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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Sinha, Viraj Yadav, Richa Pretty Diana Cyril, C. Usha, G. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India

ISBN: (纸本)9798331528201

Lung Adenocarcinoma is a kind of NSCLC (non-small cell lung cancer) which is the most occurring form of lung cancer. It progresses in the epithelial cells that coat the Alveoli sacs of the lungs. Adenocarcinoma refers to cancer formed in mucus secreting cells, thus giving it its name. Patients suffering from this disease shows a combination of symptoms such as pneumonia, coughing up blood, fatigue and weight loss which covers a wide range of unrelated manifestation leading the disease to go unnoticed and often causing fatality. Just like any other cancer, Lung Adenocarcinoma desires early and accurate diagnosis. Current methods include various forms of imaging techniques such as PET scan, biopsy procedures and genetic testing. Diagnosis is followed by a plethora of treatment options such as Chemotherapy, Targeted therapy and Immunotherapy. Despite such efficacious treatment techniques, their effect entirely relies on the stage of diagnosis and its accuracy. Keeping such dearth in mind, we put forward a high resolution upsampled image generator which is packed with combinations of advanced neural network incorporating Machine learning model and techniques such as GANs (Generative Adversarial Network). This platform utilizes pathological data from several trusted sources to develop the AI model which as a result will increase the accuracy of biopsy diagnosis. Trained on the dataset that was carefully acquired and the initial 750 samples were augmented generating the final dataset of 4500+ image samples using the 'Augmentor' package in python, the model delivers superior results than current diagnosis methods while simultaneously improving biopsy performance. It guarantees model stability to variations which is achieved through minimizing loss function and parameter tuning while enhancing computational efficiency. This development may eventually result in filling the gap in current diagnosis methods by improving its results manifold. © 2024 IEEE.

关键词： Lung cancer

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Application-Oriented Cloud Workload Prediction:A Survey and New Perspectives

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Tsinghua science and technology 2025年第1期30卷 34-54页

作者： Binbin Feng Zhijun Ding Key Laboratory of Embedded System and Service Computing Ministry of EducationDepartment of Computer Science and TechnologyTongji UniversityShanghai 201804China Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji UniversityDepartment of Computer Science and TechnologyTongji UniversityShanghai 201804China Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Workload prediction is critical in enabling proactive resource management of cloud *** workload prediction is valuable for cloud users and providers as it can effectively guide many practices,such as performance assurance,cost reduction,and energy consumption ***,cloud workload prediction is highly challenging due to the complexity and dynamics of workloads,and various solutions have been proposed to enhance the prediction *** paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature *** existing surveys,for the first time,we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective,i.e.,application-oriented rather than prediction methodologies per ***,we first introduce the basic features of workload prediction,and then analyze and categorize existing efforts based on two significant characteristics of cloud applications:variability and ***,we also investigate how workload prediction is applied to resource ***,open research opportunities in workload prediction are highlighted to foster further advancements.

关键词： cloud computing workload prediction resource management artificial intelligence for IT operations(AIOps)

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Predicting Hospital Length of Stay Using Light Gradient Boosting Machine Regression 7th

Predicting Hospital Length of Stay Using Light Gradient Boos...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Suchithra, M. Shashwat, Kumar Shoaib Khan, Mohammed Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9783031699856

Length of stay (LoS) in a hospital is an important metric in the healthcare management system, with profound implications for resource allocation, patient outcomes, and cost reduction. This paper reviews the literature on approaches for predicting hospital length of stay with a R2 score of 96.14 per cent using Light Gradient Boosting Machine regressor. We critically assess the merits and limitations of various methods and propose a unified framework for the generalized prediction of length of stay. Our framework includes investigating the types of routinely collected data and recommendations for robust knowledge modelling. © IFIP International Federation for Information Processing 2024.

关键词： Adaptive boosting

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A Survey on Logic-Locking Characteristics and Attacks

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Journal of The Institution of Engineers (India): Series B 2024年第4期105卷 1073-1087页

作者： Subbiah, Karthik Chinnathevar, Sujatha Electronics and Communication Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India Computer Science and Engineering SSM Institute of Engineering and Technology Tamilnadu Dindigul India

Integrated circuits (ICs) are ubiquitous and a crucial component of electronic systems, from satellites and military hardware to consumer devices and cell phones. The computing system’s foundation of trust is the IC. Most semiconductor businesses are shifting to fabless manufacturing and outsourcing to foundries worldwide as integrated circuit feature sizes continue to decrease. That puts the design business at risk for several things, such as unauthorized overproduction, resale on the black market, and illegal copying brought on by intellectual property theft. Logic locks offer one solution in which the chip’s actual functioning is "locked" with a key only known to the inventor. The design will only function as intended if specific keys are pressed. Unlocking overproduced chips should be impossible for supply chain attackers since designers open them after manufacturing them. Logical locks against the risk of overproduction are the main subject of this research. We examine current locking systems, define features based on crucial processing, and identify commonalities and discrepancies between the employed attacker models. This research paper is intended to assist scientists, IP distributors, and SoC developers in rapidly investigating and comprehending the most recent technologies that should be considered and analyzed for additional research on logic-locking techniques. © The Institution of Engineers (India) 2024.

