorithms in conjunction with numerical and image data. Human papillomavirus (HPV)–induced warts are a common dermatological concern. Several factors can affect the severity and spread of these lesions. Making use of both picture and numerical data, the study suggests a thorough method for classifying treatments. The paper shows that the suggested methodology is effective through thorough experimentation. The study achieves remarkable classification accuracy, specifically $91.67 \%$ for cryotherapy and $85 \%$ for immunotherapy datasets, by utilising machine learning and deep learning algorithms. Notably, accuracy rates of $76 \%$ for cryotherapy and $74 \%$ for immunotherapy are obtained by combining synthetic and raw data, demonstrating the potential benefits of integrating various data sources. The study adds a comprehensive framework that makes accurate classification of wart treatments possible. The model provides a comprehensive understanding of wart types and treatment outcomes by combining image analysis with numerical data. This creative method uses both quantitative and visual data to enable users to make well-informed decisions. All things considered, the study highlights the potential of AI and ML methods to improve the classification of wart treatments, offering dermatologists and other medical professionals a useful tool. This study is a major step towards more individualised and data- driven dermatological care strategies, which could lead to better patient outcomes and more effective treatments.

关键词： Deep learning Visualization Accuracy Machine learning algorithms Soft sensors Immunotherapy Classification algorithms

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Exponential Capacity of Dense Associative Memories

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Physical Review Letters 2024年第7期132卷 077301-077301页

作者： Carlo Lucibello Marc Mézard Department of Computing Sciences Bocconi University Milano 20136 Italy and Bocconi Institute for Data Science and Analytics (BIDSA) Milano 20136 Italy.

Recent generalizations of the Hopfield model of associative memories are able to store a number P of random patterns that grows exponentially with the number N of neurons, P=exp(αN). Besides the huge storage capacity, another interesting feature of these networks is their connection to the attention mechanism which is part of the Transformer architecture widely applied in deep learning. In this work, we study a generic family of pattern ensembles using a statistical mechanics analysis which gives exact asymptotic thresholds for the retrieval of a typical pattern, α1, and lower bounds for the maximum of the load α for which all patterns can be retrieved, αc, as well as sizes of attraction basins. We discuss in detail the cases of Gaussian and spherical patterns, and show that they display rich and qualitatively different phase diagrams.

关键词： Disordered systems Spin glasses Deep learning Machine learning Replica theory

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Block Broyden's Methods for Solving Nonlinear Equations 37

Block Broyden's Methods for Solving Nonlinear Equations

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Chengchang Chen, Cheng Luo, Luo Lui, John C.S. Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Data Science Fudan University China

ISBN: (纸本)9781713899921

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden's methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden's method has a faster condition-number-free convergence rate than existing Broyden's methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden's method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden's method outperforms bad Broyden's method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Nonlinear equations

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An Improved Detection of Skin Melanoma Using Hybrid CNN-MobileNet V2 Approach with Multi-Scale Attention 2

An Improved Detection of Skin Melanoma Using Hybrid CNN-Mobi...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Maniraj, S.P. Thamizhamuthu, R. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Chennai603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331505745

Identifying melanoma in dermoscopic images is an immense challenge due to the varied appearances of the lesions. This research introduces a hybrid CNN-LSTM model augmented with a multi-scale attention mechanism to tackle this complexity, utilising CNNs for spatial feature extraction and LSTMs for temporal pattern recognition. Our model surpasses traditional CNN and MobileNet V2-LSTM models, with an accuracy of 96.98%, a recall of 96.65%, a specificity of 97.56%, and a JSI of 95.78%. These measurements demonstrate enhanced sensitivity and reliability compared to baseline models, signifying strong diagnostic capabilities. This study improves melanoma detection, aiding in more precise diagnoses for clinical environments and remote telemedicine applications. Future initiatives involve enhancing the model for more comprehensive skin lesion categorisation. © 2025 IEEE.

关键词： Feature Selection

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ACCELERATING 3D MOLECULE GENERATION VIA JOINTLY GEOMETRIC OPTIMAL TRANSPORT

arXiv

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arXiv 2024年

作者： Hong, Haokai Lin, Wanyu Tan, Kay Chen Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong

This paper proposes a new 3D molecule generation framework, called GOAT, for fast and effective 3D molecule generation based on the flow-matching optimal transport objective. Specifically, we formulate a geometric transport formula for measuring the cost of mapping multi-modal features (e.g., continuous atom coordinates and categorical atom types) between a base distribution and a target data distribution. Our formula is solved within a joint, equivariant, and smooth representation space. This is achieved by transforming the multi-modal features into a continuous latent space with equivariant networks. In addition, we find that identifying optimal distributional coupling is necessary for fast and effective transport between any two distributions. We further propose a mechanism for estimating and purifying optimal coupling to train the flow model with optimal transport. By doing so, GOAT can turn arbitrary distribution couplings into new deterministic couplings, leading to an estimated optimal transport plan for fast 3D molecule generation. The purification filters out the subpar molecules to ensure the ultimate generation quality. We theoretically and empirically prove that the proposed optimal coupling estimation and purification yield transport plan with non-increasing cost. Finally, extensive experiments show that GOAT enjoys the efficiency of solving geometric optimal transport, leading to a double speedup compared to the sub-optimal method while achieving the best generation quality regarding validity, uniqueness, and novelty. The code is available at github. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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