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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： George, Geomol Anusuya, S. Simats School of Engineering Department of Electronics and Communication Chennai India Simats School of Engineering Department of Computer Science Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

This study explores the use of the pre-trained EfficientNet-B3 model for classifying breast cancer images into benign, malignant, and normal categories. The model was finetuned with layer-specific adjustments, advanced regularization techniques, and comprehensive layer fine-tuning to enhance its ability to detect complex patterns. The results demonstrated exceptional performance, with benign cases achieving a precision of 98%, recall of 94%, and F1 score of 96%;malignant cases with precision of 90%, recall of 96%, and F1 score of 93%;and normal cases showing precision of 94%, recall of 96%, and F1 score of 95%. The model also achieved 99.84% training accuracy, 0.2964 training loss, and 95.25% test accuracy with a loss of 0.4333. These findings underscore the potential of this advanced EfficientNet-B3-based method to improve diagnostic accuracy and support timely, reliable treatment decisions in breast cancer care. © 2024 IEEE.

关键词： Ultrasonic imaging

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Optimizing Quality of Service in IoMT Through Fog Computing-Enabled Service Composition 13

Optimizing Quality of Service in IoMT Through Fog Computing-...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Anand, Dinesh Kaur, Avinash Singh, Parminder School of Computer Applications Lovely Professional University Punjab Phagwara India Guru Nanak Dev Engineering College Department of Computer Applications Punjab Ludhiana India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350380583

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, allowing for remote patient monitoring and personalized treatments. However, due to the resource limitations of medical devices, the vital nature of health- care data, and the necessity for real-time response, guaranteeing Quality of Service (QoS) in IoMT systems offers considerable issues. This research presents a technique for addressing these difficulties in IoMT by exploiting Fog Computing-Enabled Service Composition. The issue statement centers on the need to improve QoS in IoMT systems, which includes response time, reliability, and energy consumption. Existing solutions struggle to match the severe criteria of IoMT, where service deployment, orchestration, and data processing must be performed in a fast, energy-efficient, and reliable way. To address these concerns, we propose a set of techniques that combine Fog Computing with service composition to optimize QoS in IoMT. The framework is proposed to incorporate dynamics service orchestration, context ware decision-making and balance the load into the IoMT systems to allow service composition at network edge. The implementation of proposed technique is done and demonstrates how computing nodes are worked inside the IoMT system to enable dynamic service composition. Our results show improvements in the QoS parameters like response times, optimized energy consumption and enhanced reliability. © 2024 IEEE.

关键词： Fog Computing Internet of Medical Things Quality of Service Service Composition

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Parameterised Distance to Local Irregularity 19

Parameterised Distance to Local Irregularity

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19th International Symposium on Parameterized and Exact Computation, IPEC 2024

作者： Fioravantes, Foivos Melissinos, Nikolaos Triommatis, Theofilos Department of Theoretical Computer Science FIT Czech Technical University in Prague Czech Republic School of Electrical Engineering Electronics and Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959773539

A graph G is locally irregular if no two of its adjacent vertices have the same degree. The authors of [Fioravantes et al. Complexity of finding maximum locally irregular induced subgraph. SWAT, 2022] introduced and provided some initial algorithmic results on the problem of finding a locally irregular induced subgraph of a given graph G of maximum order, or, equivalently, computing a subset S of V(G) of minimum order, whose deletion from G results in a locally irregular graph;S is called an optimal vertex-irregulator of G. In this work we provide an in-depth analysis of the parameterised complexity of computing an optimal vertex-irregulator of a given graph G. Moreover, we introduce and study a variation of this problem, where S is a subset of the edges of G;in this case, S is denoted as an optimal edge-irregulator of G. We prove that computing an optimal vertex-irregulator of a graph G is in FPT when parameterised by various structural parameters of G, while it is W[1]-hard when parameterised by the feedback vertex set number or the treedepth of G. Moreover, computing an optimal edge-irregulator of a graph G is in FPT when parameterised by the vertex integrity of G, while it is NP-hard even if G is a planar bipartite graph of maximum degree 6, and W[1]-hard when parameterised by the size of the solution, the feedback vertex set or the treedepth of G. Our results paint a comprehensive picture of the tractability of both problems studied here. © Foivos Fioravantes, Nikolaos Melissinos, and Theofilos Triommatis.

