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Brain tumor segmentation and classification using transfer learning based CNN model with model agnostic concept interpretation

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Multimedia Tools and Applications 2025年第5期84卷 2509-2538页

作者： Nancy, A. Maria Maheswari, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai632014 India

In recent decades, brain tumors have been regarded as a severe illness that causes significant damage to the health of the individual, and finally it results to death. Hence, the Brain Tumor Segmentation and Classification (BTSC) has gained more attention among researcher communities. BTSC is the process of finding brain tumor tissues and classifying the tissues based on the tumor types. Manual tumor segmentation from is prone to error and a time-consuming task. A precise and fast BTSC model is developed in this manuscript based on a transfer learning-based Convolutional Neural Networks (CNN) model. The utilization of a variant of CNN is because of its superiority in distinct tasks. In the initial phase, the Magnetic Resonance Imaging (MRI) brain images are acquired from the Brain Tumor Image Segmentation Challenge (BRATS) 2019, 2020 and 2021 databases. Then the image augmentation is performed on the gathered images by using zoom-in, rotation, zoom-out, flipping, scaling, and shifting methods that effectively reduce overfitting issues in the classification model. The augmented images are segmented using the layers of the Visual-Geometry-Group (VGG-19) model. Then feature extraction using An Attribute Aware Attention (AWA) methodology is carried out on the segmented images following the segmentation block in the VGG-19 model. The crucial features are then selected using the attribute category reciprocal attention phase. These features are inputted to the Model Agnostic Concept Extractor (MACE) to generate the relevance score between the features for assisting in the final classification process. The obtained relevance scores from the MACE are provided to the max-pooling layer of the VGG-19 model. Then, the final classified output is obtained from the modified VGG-19 architecture. The implemented Relevance score with the AWA-based VGG-19 model is used to classify the tumor as the whole tumor, enhanced tumor, and tumor core. In the classification section, the proposed

关键词： Magnetic resonance imaging

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Marine Life Ecosystem Analysis Based on Climate Change Detection Using Deep Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 545-554页

作者： Rebecca, B. Sandhya, A. Pokkuluri, Kiran Sree Kanishka, Gunipati Shaik, Khasimbee Krishna, B. Murali Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology & Science Madanapalle India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The surrounding environmental and climatic conditions have a significant impact on the utilisation of ecosystem services for recreational purposes. Climate change poses a threat to future natural leisure opportunities because of this reliance. The way society functions is being challenged by climate change and significant adaptation will probably be needed to deal with future changes in weather patterns. Machine learning (ML) methods have advanced to the point that they are now being offered as a tool to support climate studies, in addition to being the source of advances in other fields of study. This research proposes a novel technique in marine life ecosystem–based climate change detection using deep learning model. Input is collected as climate change detection–based marine life ecosystem and processed for noise removal with normalisation. Then this data has been segmented and classified using watershed semantic fuzzy component analysis with quantile shrinkage convolutional regression model. The experimental analysis has been carried out in terms of training accuracy, specificity, recall, AUC and NSE. Suggested technique achieved training accuracy 98%, specificity 96%, NSE 45%, recall 95% and AUC 97%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change detection Convolutional regression Deep learning Fuzzy component analysis Marine life ecosystem

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Maximizing Learning Outcomes while Minimizing: Costs: A Cost-Effective Approach to M-Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第11期18卷 4-14页

作者： Suresh Kumar, B. Hemawathi, M.S. Dhanasekaran, P. Kaliappan, Vishnu Kumar Sivaganesan, D. Department of Electronics and Communication Engineering The Kavery Engineering College Tamil Nadu Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibil- ity offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and per- sonalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without com- promising the quality of educational delivery. Ultimately, the findings highlight the transfor- mative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. © 2024 by the authors of this article.

关键词： Contrastive Learning

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Optimizing abnormality detection in fundus images with Triplet-OS and orchard search optimization model

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Neural Computing and Applications 2024年第36期36卷 23181-23194页

作者： Venkatraman, K. Hemalatha, R. Radhika, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Thandalam Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Department of EEE Sathyabama Institute of Science and Technology Chennai India

The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given

关键词： Medical imaging

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Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest

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Multimedia Tools and Applications 2024年 1-26页

作者： Yarrarapu, Manasa Leelavathy, Narkedamilly Haritha, Dasari Department of Computer Science and Engineering Jawaharlal Nehru Technological University Andhra Pradesh Kakinada533003 India Department of Computer Science and Engineering Godavari Institute of Engineering and Technology Andhra Pradesh Rajahmundry533296 India Department of Computer Science and Engineering University College of Engineering Jawaharlal Nehru Technological University Andhra Pradesh Kakinada533003 India