关键词： Locks (fasteners)

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Adaptive Attention Mechanisms Based Multicriteria Recommender Systems for Improved User Preference Discovery

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SN computer science 2025年第4期6卷 1-16页

作者： Rajput, Ishwari Singh Tewari, Anand Shanker Tiwari, Arvind Kumar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science & Engineering Kamla Nehru Institute of Technology Sultanpur (UP) India

Personalized recommender systems are becoming more popular to reduce the issue of information overload. It is also observed that the recommendations provided by multi-criteria recommender system (MCRS) are more accurate and precise in comparison to conventional single-criteria recommender systems. MCRS incorporates customers’ preferences based on multiple criteria while recommending any item. Recent research has shown that MCRS can efficiently use implicit and explicit input across various dimensions to make exact recommendations across multiple domains. Due to use of multiple criteria, existing MCRS methods often struggle with effectively aggregating and interpreting these criteria, making it hard to accurately determine user preferences. In this article, we introduce a novel adaptive attention mechanism that dynamically assigns weights to each criterion based on user preferences. Each criterion is initially weighted based on its importance to a user. An attention mechanism is used to understand the exact user’s preference for each criterion. Later, the nearest-neighbor method finds users with similar interest to predict unknown ratings which is further used for Top-N recommendations. The performance of the proposed approach (AM-MCRS) is validated on Yahoo! movies multi-criteria dataset having 1716 unique users and 965 different movies and a comparative analysis is performed with existing single and multi-criteria recommender systems. The results clearly show that the AM-MCRS methodology outperform baseline methods, achieving the highest precision (0.9412), F-score (0.9343), accuracy (0.929), and coverage (0.9407), while also observing a significant reduction in MAE (0.7623) and RMSE (0.9145). These findings highlight the capability of AM-MCRS to improve recommendation accuracy and dependability across various domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Attention mechanism Deep learning Dynamic user preference Multi-criteria recommender system Nearest-neighbor

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EmoSense: Deep Analysis of Real-Time Facial Expression for Multi-Class Emotions 2

EmoSense: Deep Analysis of Real-Time Facial Expression for M...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Sharvesh, S. Kaileshwar, M. Arthi, B. Jothikumar, C. Srm Institute of Science and Technology Computing Technologies School of computing Kattankulathur Chennai India

ISBN: (纸本)9798350372892

This study explores the efficacy of CNNs in classifying images into predefined categories, highlighting advancements in automated image recognition technology. The project aims to develop a robust CNN model that can accurately identify and classify images based on their content, which has significant implications for fields requiring automated image analysis. A sequential CNN architecture featuring layers of convolution, max pooling, and dropout, complemented by fully connected layers for feature learning and classification, developed and validated using TensorFlow and Keras. The model was trained and tested on a curated dataset comprising diverse images categorized into seven distinct classes, preprocessed to normalize image sizes and pixel values to enhance model training efficiency. 8L-CThe model used in the research includes 2L-CNN, 3L-CNN, and 4L-CNN architecture are used. In that, better accuracy of 62.2 % for 3-layer architecture received. The CNN demonstrated high accuracy and generalizability in image classification, proving capable of handling complex pattern recognition tasks, underscoring the potential of CNNs in transforming image processing applications. © 2024 IEEE.

关键词： Emotion Recognition

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Recent advances in 3D Gaussian splatting

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Computational Visual Media 2024年第4期10卷 613-642页

作者： Tong Wu Yu-Jie Yuan Ling-Xiao Zhang Jie Yang Yan-Pei Cao Ling-Qi Yan Lin Gao Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Tencent AI Lab Beijing 100089China VAST Beijing 100000China Department of Computer Science University of CaliforniaSanta BarbaraCA 93106USA

The emergence of 3D Gaussian splatting(3DGS)has greatly accelerated rendering in novel view *** neural implicit representations like neural radiance fields(NeRFs)that represent a 3D scene with position and viewpoint-conditioned neural networks,3D Gaussian splatting utilizes a set of Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished by rasterizing Gaussian ellipsoids into *** from fast rendering,the explicit representation of 3D Gaussian splatting also facilitates downstream tasks like dynamic reconstruction,geometry editing,and physical *** the rapid changes and growing number of works in this field,we present a literature review of recent 3D Gaussian splatting methods,which can be roughly classified by functionality into 3D reconstruction,3D editing,and other downstream *** point-based rendering methods and the rendering formulation of 3D Gaussian splatting are also covered to aid understanding of this *** survey aims to help beginners to quickly get started in this field and to provide experienced researchers with a comprehensive overview,aiming to stimulate future development of the 3D Gaussian splatting representation.

关键词： 3D Gaussian splatting(3DGS) radiance field novel view synthesis 3D editing scene generation

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Enhancing Text Classification Performance using Stacking Ensemble Method with TF-IDF Feature Extraction 5

Enhancing Text Classification Performance using Stacking Ens...

