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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Yuganesan, R. Rajkumar, S. Allen Gilchrist, J. Gowseelan, C. Varun Manickam, S. Soy, Theja School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819619801

Identification and classification of brain tumors have become a concern for the medical community as the treatment planning and patients’ prognosis is highly dependent on their classification. In the past few years, deep learning models have become the most discussed and widely used tools to achieve higher accuracy and efficiency in this area. This study gives a comparison of the two widely used deep learning networks, the modified UNet and ResNet-50 for the task of brain tumor classification. The study assesses the efficiency of these architectures on the benchmark dataset and indicates the strengths and limitations of each. The evaluation process is made of extremely rigorous testing and validation procedures, which are aimed at making the data reliable. The outcomes of our study indicate that both modified UNet and ResNet-50 can reasonably be applied to the brain tumor classification. Modified UNet surpasses ResNet-50 in overall accuracy, which implies its effectiveness in high-precision classifications. This comparison revealed the feasibility of the architectures for classifying brain tumors. It helps both researchers and practitioners in the field by providing them with information to find the model appropriate for their specific needs. Moreover, the results of the study could be used to build the discussion on the application of the deep learning models in the diagnosis and classification of brain tumors as a part of the medical image analysis. Therefore, this paper provides a thorough guidance for individuals who want to use deep learning to enhance healthcare outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electrotherapeutics

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Urban Tree Canopy Mapping and Analysis Using Iterative Annotation Method and Deep Learning: A Case Study in Beijing

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12645-12659页

作者： Ding, Yulong Cui, Ximin Chen, Zhengchao Wang, Zeqing Yuan, Debao Meng, Xiang Yang, Xuan Xu, Yue Tian, Xiangyu China University of Mining and Technology-Beijing College of Geoscience and Surveying Engineering Beijing100083 China Chinese Academy of Sciences Key Laboratory of Remote Sensing and Digital Earth Aerospace Information Research Institute Beijing100094 China China Aerospace Academy of Systems Science and Engineering Beijing100037 China Chinese Academy of Sciences China Remote Sensing Satellite Ground Station Aerospace Information Research Institute Beijing100094 China Chinese Academy of Sciences Institute of Geographic Sciences and Natural Resources Research Beijing100094 China

Urban trees have significant ecological and social functions, and generating Urban Tree Canopy (UTC) maps is an effective method for understanding their distribution. However, existing studies primarily rely on medium- to low-resolution imagery for large-scale extraction or high-resolution imagery for small-scale extraction, making it challenging to balance spatial coverage and accuracy. To comprehensively analyze the tree canopy characteristics of urban trees across large areas, this study selects Beijing as the research area, and employs highresolution remote sensing imagery with deep learning techniques to construct the UTC map of Beijing. Deep learning models typically require large amounts of labeled data for training, but manually annotating pixel-level labels is time-consuming and costly. To address this, we propose an iterative method based on Intersection over Union (IoU), integrating multi-scale segmentation and nearest-neighbor classification algorithms. By leveraging a limited number of labeled pixels, this approach efficiently generates a extensive labeled data, enabling the rapid construction of a high-precision dataset. Using this method, we established the Beijing Urban Tree Canopy Dataset (BUTCD). By Employing BUTCD and InternImage_T+UPerNet as the tree canopy segmentation network, we generated the urban tree canopy map for Beijing. Accuracy assessments demonstrated an accuracy of 0.9806, precision of 0.9569, recall of 0.9702, mean IoU (mIoU) of 0.9296, and F1-score of 0.9635. We analyzed the results of Beijing's urban tree canopy, revealing that the tree canopy coverage rate is approximately 43.99%, with higher coverage in the northwest and lower coverage in the southeast. The per capita tree canopy area in Beijing was calculated as 330.29 m². Within Beijing's central urban area, trees outside forests account for 26.54% of the the total urban tree population, indicating that conventional statistics on tree-related data in this area tend to underest

