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Text Filtering Insignificant Word (TFIW) Readability Framework Model to Explain NLP in Sentiment Analysis Using Reinforcement Machine Learning 2

Text Filtering Insignificant Word (TFIW) Readability Framewo...

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandakatla, Rajesh Kumar, V Dhilip Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Dept. Computer Science and Engineering Chennai India BVRIT HYDERABAD College of Engineering for Women Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

Reading of a text in a given sentence have lot of parameters to consider for expressing an opinion on a given text to conclude nature of the text. The data which need to be formed in the form of data sets based on specific constrains. The Natural Language Processing (NLP) will always presume that Text readability made as the considerable measure for better round spread of any newspaper. The data related to these documents must be collected and store them in data sets. During this process of measuring text readability at the granular level of the text reading is a cumbersome task. This will always implicate a problem of machine readability of the text and explainability of the text taken from the documents mentioned above. In this context human readability will act an important role in predicting the text reading at granular level where the text-based sentiment analysis have been predicted through a machine learning model. The text reading has been evaluated in order to improve the readability through explainable system. The explainable systems will capture and explain the predictions of text reading. In this technical work an explainable system Text Filtering Insignificant Word (TFIW) is introduced where it improves the textual reading at the granule level based on sentiments by prediction the words which need to be filtered. This will improve the explainability of text and text reading and improve the fast text and explainability of the document. © 2024 IEEE.

关键词： Reinforcement learning

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Advancements in Distributed Deep Learning: Federated Learning, AutoML Integration, and Beyond

Advancements in Distributed Deep Learning: Federated Learnin...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Kavitha, T. Manikandan, S.P. Patil, Bhimaraya Patil, Anita Dept. of Computer Science and Engineering New Horizon College of Engineering Bangalore India CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg. Bangalore India Dept. of Information Science and Engg. City Engineering College Bengaluru India

ISBN: (纸本)9798331505134

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication overhead. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research. © 2024 IEEE.

关键词： Federated learning

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A computer science Perspective on Digital Sustainability: A Thematic Literature Review 22

A Computer Science Perspective on Digital Sustainability: A ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Olaiya, Kelvin Salomoni, Paola University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331508050

This paper explores the evolving concept of digital sustainability, emphasizing its growing importance as digital technologies become essential to addressing global sustainability challenges. Through a thematic literature review focused on computer science, we analyze papers from Scopus-indexed journals to identify key themes in the discourse on digital sustainability. Additionally, we map the identified topics to the traditional three-pillar model of sustainable development, noting areas of alignment and divergence. The study offers insights into how digital sustainability is framed within the computer science literature and its implications for future research. © 2025 IEEE.

关键词： Green development

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Attaining Peak Performance through an Interference-Aware Joint Channel Assignment Scheme for Wireless Mesh Networks 15

Attaining Peak Performance through an Interference-Aware Joi...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Mulla, Tahseen Shikalgar, Anisa Dept. of Computer Engineering Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Sion Mumbai India Dept. of Computer Science & Engineering Walchand College of Engineering Sangli India

ISBN: (纸本)9798331300579

Wireless Mesh Networks (WMNs) have witnessed a remarkable surge in popularity across a diverse spectrum of applications, encompassing military networks and public wireless infrastructures. These networks, emerging as the cornerstone of numerous wireless communication systems, offer expansive wireless connectivity to a substantial user base. Despite these advancements, the inherent challenge of interference among nodes poses a significant hurdle, resulting in diminished performance for WMNs. The prevalence of interference within WMNs is a critical concern, adversely affecting their operational efficiency. The need to address this challenge is underscored by the imperative to optimize WMN performance. A pivotal step in this direction involves the development of channel assignment algorithms that are not only efficient but also effective in mitigating interference issues. Efforts to enhance WMN performance through advanced channel assignment strategies are paramount. By devising algorithms that intelligently allocate channels and manage interference, we can significantly elevate the overall efficiency of WMNs. This imperative undertaking is fueled by the overarching goal of ensuring seamless connectivity and robust performance in the face of escalating demands from various application domains. In summary, the increasing prominence of WMNs in diverse sectors necessitates a proactive approach in overcoming performance limitations. Through the implementation of sophisticated channel assignment algorithms, we can fortify WMNs, ensuring their continued role as a reliable backbone for wireless communication systems. © Grenze Scientific Society, 2024.