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5th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Parmar, Mahesh Tiwari, Akhilesh Madhav Institute of Technology & Science Computer Science and Engineering Gwalior India Madhav Institute of Technology & Science Information Technology Gwalior India

ISBN: (纸本)9798350395235

This study explores various techniques for extracting features from unstructured textual data and evaluates their effectiveness in text classification. Categorizing text into appropriate categories is a crucial task in natural language processing (NLP) known as text classification. The most commonly used feature extraction methods for this task are TF-IDF and NLP techniques. For this research, a dataset of Amazon customer reviews extracted from Amazon's website is used. The dataset contains 3071 reviews, along with star ratings, review dates, variants, and feedback for various Amazon Alexa products. To enhance classification accuracy, the study proposes a stacking ensemble method that combines the predictions of Random Forest Classifier and Gradient Boosting Classifier models with TF-IDF feature weighting. In addition, the experiment compared the proposed method's effectiveness with other methods by evaluating various feature extraction techniques, such as NGrams, Bag-of-Words (BoW), TF-IDF, Word2vec, and Glove, along with several classifiers, including Logistic Regression (LR), Decision Tree (DT), Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), k-nearest neighbors (KNN), and Random Forest (RF). According to the results of the experiment, the Stacking Ensemble (RF+GBM-Stacking) approach performed better than other methods. The proposed technique demonstrated promising results with an accuracy, precision, recall, and F1 score of 0.85, 0.82, 0.87, and 0.84, respectively. These findings suggest that the technique has the capability to improve text classification performance significantly. © 2024 IEEE.

关键词： Decision trees

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Improving long-tail classification via decoupling and regularisation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 62-71页

作者： Shuzheng Gao Chaozheng Wang Cuiyun Gao Wenjian Luo Peiyi Han Qing Liao Guandong Xu School of Computer Science and Technology Harbin Institute of TechnologyShenzhenChina Peng Cheng Laboratory ShenzhenChina Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of TechnologyShenzhenChina Faculty of Engineering and Information Technology University of Technology SydneySydneyNew South WalesAustralia

Real-world data always exhibit an imbalanced and long-tailed distribution,which leads to poor performance for neural network-based *** methods mainly tackle this problem by reweighting the loss function or rebalancing the ***,one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle *** this paper,the authors shed light on the significance of the angle distribution in achieving a balanced feature space,which is essential for improving model performance under long-tailed ***,it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature *** tackle these challenges,the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components:classifier norm(*** magnitude of the classifier vector),feature norm(*** magnitude of the feature vector),and cosine similarity between the classifier vector and feature *** this way,the authors analyse the change of each component in the training process and reveal three critical problems that should be solved,that is,the imbalanced angle distribution,the lack of feature discrimination,and the low feature *** from this analysis,the authors propose a novel loss function that incorporates hyperspherical uniformity,additive angular margin,and feature norm *** component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature *** authors conduct extensive experiments on three popular benchmark datasets including CIFAR-10/100-LT,ImageNet-LT,and iNaturalist *** experimental results demonstrate that the authors’loss function outperforms several previous state-of-the-art methods in addressing the challenges posed by imbalanced and longtailed datasets,t

关键词： computer vision image classification long-tailed data machine learning

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A novel dominating set and centrality based graph convolutional network for node classification

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Multimedia Tools and Applications 2025年第14期84卷 12979-13004页

作者： Garg, Neeraj Garg, Sneha Jadeja, Mahipal Meena, Yogesh Kumar Gopalani, Dinesh Chauhan, Ganpat Singh Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Jaipur Rajasthan Jaipur302017 India Department of Information Technology School of IT Manipal University Jaipur Rajasthan Jaipur302034 India

To learn and analyze graph-structured data, Graph Neural Networks (GNNs) have emerged as a powerful framework over traditional neural networks, which work well on grid-like or sequential structure data. GNNs are particularly designed to record dependencies and relations in large and complicated networks such as social networks, molecular structures, citation networks and recommendation systems. The main idea behind GNN is the message-passing mechanism that allows sharing of information between nodes and edges in a graph network, enabling the model to learn about graph structures and perform well even with limited training data. GNNs have three different architectures: Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Model (GAT) and other hybrid models. In this paper, we have proposed novel GCN models namely dominating Set with Degree Centrality based GCN (DS_DC_GCN) and dominating Set with Betweenness Centrality based GCN (DS_BWC_GCN) model, for node classification task. In the traditional GNN models, training nodes are selected randomly. In our proposed models, we first identify a subset of the nodes with the help of a dominating set (D) of the underlying network. Then we consider final training nodes (say D′) from set D based on the centrality scores (degree or betweenness). We give preference to nodes having high centrality scores for training. To evaluate the performance of the proposed models, we have used three standard citation network datasets - Cora, Citeseer and Pubmed. Our models obtain improved node classification performance as compared to other state-of-the-art-methods. The consideration of high centrality nodes of dominating set in training helps to improve the feature availability in the underlying computational graphs, which in turn improves learning and hence, model performance. These models also have the potential to offer better results for other network tasks, including link prediction and community detection. © The Author(s), under

关键词： Graph neural networks

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