关键词： Vegetation Urban areas Deep learning Accuracy Feature extraction Data mining Image segmentation Forests Land surface Biological system modeling

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Assessment methodology for the resilience of energy systems in positive energy buildings

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2025年 11卷

作者： Jimba, Chiho Akimoto, Yutaro Okajima, Keiichi University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Japan

Distributed renewable energy systems are emerging in communities and buildings in response to global warming. Concurrently, the frequency of large-scale power outages, which are often triggered by natural disasters and heavy rainfall, has increased. This trend highlights the necessity to develop robust energy systems that integrate distributed renewable energy with other sources. Economic and resilience assessments of photovoltaic (PV) and battery systems within infrastructure contexts have been extensively studied. However, resilience evaluation at the building level is necessary. Given that energy resilience encompasses various assessment aspects, the importance of employing multiple indicators for a comprehensive quantitative evaluation of resilience in PV and battery installations is increasing. This study introduced a methodology to quantitatively assess the resilience of energy systems. It employs multiple resilience indicators and simulates a power outage scenario in a positive energy building (PEB) equipped with PV and batteries. Additionally, this study analyzed the impact of different weather conditions on resilience. An assessment using multiple resilience indicators reveals that energy systems are more resilient on favorable weather days and that resilience is maximized when supply interruptions coincide with daylight hours. Furthermore, while no single indicator can measure resilience, this study shows that the use of multiple indicators enables the clarification and comparison of electricity supply and demand conditions during a disaster. © 2025 The Author(s)

关键词： Battery Energy evaluation Power outage PV Renewable energy

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A hybrid strategy for solving cubic equations of state based on a novel root discriminant method

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Fluid Phase Equilibria 2025年 597卷

作者： Zhao, Jun Zhang, Luoyu Wang, Lili Chen, Yushi Zhao, Wenying Tao, Shaohui Xiang, Shuguang Institute of Process Systems Engineering Qingdao University of Science and Technology ShanDong266042 China Petro-Cyber Works Information Technology Co. Ltd Shanghai200050 China

The cubic equations of state (CEoS) is one of the most fundamental and popular models in chemical process simulation. This study aims to explore the impact of CEoS solution methods on the speed of process simulation and propose a simple and fast method to determine the single real root of CEoS at a single-phase state. Utilizing the advantages of this method, a hybrid CEoS solution strategy based on Cardano-Tartáglia's formula and Newton-Raphson method (HCTNR) was developed. The average computation time is approximately 37 % of that of the Cardano-Tartáglia's formula. The calculation speed of the new method is faster than that of the well-known Halley's method. Extensive examples of chemical process simulation were used to verify the speed and robustness of this method, and the results indicate that HCTNR can effectively improve the speed of process simulation while ensuring the accuracy of the calculation results. © 2025

关键词： Equations of state of solids

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Denoising diffusion models with optimized quantum implicit neural networks for image generation

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Future Generation Computer systems 2025年 173卷

作者： Zhang, Jiale Che, Xilong Fan, Yuzhe Peng, Shun Chen, Geng Ma, Quangong Hu, Juncheng College of Computer Science and Technology Jilin University Changchun China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information Systems Engineering Information Engineering University Zhengzhou China