关键词： Parameterization

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A Novel Deep Learning Based Approach for Detecting and Localization of Pulmonary Cancer 14

A Novel Deep Learning Based Approach for Detecting and Local...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Manoj, Diwakar Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

In the ever-evolving field of scientific diagnostics, the early diagnosis of pulmonary most cancers continues a key undertaking. This observation proposed a unique deep learning-primarily based approach, in particular using Generative Adversarial Networks (GANs), intending to modernise the identification and localization of pulmonary malignancies through scientific imaging. Our models, trained using a varied dataset, demonstrated a promising accuracy fee of 70% within the sample set, suggesting its ability to adeptly differentiate between malignant and non-malignant instances in scientific images. While the conclusions suggest a significant growth in lung cancer detection, also they highlight locations demanding in addition refinement. The balance of technological prowess and scientific significance, as reflected through criteria like sensitivity and specificity, remains a focus topic for future projects. The outcomes of this research are substantial. Beyond the on the spot discoveries, the take a look at emphasizes the transformational possibility of incorporating sophisticated AI approaches into healthcare. As the scientific network grapples with the difficulties of early cancer identification, gear like the one displayed in this study ought to usher in a new age in diagnostics-marked by accuracy, efficiency, and patient-centricity. In conclusion, this have a look at now not simplest adds a fresh diagnostic tool to the sector but moreover sets the way for future innovations within the confluence of AI and healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

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Recent Advances in computer science and Communications 2022年第5期15卷 693-699页

作者： Dewangan, Bhupesh Kumar Jain, Anurag Shukla, Ram Narayan Choudhury, Tanupriya Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Virtualization School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science & Engineering School of Engineering OP Jindal University Raigarh India

Background: In the cloud environment, the satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in a minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumers and service providers. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objectives: The research aims to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, the authors have proposed a new load-balancing algorithm named EBGAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and AntColony algorithm. A fitness value is assigned to all virtual machines based on the availability of resources and load on a virtual machine. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of the amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment. Conclusion: Through experimental results, it can be concluded that the proposed load balancing approach EB-GAP has outperformed other load balancing approaches based on relevant parameters. © 2022 Bentham science Publishers.

关键词： Virtual machine

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Corruption Robust Dynamic Pricing in Liner Shipping under Capacity Constraint 40

Corruption Robust Dynamic Pricing in Liner Shipping under Ca...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Hu, Yongyi Lit, Xueyan Wei, Xikai Shi, Yangguang Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering China School of Computer Science and Technology Shandong University China

ISBN: (纸本)9798350317152

The shipping industry has irreplaceable importance in international trade and commerce. How to dynamically price different containers has long been a hot topic due to its direct connection to the final revenue. Two critical observations have been made after a comprehensive survey within a top liner company, China Ocean Shipping Company (COSCO). (1) Each type of container carried on a liner ship has its maximum capacity. (2) The sales volume is occasionally subject to huge fluctuations due to rare uncontrollable factors, such as COVID. Based on the above two points and the liner routine's periodic nature, we model the dynamic pricing problem as an episodic MDP model integrating with both capacity constraints and adversarial corruption, named C3-MDP. To maximize the cumulative revenue in the C3-MDP setting, we propose a programming framework, Bonus-Exploration based Episodic Programming (BEEP). This framework can directly accommodate the linear programming algorithm to form the algorithm BEEP-LP, which provides the episode-wise greedy optimal strategy. Furthermore, a detailed regret analysis is provided, showing that BEEP-LP has a regret that is sublinear in the number of episodes. Combining deep techniques, we also present an approximation algorithm BEEP-DQN in the case of large state-action space to strike a balance between the running time and the performance. Abundant experiments based on real container sales data exhibit the rationality of C3-MDP and the effectiveness of BEEP. © 2024 IEEE.