关键词： Wireless mesh networks (WMN)

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An Optimized Grid Computing based Feature Selection approaches from Brain Image dataset using Pretrained models 1

An Optimized Grid Computing based Feature Selection approach...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Nayak, Sradhanjali Biswal, Pradyut Kumar Mishra, Pravakar Pradhan, Sateesh Kumar Niis Institute of Business Administration Dept. of Computer Science Bhubaneswar India Iiit Bhubaneswar Dept. of Etc Bhubaneswar India Niis Institute of Business Administration Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Dept. of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9798350355093

The outcome of simulated experiments expressed the efficiency of grid-based algorithm in the domain of image processing in the context of Brain MRI Images for Tumor Detection. It provides a faster approach in detecting object and achieving higher rate of detection in comparison with existing algorithms for the similar purpose. The foremost goal of this work is improving the rate of detection by using grid based feature fusion approach with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Scalp Swarm Optimization Algorithm. This technique produces efficient result due to hybrid fusion of gridded Images and optimization for exhaustive and precise image analysis. In the initial phase Each image is segmented into 4 grids. Pretrained models like Inception-V2, Resnet 50, Mobnetv2, VGG-16 are used to extract features before and after the images are gridded. The extracted features are analysed with classifiers such as Decision tree, Support vector Machine, Multiplayer and Self Attention Transformer that not only chooses the best set of features as well as best feature extractor. Among all these models the VGG-16 gives best result in terms of Accuracy, sensitivity, specificity and F-score. Hence VGG-16 is utilized as evaluation functions in the optimization Algorithms. The performance of the approach is measured by the values produced against the terms as precision, Recall, F-score, and accuracy rate. In the first phase of iteration, features extracted from each grid are assigned with initial weight. Optimization algorithms are used for optimizing the weight values in the combined features to bring best accuracy ratio. Grid based segmentation of Image stabilize the computation time thereby reduces the time complexity. Weighted sum Scalp Swarm Optimisation assigns best weighted values to segmentation parameters to get maximum performance. The experimental outcomes proves the efficiency of the proposed strategy. © 2024 IEEE.

关键词： Genetic algorithms

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Gupta, Mridul Yadav, Devansh Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350388534

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life. © 2024 IEEE.

关键词： Contrastive Learning

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset 2

Implementation of Pruned and Quantized Semantic Segmentation...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts. © 2024 IEEE.

关键词： Semantic Segmentation

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Joint Power and Channel Allocation in Fading Channels Using Deep Reinforcement Learning 2

Joint Power and Channel Allocation in Fading Channels Using ...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798331519056

This paper investigates joint power and channel allocation under fading communication channels using Deep Reinforcement Learning (DRL). To this end, we design a DRL to allocate dynamic resources with different network condition properly and achieve the spectral efficiency improvement while reducing latency to meet lower bound in cost function as well keeping target fairness. Results show that our DRL-based approach can achieve better spectrum efficiency and latency than traditional algorithms while at the same time, sustaining a higher value of fairness index. In addition, it is observed that the DRL method also offers both adaptability (with fast adaptation to network changes) and robustness in performance but at a higher computational expense. Our results illustrate that the DRL-based technique may serve as a strong, scalable candidate in future wireless systems up to several orders of magnitude better than existing resource allocation schemes. © 2024 IEEE.

关键词： Deep reinforcement learning

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Few-Shot Learning with Feature Pairing and Mean Discrepancy

Few-Shot Learning with Feature Pairing and Mean Discrepancy

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Singh, K.K. Hima Bindu, K. Nit Andhra Pradesh and Rgukt Nuzvid Dept. of Computer Science and Engineering India Nit Dept. of Computer Science and Engineering AP India

ISBN: (纸本)9798350333602

Few-Shot Learning (FSL) is a sub-area of machine learning which mainly deals with data where there is a scarcity of training supervised samples. Few shot learning (FSL) more closely resembles the human brain in comparing new concepts to others based on prior experience rather than identifying it exactly. FSL aims to generalize the model across the tasks (in meta learning) opposed to the classical supervised learning which generalizes across the data points. In general the FSL models may suffer from underfitting because of scarcity of supervised samples and at the same time it causes overfitting as it is likely to memorize task specific features of the training set. This work aims to reduce such problems and is presented as a metric based model 'Few Shot Learning with Feature Pairing and Mean Discrepancy' (FL-FPMD). As the title suggests, feature pairing is one among various data augmentations. It is observed that flip augmentation is more suitable in the context of pairing the features within the given task. Memorizing task specific features is reduced by incorporating the discrepancy of mean distributions of the query and the support embedding in the loss function. The training and the evaluation is performed at the miniImageNet dataset and the results indicate that the proposed model outperforms the state-of-the-art models of similar complexity. © 2023 IEEE.

关键词： Embeddings

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies 1

An Approach of Fog Computing and Edge Computing for Computin...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Yadav, Devansh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350365016

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems. © 2024 IEEE.

关键词： Fog computing

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