Denoising Diffusion Models (DDMs) have attracted significant attention due to their capacity to generate diverse, high-quality samples in computer vision tasks, offering flexible architectures and straightforward training processes. While several studies have extended diffusion models to quantum domains, these approaches often rely on hybrid U-net architectures or mixed-state manipulations with trace-out measurements, which pose substantial challenges for implementation on current quantum hardware. To address these challenges, we introduce an Optimized Quantum Implicit Denoising Diffusion Model (OQIDDM), which combines our proposed Optimized Quantum Implicit Neural Networks (OQINNs) with Consistency Models. OQINNs optimizes the complex network structures inherent in existing Quantum Implicit Neural Networks. By leveraging OQINNs to model image data distributions over multiple timesteps and employing Consistency Models for O(1) sampling steps, our approach is better suited for deployment on noisy quantum hardware. Experimental results on the MNIST, Fashion-MNIST, and E-MNIST datasets demonstrate that OQIDDM outperforms existing quantum diffusion models in terms of image quality, while requiring far fewer parameters in the quantum neural network. Compared to state-of-the-art quantum generative adversarial networks (QGANs) and classical GANs, our model notably enhances image generation quality while substantially reducing training parameters. Additionally, we present experiments on facial image generation, marking the first application of quantum denoising diffusion models to complex datasets. Analysis of quantum noise effects, along with experimental results on three different superconducting quantum computers, further underscores the considerable potential of OQIDDM for quantum generative tasks. © 2025 Elsevier B.V.

关键词： Qubits

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Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

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8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina Alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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CMD: A Cache-Assisted GPU Memory Deduplication Architecture

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年

作者： Zhao, Wei Feng, Dan Tong, Wei Wei, Xueliang Wu, Bing Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology China

Massive off-chip accesses in GPUs are the main performance bottleneck. We find that many writes are duplicate, and the duplication can be inter-dup and intra-dup. While inter-dup means different memory blocks are identical, and intra-dup means all the 4B elements in a line are the same. In this work, we propose a cache-assisted GPU memory deduplication architecture named CMD to reduce the off-chip accesses via utilizing the data duplication in GPU applications. CMD includes three key design contributions which aim to reduce the three kinds of accesses: (1) A novel GPU memory deduplication architecture that removes the intra-dup and inter-dup lines. We design several techniques to manage duplicate blocks, reducing massive off-chip writes. (2) We propose a cache-assisted read scheme to reduce the reads to duplicate data. When an L2 cache miss wants to read the duplicate block, if the reference block has been fetched to L2 and it is clean, we can copy it to the L2 missed block without accessing off-chip DRAM. As for the reads to intra-dup data, CMD uses the on-chip metadata cache to get the data. (3) When a cache line is evicted, the clean sectors in the line are invalidated while the dirty sectors are written back. However, most read-only victims are re-referenced from DRAM more than twice. Therefore, we add a full-associate FIFO to accommodate the read-only (it is also clean) victims to reduce the re-reference counts. Experiments show that CMD can decrease the off-chip accesses by 31.01%, reduce the energy by 32.78% and improve performance by 42.53%. Besides, CMD can improve the performance of memory-intensive workloads by 57.56%. © 2025 IEEE.

关键词： Cache memory

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EEG VMAC Toolbox: A User-Friendly Open-Source Toolbox for EEG Signals Visualization Manipulation Analysis and Classification

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Procedia Computer science 2025年 258卷 1062-1080页

作者： Gopal Chandra Jana Abhishek Karmakar Anupam Agrawal Interactive Technologies & Multimedia Research (ITMR) Lab Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj – 211015 Uttar Pradesh India Indian Institute of Information Technology Kalyani Department of Computer Science and Engineering Kalyani – 741235 West Bengal India

Electroencephalography (EEG) is a well-known, non-invasive method for monitoring and recording electrical activities of the human brain. Problem: EEG signal visualization, manipulation, analysis, and classification are essential for clinical experts, doctors, and researchers to make certain decisions. So, there is a need for a toolbox that can provide such functionalities with a user-friendly graphical user interface (GUI); availability may be commercial or open-source. Aim: This paper describes the proposed and developed open-source EEG VMAC Toolbox, which provides an interface with features including a series of state-of-the-art methods for EEG signal analysis. Method: The main menu options of EEG VMAC Toolbox are - 1) File, 2) Signal Visualization, 3) Filtering, 4) Signal Decomposition, 5) Feature Reduction, 6) Feature Extraction, 7) label, 8) Classification Models, and 9) Help. Each menu of the toolbox contains several functionalities. In addition to these nine menus, a file conversion option is available at the bottom of the toolbox. EEG VMAC Toolbox integrates all major state-of-the-art functionalities for EEG signal visualization, manipulation, analysis, and classification, which would be a valuable addition to the current literature. Results and Findings: The EEG VMAC Toolbox has been developed using Python programming language and tested over the CHB-MIT EEG Scalp EEG dataset, a benchmark dataset for seizure detection. So, this toolbox has a provision to bring psychologists, neuroscientists, clinical experts, and EEG researchers on the same platform to pursue extensive investigation and research for better reach.

