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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： K E Narayana Jayashree K ThavaVinu A M.Sindhuja Jaeyalakshmi M Department of Information Technology Rajalakshmi Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamilnadu India Department of Information Technology Prathyusha Engineering College Chennai Department of Computer Science and Engineering Acharya University Karakul Uzbekistan Department Of Computer Science And Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Lung cancer is one of the most deadly and ubiquitous forms of cancer globally. Early detection can make a significant difference in survival rates, prognosis, etc. Background The present study compares the performance of logistic regression and decision trees using a survey-based dataset for predicting lung cancer. This dataset has a total of 284 cases with 16 features; behavioral patterns, symptomatic data, and demographic details. Logistic regression is a binary classification algorithm that uses input features to model the probability of lung cancer presence given these input features using for example a sigmoid function. Using Decision Trees can identify significant fields in a dataset while reducing Gini Impurity for models that categorize patients based on feature values. The performance of both models is evaluated according to accuracy, F1-score, precision, and recall measures. From the preliminary results, it looks like the Decision Tree works a little better than the logistic regression model which gets an accuracy of 94%, but this one does with more accuracy (97%). This result emphasizes the inclusion of logistic regression within this context, as it may be able to predict lung cancer with greater accuracy. Additionally, to ensure the generalization and robustness of the model, further investigation and trials will focus on optimizing performance through ensemble approaches, hyperparameter tuning, and cross-validation. This comparative analysis demonstrates the effectiveness of machine learning techniques in developing prediction models for early lung cancer detection, an important step toward a quick and appropriate intervention.

关键词： Surveys Logistic regression Accuracy Computational modeling Lung cancer Predictive models Robustness Decision trees Prognostics and health management Tuning

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Deep Learning-Based Buoyancy Prediction for Intelligent Mussel Farm Monitoring

Deep Learning-Based Buoyancy Prediction for Intelligent Muss...

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Carl McMillan Junhong Zhao Bing Xue Ross Vennell Mengjie Zhang Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington New Zealand Coastal and Freshwater Group Cawthron Institute Nelson New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Greenshell mussel farming is the largest contributor to export earnings in Aotearoa New Zealand's (NZ) aquaculture industry. Floats are essential to mussel farm structures, keeping tons of mussels suspended in the water. Monitoring float buoyancy is essential for minimising the loss of mussels due to sinking or excessive energy transfer to the mussels. We propose a novel transformer-based neural network architecture for float buoyancy prediction in NZ's greenshell mussel farms. The model extends a vision transformer to incorporate relationships between adjacent floats, enhancing prediction performance. Results show improved accuracy over baseline methods, suggesting potential for integration into a cost-effective, computer vision-based monitoring system for mussel farm buoyancy at scale. This approach could address the challenges of monitoring buoyancy in expanding coastal and open ocean aquaculture operations.

关键词： Accuracy Computational modeling Oceans Neural networks Sea measurements Buoyancy Predictive models Transformers Monitoring Aquaculture

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Improved Power Conversion Efficiency and Stability of Perovskite Solar Cells Induced by Dual Interface Passivation

Improved Power Conversion Efficiency and Stability of Perovs...

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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model

arXiv

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arXiv 2025年

作者： Chen, Yufan Leung, Ching Ting Sun, Jianwei Huang, Yong Li, Linyan Chen, Hao Gao, Hanyu Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Chemistry Hong Kong University of Science and Technology Hong Kong Department of Data Science City University of Hong Kong Hong Kong

