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Architectural framework for multiplayer cooperative cloud gaming to optimise quality of service

International Journal of Networking and Virtual Organisations 2024年第2期30卷 174-205页

作者： Srilakshmi, Nimmagadda Kannan, Naresh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-intensive tasks within the cloud. In this paper, an architectural model for supporting cooperative gaming towards multiplayer is proposed to improve the quality of service in terms of bandwidth and latency compared to existing architectural models. According to this study, the time it takes to change a video has grown by 95% compared to how well ad hoc mobile cloudlets and the cloud do at sharing videos. Although P-frames, I-frames, and B-frames account for 23%, 78%, and 94% of the resource consumption, density in intra-stream P-frames is also considered. Similarly, resource and CPU use based on skewness are effective compared to mobile devices' 95% faster video sharing and 77% shorter link delays. © 2024 Inderscience Enterprises Ltd.

关键词： Quality of service

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Enhancing NLP for Indic Languages with Limited Resources: A Study of Transformer Models for Translation and Summarization

Enhancing NLP for Indic Languages with Limited Resources: A ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Bhati, Abhivyakt Gupta, Ved Kumar Shah, Hare Ram Nagar, Rajesh Nimawat, Sunil Computer Science & Engineering-AIML IPS Academy Institute of Engineering & Science Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science SAGE University Indore India Computer Science & Engineering Indore India IQAC Cell Indore India Computer Science & Engineering IPS Academy Institute of Engineering & Science Indore India

ISBN: (纸本)9798331512965

Significant advancements have been made in natural language processing (NLP), especially in numerous languages. The vast linguistic diversity of India presents unique challenges for automated language processing tasks as Indian languages other than Hindi are marginalized in both cases;datasets which incur a vast toll on summarization models that require huge datasets to be trained on. This paper presents a comparative and comprehensive analysis of multiple transformer-based models which have been trained and/or fine-tuned for lesser-used Indic languages. Purely focusing on Natural Language Translation (NLT) and evaluating its efficiency in generating contextually accurate summaries for languages such as Bengali and Marathi. Models such as mT5, IndicBART, mBART and other multilingual NLP models will be benchmarked and assessed using the ROUGE score. This study highlights the model's strengths and weaknesses in handling the limitations summarization of low-resource Indic languages. The findings will contribute heavily to the development of NLP tools aimed at improving summarization inclusivity across India's diverse languages. © 2025 IEEE.

关键词： Translation (languages)

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Exploring ensemble optimized voting and stacking classifiers through Cross-validation for early detection of suicidal ideation

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 335-349页

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad Department of Computer Science Engineering National Institute of Technology Patna Bihar Patna India

Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for *** ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data - common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds"and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. © 2024 - IOS Press. All rights reserved.

关键词： Semantics

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An improved method for classifying depth-based human actions using self-adaptive evolutionary technique

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 157-173页

作者： Pareek, Preksha Thakkar, Ankit Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

Automatic Human Action Recognition (HAR) using RGB-D (Red, Green, Blue, and Depth) videos captivated a lot of attention in the pattern classification field due to low-cost depth cameras. Feature extraction in action recognition is an important aspect. As compared to Depth Motion Maps (DMM), Depth Motion Maps–Local Binary Pattern (DMM–LBP) provides compact representation of features. After extracting features using DMM–LBP, Principal Component Analysis (PCA) is used for dimensionality reduction. For classification task, randomly generated input weights of Extreme Learning Machine (ELM) can lead to non-optimal results. Therefore, in this paper, we have used an improved learning algorithm named Self-adaptive Differential Evolution (SaDE) ELM for action classification. In the proposed approach, DMM–LBP is used for feature extraction and SaDE–ELM is used for action classification. To evaluate strength of the proposed approach, experiments are performed on four public datasets, namely, MSRAction3D, MSRDaily Activity3D, UTD–MHAD, and MSRGesture3D. The proposed approach gives better accuracy compared to existing approaches Kernel ELM (KELM), l2-Collaborative Representation Classifier (l2-CRC), and Probabilistic CRC (ProCRC) methods. We have also presented statistical significance of results in the paper. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Local binary pattern

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HeRo: Heuristic-Based Routing in Payment Channel Networks