关键词： Electroencephalogram (EEG) EEG toolbox EEG signal visualization toolbox EEG signal analysis tool Open-source toolbox Python EEG toolbox

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Enhancing Fairness and Performance: Scalable Hybrid Solutions for Class Imbalance in Big Data Analytics

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Procedia Computer science 2025年 258卷 1050-1061页

作者： Puneetha B H Manoj Kumar M V Prashanth B S Ajay Kumara M.A. Department of computer science and Business Systems Bapuji Institute of Engineering and Technology Davanagere Karnataka Department of Information Science and Engg. Nitte Meenakshi Institute of Technology Bangalore-560064 Karnataka Department of Computer Science University of North Carolina Wilmington USA

Recent advancements in handling class imbalance include hybrid approaches that combine data-level and algorithmic-level techniques. However, challenges such as computational efficiency and adaptability to dynamic datasets remain to be prominently addredssed. This research paper introduces novel hybrid techniques that combine both data-level and algorithmic-level approaches to address class imbalance in large datasets. These techniques are designed to improve class distribution and processing efficiency while enhancing prediction accuracy, particularly for minority classes. The proposed model applies K-means SMOTE for over-sampling the minority class and centroid-based undersampling for the majority class, followed by Classification using XGBoost. Evaluations on multiple datasets demonstrate significant improvements in precision, recall, AUC-ROC, and F1-score compared to traditional methods. In addition to enhancing robustness and fairness, the approach effectively addresses challenges related to scalability, data diversity, and dynamic data sources in big data analytics. The hybrid technique achieved a 10-20% improvement in key performance metrics, including precision, recall, F1-score, and AUC-ROC, across various UCI datasets such as Diabetes, Hepatitis, and German Credit, confirming the model’s superiority in handling class imbalance and improving overall Classification performance.

关键词： Class imbalance Undersampling Oversampling XGBoost

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Aspect category and sentiment polarity detection using a permutation language-based transformer model

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Procedia Computer science 2025年 258卷 4179-4189页

作者： Ashok Kumar Jayaraman Tina Ester Trueman Gayathri Ananthakrishnan Abirami Murugappan Erik Cambria Manikandan G Satanik Mitra HCLTech Engineering and R&D Services Chennai Tamil Nadu India Department of Computer Science University of the People California USA School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Information Science and Technology Anna University Chennai India College of Computing and Data Science Nanyang Technological University Nanyang Singapore School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Management BITS Pilani India

Aspect-based sentiment analysis (ABSA) performs fine-grained analysis on text to determine a specific aspect category and a sentiment polarity. Recently, machine learning models have played a key role in ABSA tasks. In particular, transformer-based pre-trained models have achieved promising results in natural language processing tasks. Therefore, we propose a permutation based XLNet fine-tuning model for aspect category detection and sentiment polarity detection. Our model learns bidirectional contexts via positional encoding and factorization order. We evaluate the proposed permutation language model on three ABSA datasets, namely, SentiHood, SemEval 2015, and SemEval 2016. Specifically, we studied the ABSA tasks in a constrained system with a multi-class environment. Our result indicates that the proposed permutation language model achieves a better result.

关键词： Aspect-based sentiment analysis permutation language model deep learning transformers multiclass classification

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