Artificial intelligence (AI) has demonstrated significant promise in advancing organic chemistry research;however, its effectiveness depends on the availability of high-quality chemical reaction data. Currently, most published chemical reactions are not available in machine-readable form, limiting the broader application of AI in this field. The extraction of published chemical reactions into structured databases still relies heavily on manual curation, and robust automatic parsing of chemical reaction images into machine-readable data remains a significant challenge. To address this, we introduce the Reaction Image Multimodal large language model (RxnIM), the first multimodal large language model specifically designed to parse chemical reaction images into machine-readable reaction data. RxnIM not only extracts key chemical components from reaction images but also interprets the textual content that describes reaction conditions. Together with specially designed large-scale dataset generation method to support model training, our approach achieves excellent performance, with an average F1 score of 88% on various benchmarks, surpassing literature methods by 5%. This represents a crucial step toward the automatic construction of large databases of machine-readable reaction data parsed from images in the chemistry literature, providing essential data resources for AI research in chemistry. The source code, model checkpoints, and datasets developed in this work are released under permissive licenses. An instance of the RxnIM web application can be accessed at https://***/spaces/CYF200127/RxnIM. © 2025, CC BY.

关键词： data curation

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Multi-constraint smart UAV healthcare transportation path using imperial competition algorithm for smart cities

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Discover Internet of Things 2025年第1期5卷 1-19页

作者： Byeon, Haewon Ramesh, Janjhyam Venkata Naga AlGhamdi, Azzah Soni, Mukesh Nimma, Divya Pothumarthi, Sridevi Shabaz, Mohammad Convergence Department Korea University of Technology and Education Cheonan Korea Republic of Department of CSE Graphic Era Hill University Dehradun248002 India Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India University of southern Mississippi Data Analyst in UMMC Hattiesburg United States Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist. Andhra Pradesh Vaddeswaram522302 India Model Institute of Engineering and Technology J&K Jammu India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India

In response to the path planning problem of using Unmanned Aerial Vehicle (UAV) for blood transportation, with the objective of minimizing the total distance travelled by the UAV, a multi-constraint drone blood transportation path planning model is established. Taking into account the limited number of drone take-off and landing platforms and the safety time intervals for continuous drone take-off and landing, a drone take-off scheduling strategy is designed to reduce the total time spent by UAV completing transportation tasks. Additionally, an Imperial Competition Algorithm based on Imperialist Competitive Algorithm is proposed to solve this problem. This algorithm introduces a sine disturbance strategy and adds Imperial Reform stages to improve the search accuracy of the algorithm. It utilizes acceptance criteria related to solution quality to maintain the diversity of the population. Validation is conducted using benchmark examples and instances of drone blood transportation. The results indicate that the proposed algorithm can provide transportation solutions for drone blood transportation tasks that meet various constraints without any conflicts in drone take-off and landing. The drone take-off scheduling strategy effectively reduces the total time spent by UAV in completing tasks. © The Author(s) 2025.

关键词： Target drones

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An Efficient Graphics data Retrieval Method Based on data Conversion Schemes

An Efficient Graphics Data Retrieval Method Based on Data Co...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Nakhoon Baek School of Computer Science and Engineering Kyungpook National University Daegu Korea Graduate School of Data Science Kyungpook National University Daegu Korea Data-Driven Intelligent Mobility ICT Research Center Kyungpook National University Daegu Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Modern computer graphics systems usually manage a set of internal data to store control parameters and all kind of graphics data. The behind problem on these graphics system data is that they are frequently updated and retrieved with variety of data types, even they are initially stored as specific data types. To support these updates and retrievals with various data types, modern graphics systems typically use data type conversion features, and even in the worst cases, they should provide all combinations of internally stored data types and newly provided or retrieved data types. In this paper, we show how to efficiently provide all the required combinations of data type conversions, with motivations, analysis, and overall designs. We also show typical type conversion scenarios, with specific API function examples. Our design shows that it can improve the internal architecture and the internal efficiency of modern computer graphics systems. In its implementation, we use C++std:: map container for fast lookup of the best-suitable conversion function. This overall scheme was integrated in our existing systems, and shows its feasibility and correctness.