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 56-60页

作者： Mishra, Shruti Aggarwal, Virat Pal, Sujata Agarwal, Vidushi Indian Institute of Technology Ropar Department of Computer Science and Engineering Rupnagar140001 India

Payment channels support off-chain transactions by enhancing transaction speed and reducing fees in the main blockchain. However, the costs and complexity of the network increase as we increase the size of the network. To address these challenges, we propose Heuristic-Based Routing with Scheduling (HeRo) combining heuristic-based routing and scheduling techniques in Payment Channel Networks (PCNs). HeRo achieves a cost reduction of 32.71% and 73.08% compared with multi-charge PCN (MPCN-RP) and Dijkstra algorithms, respectively. © 2019 IEEE.

关键词： Network routing

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A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

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International Journal of Environmental Studies 2024年第2期81卷 661-674页

作者： Kalaivanan, K. Vellingiri, J. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water quality

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Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate

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Critical Reviews in Biomedical engineering 2024年第2期52卷 39-50页

作者： Wang, Xue Song, Zhili Zhu, Jianlin Li, Zhixiang School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov–Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences. © 2023 by Begell House, Inc.

关键词： Magnetic resonance imaging

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Diabetic retinopathy detection by fundus images using fine tuned deep learning model

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Multimedia Tools and Applications 2024年第39期83卷 86657-86679页

作者： Singh, Satya Prakash Gupta, Pankaj Dung, Rakshita Department of Computer Science & amp Engineering Birla Institute of Technology Ranchi Mesra India

This study employs transfer learning using a fine-tuned pretrained EfficientNetB0 convolutional neural network (CNN) model to accurately detect the various stages of Diabetic Retinopathy. The training process involved utilizing three datasets: Messidor, IDRiD (Indian Diabetic Retinopathy Detection), and APTOS 2019 Blindness Detection, which collectively encompassed 5,379 fundus images. Different types of processed fundus images were fed into the model to determine the optimal pre-processing approach for stage detection in Diabetic Retinopathy. The model was assessed on the original dataset with some augmentation techniques applied. According to the training data, the model achieved a maximum accuracy of 72%. However, converting the dataset to grayscale yielded an improved accuracy of 80%. Similarly, extracting the green, red, and blue channels individually resulted in accuracies of 72%, 76%, and 73% respectively. Notably, when the green channel extracted images underwent histogram equalization, the model achieved its highest accuracy of 83%. Furthermore, the application of a Sobel filter to the red channel images led to a maximum accuracy of 51%. Finally, to determine the effectiveness of each processed image type, sensitivity and specificity measures were compared. Among all the variations, the green channel extracted images with histogram equalization demonstrated superior performance in correctly identifying the respective classes, outperforming the other approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural network models

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A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 556-565页

作者： Aralikatti, Rajath C Pawan, S.J. Rajan, Jeny National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal575025 India

Deep neural networks have played a vital role in developing automated methods for addressing medical image segmentation. However, their reliance on labeled data impedes the practicability. Semi-Supervised learning is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods have gained interest due to their efficient learning procedures. They are, however, confined to data or network-level perturbations, negating the benefit of having both forms in a single framework. In light of this, we ask an intriguing but unexplored question: Can we have both network-level and data-level perturbation in the semi-supervised framework? To this end, we present a holistic approach that integrates data-level perturbation in the model pre-training stage, followed by implicit network-level perturbation in the fine-tuning stage. Furthermore, we incorporate networks with manifold learning paradigms throughout the training to facilitate the formation of robust data representations by ensuring local and global semantic affinities adhering to the theory of consensus. Notably, this may be the first attempt in the semi-supervised medical image segmentation archetype to use data and network-level perturbation with a model pre-training strategy. We extensively validated the efficacy of the proposed framework on three benchmark datasets, namely the Automated Cardiac Diagnosis Challenge, ISIC-2018, and Left Atrial Segmentation Challenge datasets, subjected to severely low-sampled labeled data. Notably, in ACDC (4%), ISIC-2018 (5%), and LA (6%) labeled cases, the proposed method outperforms the second-best method by 2.95%, 1.31%, and 0.71% in the Dice Similarity Metric. © 2023 IEEE.

关键词： Feature extraction

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