Now-a-days, the generation of videos has increased dramatically due to the quick growth of multimedia and the internet. The need for effective ways to store, manage, and index the massive numbers of videos has become imperative due to this expansion. As a result, a method needs to be proposed that collects only the necessary data from the original recording. In computer vision, Video summarization is a significant task, and its primary goal is to give a quick summary of the video by removing irrelevant information and capturing key frames from the video. Many approaches have developed over the last several decades, using the most recent deep neural network architectures that represent the current state-of-the-art. Our method involves extracting vital key frames from the input video using the MobileNetSSD model, which is well-known for its efficient recognition and localization of objects of interest. These highlighted frames are essential in creating a detailed video summary. Furthermore, a method of temporal analysis is applied to guarantee that the summary accurately reflects the relevant events in the order in which they occurred, contributing to a coherent and meaningful representation of the information. We evaluated the proposed approach on TV Sum and SUM me video datasets, comparing the results against cutting-edge video summarization techniques. Our approach works effectively to produce clear and meaningful video summaries. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(Information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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REGAC: Multi-Class Offensive Content Identification Using Graphical Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 931-952页

作者： Chinivar, Sneha Roopa, M.S. Arunalatha, J.S. Venugopal, K.R. Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering Bangalore University K R Circle Karnataka Bengaluru India Karnataka Bengaluru India

Online offensive behaviour continues to rise with the increasing popularity and use of social media. Various techniques have been used to address this issue. However, most existing studies consider offensive content identification as a binary or ternary problem, disregarding the potential for multi-class classification of derogatory content. In this work, we propose RoBERTa Embedding-based Graphical Approach Classifier (REGAC), which aims to designate the identified offensive content into varied classes,like age, gender, ethnicity, religion, and others to understand what exact qualities the bully usually targets in their victim so that pertinent measures can be taken to battle them. Additionally, the models’ efficiency is evaluated using an unbalanced dataset to identify types of offensive content. The word embeddings are generated for a balanced dataset using RoBERTa embedding model to bring out the best-fitting vectors. These vectors are then input into traditional Machine Learning algorithms (SVM, KNN, Logistic Regression, Random Forest, and XGBoost) and graph-based algorithms (Graph Convolution Network (GCN), GraphSAGE, and Graph Attention Network (GAT)) for fine-grained categorization of offensive textual content. The experimental results demonstrate the efficiency of combating social media’s offensive content of the proposed work with higher Precision, Recall, F1-Score, and Accuracy compared with most state-of-art approaches. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Graph embeddings

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

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computer Systems science & engineering 2023年第4期45卷 715-736页

作者： Kandasamy Sellamuthu Vishnu Kumar Kaliappan Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407Tamil NaduIndia

Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to *** residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly *** residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in *** is an obligation to provide a warning about this amount of pesticide use in *** technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential *** analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and ***,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

关键词： Pesticide contamination complex event processing recurrent neural network Q learning multi residual level and contamination level

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Complex preference analysis: a score-based evaluation strategy for ranking and comparison of the evolutionary algorithms

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Soft Computing 2025年第4期29卷 1967-1980页

作者： Sarkar, Debojyoti Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar788010 India

In this paper, an evaluation strategy is proposed for evaluation of optimization algorithms, called the Complex Preference Analysis, that assesses the efficiency of different evolutionary algorithms by considering multiple performance metrics concurrently across diverse dimensions and generating a single score for ranking. The proposed strategy allows for a thorough evaluation by assigning distinct priorities in the form of weights, to various performance parameters. The algorithm’s performances on each performance parameter are captured and the best and worst performances in each performance category are identified. The deviation of each algorithm from the best and worst performances in the respective performance category is formulated to generate a single score for each of the algorithms for comparison. The flexibility to adjust weights provides a valuable tool for customizing evaluations according to individual needs and preferences. The proposed evaluation strategy is studied by running evaluations on nine evolutionary algorithms based on their performance over five performance indicators on six datasets of the Travelling Salesman Problem. Conducting Complex Preference Analysis on the different evolutionary algorithms reveals distinctive patterns in their performance, with some algorithms such as Simulated Annealing, Tabu Search, Bat Algorithm, Genetic Algorithm, etc. consistently demonstrating superior results across multiple Travelling Salesman Problem instances, while other algorithms such as Ant Colony Optimization, Teaching Learning Based Optimization, etc. rank lower. The algorithms’ performances on individual performance parameters, when reflected on the final rank of the algorithms, generated based on the collective impact of all the parameters, elucidate the efficiency of the proposed evaluation strategy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Optimization algorithms

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