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DICNet: A Novel CNN Model based on DenseNet with Interleaved Convolutional Block Attention Module for the Classification of Breast Cancer Histopathology Images

DICNet: A Novel CNN Model based on DenseNet with Interleaved...

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Sant longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur India Punjabi University Department of Computer Science Patiala India

ISBN: (纸本)9798350364040

Histopathology image analysis is crucial for the accurate diagnosis of breast cancer (BC), which is the most prevalent cancer among women. The prompt secondary opinions based on automated analysis can assist pathologists, reduce workload, and minimize interobserver variability. This paper introduces the DICNet, a novel convolutional neural network (CNN) architecture based on DenseNet with interleaved convolutional block attention module (CBAM) to classify the BC histopathology images. Specifically, the interleaved attention mechanism utilizes inter-channel and inter-spatial relationships among convolutional features to accentuate more informative and discriminative features. Furthermore, the pooled responses from multiple convolutional layers with varying spatial resolutions are concatenated to obtain a multi-scale representation. The classification performance of the proposed model is evaluated on the publicly available benchmark BreaKHis dataset with four magnification levels. The proposed DICNet model achieves an overall best classification accuracy of 94.02 % at the 40× magnification level. Additionally, DICNet demonstrates enhanced classification performance at every magnification level, and significantly outperforms existing state-of-the-art approaches. © 2024 IEEE.

关键词： Image classification

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Enhancing Image Segmentation in the Marine Atmosphere for Time-Sensitive Contextualisation using Convolutional Long Short-Term Memory 2

Enhancing Image Segmentation in the Marine Atmosphere for Ti...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Reethika, A. Rajeshwari, R. Vijayalakshmi, S. Malathy, S. Gnanasaravanan, S. Suresh Kumar, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Information Technolog KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Information Technology Sri Ranganathar Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798331541217

The work investigates autonomous surface vehicle obstacle avoidance and detection using computer vision techniques. It recognizes boundaries in marine environments by using the temporal context from video feeds from unmanned surface vehicles. The study shows how successful LSTM cells are in the CNN topologies that are currently in use, emphasizing how they might perform better. Nevertheless, datasets and network training techniques are not perfect. The work also uses a benchmarking approach to compare new network configurations. © 2024 IEEE.

关键词： Image segmentation

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A Study of Deep Learning Algorithms in Sentiment Analysis of Diverse Domains 1

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International Health Informatics Conference, IHIC 2022

作者： Tanmaiyee, Jutla Vasamsetti, Ramya Sri Sushma Pallavi, Vangala Sai Jagdish, Mukta Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9789811990908

ISBN: (纸本)9789811990892

Analysis of sentiment and opinion mining is the space of examination that investigates considerations, discernments, appraisals, practices, and feelings of individuals from the composed language on products, administration associations, human issues, occasions, points, and different traits. The main agenda of this paper is to depict the sentiments of people using deep learning algorithms and tools by classification methodology in categorizing polarity into positive, negative, and neutral. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

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Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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Sparse and Dense: Learning Confusion Representation Network for 3D Action Recognition

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IEEE Internet of Things Journal 2025年第12期12卷 21240-21254页

作者： Hou, Xinran Geng, Pei Li, Tianchen Li, Yalin Li, Yan Lyu, Lei School of Information Science and Engineering Shandong Normal University Jinan250358 China School of Information Engineering Shandong Management University Jinan250100 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

In the field of the Internet of Medical Things (IoMT), the demand for Human Action Recognition (HAR) is growing. Due to the limitations of portability and privacy of traditional sensors, many endeavors have made significant progress in 3D skeleton-based action recognition. However, existing methods ignore potential higher-order semantic information between joints and fail to perceive rapidly changing dynamic details, resulting in frequent confusion of actions with similar motion trajectories. To alleviate this issue, we propose a novel learning confusion representation network (LCR-Net). Specifically, a progressive feature enhancement module is first designed to utilize self-attention to gradually aggregate lower-order features to higher-order features, emphasizing the relative movement between body parts. Second, we design the enhanced spatiotemporal convolution to explore the potential spatio-temporal dependencies between joints by adding a mask matrix and an attention fusion mechanism. To further perceive the spatiotemporal relationships in subtle changes, we divide the interactive sparse-dense pathways at different spatio-temporal resolutions and enhance the complementary information between the two pathways through feature interaction. Finally, the frequency excitation learning module is proposed to efficiently learn the importance of different frequencies by cross-channel modeling, promoting the compactness of actions within classes and the separability of confusion actions. In addition, the lightweight LCR-Net+ is achieved through model compression optimization to meet the deployment requirements of IoT systems. Comprehensive experiments conducted on three public datasets (NTU-RGB+ D60&120, NW-UCLA) demonstrate the superior performance of our model. © 2025 IEEE. All rights reserved.

关键词： Joints (anatomy)

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Envisioning Educational Success Through Advanced Analytics and Intelligent Performance Prediction 10

Envisioning Educational Success Through Advanced Analytics a...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sureshkumar, T. Charanya, J. Kumaresan, T. Rajeshkumar, G. Kavin Kumar, P. Anuj, B. Nehru Institute of Technology Department of Information Technology Coimbatore641 105 India Department of Computer Science and Engineering Erode638 401 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam638 401 India Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India

ISBN: (纸本)9798350353068

The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final exam, taking into account a range of socio-economic and academic factors. Every student's journey is different, and it is influenced by various factors such as their socioeconomic background, academic abilities, and personal experiences. To evaluate their performance, we use various machine learning algorithms such as KNN, Naive Bayes, SVM, AdaBoost, and Naive Bayes - and assess their performance using key metrics such as ROC curve, confusion matrix, precision, recall, and F1 score. The F1 score offers a balanced assessment of precision and recall, offering insights into overall model accuracy. Precision and recall focus specifically on the model's ability to correctly identify positive instances and capture all actual positive instances, respectively. ROC curves demonstrate the balance between the rates of correctly identifying true positives and incorrectly identifying false positives, allowing for a nuanced assessment of classifier performance. The confusion matrix further breaks down true positives, true negatives, false positives, and false negatives, enhancing our understanding of algorithmic effectiveness. Through a comprehensive comparison of these classification methods, our goal is to identify the most accurate approach for predicting student outcomes, thus contributing valuable insights to educational institutions and policymakers aiming to implement effective strategies for student success and academic improvement. © 2024 IEEE.

关键词： Students

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Food Recognition and Calorie Measurement Using Machine Learning 1

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Vasudha, Maganti Rashmi, D. Mahalakshmi Jain, B.A. Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

This study is one of many that investigate the relationship between determining the nutritional ingredients in food and calculating the calories using data analysis utilizing machine learning techniques. Due to the availability of multifood photos, which must be cropped before processing, the Indian food recipes database is used for the research. The study uses a large dataset of various food photos to train state-of-the-art deep convolutional neural networks (CNNs) to recognize and categorize distinct food items with an amazing 99.89% accuracy. This study’s applicability spans several sectors in addition to food recognition, including calorie measurement, meal planning services, and nutritional monitoring systems. The solution is widely available to a wide range of users thanks to a user-friendly web interface. The system’s 99.89% accuracy in food detection and calorie measurement demonstrates its dependability and distinguishes it from competing options. Its ability to improve individual health, fight obesity, and encourage healthy eating habits makes it a vital tool in today’s health-conscious culture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Large datasets

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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