关键词： Linear programming

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Speech Emotion Recognition with High Accuracy and Large Datasets using Convolutional Neural Networks 8

Speech Emotion Recognition with High Accuracy and Large Data...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Saranya, G. Tamilvizhi, T. Tharun, S.V. Surendran, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396638

Emotions are the best indicators of the actions of humans in advance. It is of great advantage in the current smart world. Prediction of these emotions can be able to sense the current mood of the driver and control the smart automobile accordingly and can be used in the case of chatting with customers using AI devices. This can be done by extracting the features including Mel-frequency cepstral coefficients (MFCCs) of the respective emotions and training the learning model using the classification algorithm Convolutional Neural Networks (CNN) and eventually the model can predict the emotion by comparing the newly retrieved features and the features of the training dataset and classify them accordingly. © 2023 IEEE.

关键词： Large dataset

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 IEEE.

关键词： Lung cancer

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Variational Adapter: Improving CLIP in Data-Imbalanced Scenarios

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5251-5264页

作者： Lu, Ziqian Liu, Mushui Yu, Yunlong Wang, Zhao Li, Xi Han, Jungong Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University Ningbo Innovation Center Ningbo China Zhejiang University College of Computer Science Hangzhou310027 China The University of Sheffield Department of Computer Science Sheffield United Kingdom

In this paper, we propose the Prompt-based Variational Adapter (PVA), a novel approach designed to fine-tune the pre-trained Vision-Language Models (VLMs) in data-imbalanced scenarios. Unlike existing methods that focus primarily on pairwise alignment of visual-text relationships during fine-tuning, PVA relaxes pairwise explicit constrains and emphasizes the harmonization of visual and text modality distributions, enhancing generalization and cross-modal understanding. To realize this harmonization, we develop two variational adapters, which are appended separately to the visual and text encoders. These adapters transform the feature embeddings into latent spaces that implicitly align with the corresponding modality distributions. We then adopt a divide-and-conquer strategy, dividing classes into data-abundant and data-limited sets to reduce prediction bias. Within each set, we independently fine-tune the models by incorporating both the model's original general knowledge and specialized knowledge gained from training samples. Extensive experiments across two data-imbalanced scenarios validate the superiority of our approach, establishing a new state-of-the-art on popular benchmarks. © 1991-2012 IEEE.

关键词： Visual languages

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'Optimizing Algorithmic Efficiency: Exploring Advanced Data Structures for High-Performance Computing' 3

'Optimizing Algorithmic Efficiency: Exploring Advanced Data ...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Rao, Lade Gunakar Nitya, Erupaka Devi Kusumba, Jyoshna Rajchandar, K. Narasimha Murthy, S. SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Telangana Hyderabad India Government Degree College Computer Science And Applications Telangana Warangal India Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350361391

This work explores advanced data structures as a means of optimizing algorithmic efficiency in high-performance computing. The search for faster and more scalable algorithms becomes essential as computing demands rise further. This paper explores state-of-the-art data structures intended to improve overall performance, memory usage, and computational speed. We hope to offer insights into the applicability and impact of various advanced data structures on high-performance computing tasks through a thorough review and analysis of them, including but not limited to self-balancing trees, hash tables, and advanced graph structures. The results of this study not only advance our theoretical knowledge of these data structures but also provide developers and researchers with useful advice on how to utilize algorithmic efficiency in quickly fully. It is important to remember that algorithmic urban planning is an interdisciplinary field that encompasses data science, computer science, urban planning, and many other fields. The goal of integrating algorithms and smart technologies is to build more efficient, sustainable, and liveable urban environments.[8]. © 2024 IEEE.

关键词： Advanced Data Structures Algorithmic Efficiency Computing (keywords) High-Performance Computing Optimizing

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