关键词： data conversion Pipelines Switches computer graphics C++ languages computer architecture Containers data retrieval

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Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus 4

Impact of E-Waste on Pregnant Lady, Newborn Baby, and Fetus

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Tiwari, Ankita Babu, N. Ramesh Rakesh, Nitin Kumar, Mandeep Dhattarwal, Jagjit Singh Bhola, Abhishek College of Engineering Koneru Lakshmaih Education Foundation Department of Engineering Mathematics Guntur India School of Advnced Sciences Vellore Institute of Technology Department of Mathematucs Chennai India Department of Computer Science and Engineering Nagpur Campus Pune India School of Law Sharda University Greater Noida India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence & Data Science Andhra Pradesh Vaddeswaram India Chaudhary Charan Singh Haryana Agricultural University College of Agriculture Bawal India

ISBN: (纸本)9798350387490

Hazardous materials included in electronic trash (e-waste) can affect fetuses, neonates, and expectant mothers. Preterm birth, birth irregularities, and miscarriage are among the risks associated with pregnant women who are exposed to e-waste chemicals. Exposure to e-waste puts newborns at risk for respiratory and neurodevelopmental disorders. Developmental irregularities and growth restrictions are possible in fetuses. To reduce these risks and protect mother and child health, effective methods including appropriate e-waste discarding and public health programmes are important. © 2024 IEEE.

关键词： E-waste Environmental health Fetal development Neonatal health Neuro developmental disorders maternal pregnancy Public health policy recycling technologies Toxic chemicals

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Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method

arXiv

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arXiv 2024年

作者： Liu, Ze Zhang, Jin Feng, Chao Lian, Defu Wang, Jie Chen, Enhong The School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230027 China The School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei230027 China The State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology School of Artificial Intelligence and Data Science University of Science and Technology of China Anhui Hefei230027 China The Department of Electronic Engineering and Information Science University of Science and Technology of China Anhui Hefei230027 China

With the advancement of deep learning, deep recommendation models have achieved remarkable improvements in recommendation accuracy. However, due to the large number of candidate items in practice and the high cost of preference computation, these methods still suffer from low recommendation efficiency. The recently proposed tree-based deep recommendation models alleviate the problem by directly learning tree structure and representations under the guidance of recommendation objectives. To guarantee the effectiveness of beam search for recommendation accuracy, these models strive to ensure that the tree adheres to the max-heap assumption, where a parent node's preference should be the maximum among its children's preferences. However, they employ a one-versus-all strategy, framing the training task as a series of independent binary classification objectives for each node, which limits their ability to fully satisfy the max-heap assumption. To this end, we propose a Deep Tree-based Retriever (DTR for short) for efficient recommendation. DTR frames the training task as a softmax-based multi-class classification over tree nodes at the same level, enabling explicit horizontal competition and more discriminative top-k selection among them, which mimics the beam search behavior during training. To mitigate the suboptimality induced by the labeling of non-leaf nodes, we propose a rectification method for the loss function, which further aligns with the max-heap assumption in expectation. As the number of tree nodes grows exponentially with the levels, we employ sampled softmax to approximate optimization and thereby enhance efficiency. Furthermore, we propose a tree-based sampling method to reduce the bias inherent in sampled softmax. Theoretical results reveal DTR's generalization capability, and both the rectification method and tree-based sampling contribute to improved generalization. The experiments are conducted on four real-world datasets, validating the effectivenes

关键词： Deep reinforcement learning

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LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

arXiv

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arXiv 2025年

作者： Peng, Zengqi Wang, Yubin Han, Xu Zheng, Lei Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China

Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Collaborative deep learning across multiple data centers

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science China(Information sciences) 2020年第8期63卷 169-179页

作者： Haibo MI Kele XU Dawei FENG Huaimin WANG Yiming ZHANG Zibin ZHENG Chuan CHEN Xu LAN College of Computer National University of Defense Technology School of Data and Computer Science Sun Yat-sen University School of Electronic Engineering and Computer Science Queen Mary University of London

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data of different organizations to a centralized data center owing to the constraints of privacy regulations. It is very challenging to conduct the geo-distributed deep learning among data centers without the privacy leaks. Model averaging is a conventional choice for data parallelized training and can reduce the risk of privacy leaks, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate(CLR) and the increased number of epochs for local model training. With the two strategies,we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method.

关键词： collaborative learning multiple datacenters distributed machine learning

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