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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Pandiyaraju, V. Sunil, Meghana Shravya, V. Venkatraman, Shravan Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Anna University Department of Information Science and Technology Chennai India

ISBN: (纸本)9798331534950

In medical imaging, semantic segmentation is essential because it can precisely locate and isolate regions of interest, such lesions or tumours, from intricate anatomical systems. Deep learning (DL) has led to a substantial evolution in segmentation approaches, which have improved medical diagnostics' accuracy and efficiency. The performance of six cutting-edge models is assessed in this paper: LinkNet, U-Net with ResNet50, LinkNet with ResNet50, FPN Net, Simple U-Net, and SegNet-Transformer across four segmentation tasks - polyp detection, lung lesion segmentation from Computed Tomography (CT) scans, breast lesion detection from ultrasound images, and Segmenting brain tumours with magnetic resonance imaging (MRI). With results like 0.9618 for brain tumour segmentation and 0.9483 for lung lesion segmentation, SegNet-Transformer consistently obtained the highest Dice Coefficient across all tests, demonstrating superior ability in capturing intricate boundaries. Other models, such as U-Net with ResNet50, performed competitively with Dice scores of 0.9375 for lung lesion segmentation and 0.9472 for brain tumors but required slightly more memory and longer training times. This study underscores that model performance varies across different tasks, revealing specific strengths and weaknesses. Ultimately, while SegNet-Transformer demonstrates robustness, the choice of model should be informed by the unique requirements of each segmentation task, balancing accuracy, efficiency, and computational demands. © 2024 IEEE.

关键词： Semantic Segmentation

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Agent-based Simulation and Analysis of Infrastructure-as-Code Process to Build and Manage Cloud Environment 37

Agent-based Simulation and Analysis of Infrastructure-as-Cod...

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37th ECMS International Conference on Modelling and Simulation, ECMS 2023

作者： Mycek, Andrzej Grzonka, Daniel Tchórzewski, Jacek Department of Computer Science Cracow University of Technology Cracow Poland Institute of Computer Science AGH University of Science and Technology Cracow Poland

ISBN: (纸本)9783937436807

Widespread cloud systems present new challenges time and time again. An essential element of such environments is their management. The Infrastructure as Code model has been gaining popularity for some time. In work presented here, we have proposed an agent-based approach to process execution within the Infrastructure as Code approach and have performed several numerical experiments. The work also includes an original formal agent model of the system. The results obtained allow us to develop trade-offs regarding computational demand and utilization. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.

关键词： Computational methods

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Interpretable Automatic Rosacea Detection with Whitened Cosine Similarity 17

Interpretable Automatic Rosacea Detection with Whitened Cosi...

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17th IEEE International Conference on computer Research and Development, ICCRD 2025

作者： Yang, Chengyu Liu, Chengjun New Jersey Institute of Technology Department of Computer Science NewarkNJ United States

ISBN: (纸本)9798331531881

According to the National Rosacea Society, approximately 16 million Americans suffer from rosacea, a common skin condition that causes flushing or long-Term redness on a person's face. To increase rosacea awareness and to better assist physicians to make diagnosis on this disease, we propose an interpretable automatic rosacea detection method based on whitened cosine similarity in this paper. The contributions of the proposed methods are three-fold. First, the proposed method can automatically distinguish patients suffering from rosacea from people who are clean of this disease with a significantly higher accuracy than other methods in unseen test data, including both classical deep learning and statistical methods. Second, the proposed method addresses the interpretability issue by measuring the similarity between the test sample and the means of two classes, namely the rosacea class versus the normal class, which allows both medical professionals and patients to understand and trust the results. And finally, the proposed methods will not only help increase awareness of rosacea in the general population, but will also help remind patients who suffer from this disease of possible early treatment, as rosacea is more treatable in its early stages. The code and data are available at https://***/chengyuyang-njit/ICCRD-2025. The code and data are available at https://***/chengyuyang-njit/ICCRD-2025. © 2025 IEEE.

关键词： Population statistics

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Ridership Trend Analysis and Explainable Taxi Travel Time Prediction for Bangalore Using e-Hailing Data 7th

Ridership Trend Analysis and Explainable Taxi Travel Time Pr...

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7th International Conference of Transportation Research Group of India, CTRG 2023

作者： Srivastava, Nishtha Gohil, Bhavesh N. Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (纸本)9789819610365

The accurate prediction of travel time in India is essential for efficient transportation planning and management, especially in busy cities with heavy traffic. Recent research in transportation engineering, computer science, and data analytics has demonstrated the effectiveness of data-driven approaches in predicting travel times more accurately. Accurate road travel time prediction can benefit both transportation service providers and travelers by improving trip planning, reducing travel times, and increasing operational efficiency. Driven by a vision to make a significant contribution to this expansive realm of research, this paper suggests a methodology for forecasting taxi travel times for Bangalore city using data from Uber Movement to increase the effectiveness and dependability of urban transport networks. The model combines regression approaches with spatiotemporal data analysis to capture complicated geographical and temporal trends. The model adds pertinent data, such as weather from Wunderground, and is trained using Uber Movement’s data on cab trips. This report provides a thorough trend analysis of Bangalore city’s commuters. With a Mean Absolute Error (MAE) of 103 s on the Uber data, the ExtraTree regressor outperforms the other regression methods, according to a comparison of their findings. Based on Uber Movement data, the suggested algorithm can accurately predict the time needed to travel between places in Bangalore city. Furthermore, a comprehensive evaluation is undertaken to analyze the suitability of eXplainable Artificial Intelligence (XAI) in effectively addressing the interpretability of black box machine learning models for predicting travel time. The XAI methods, SHAP and LIME are used to evaluate the interpretability of the trained models. As part of future work, this model can be applied to other cities and improve traffic management by taking proactive measures in advance. © The Author(s), under exclusive license to Springer Nature Sin

关键词： Travel time

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Insights on Improving Reasoning and Increasing Capabilities in Large Language Models 6th

Insights on Improving Reasoning and Increasing Capabilities ...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Krisshna, S. Varun Sendhil, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

Large Language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. These models have been reported to have "sparks" of artificial general intelligence owing to their very attractive properties such as high generalization and performance in unseen data. These models have demonstrated strong capabilities in generalization and performance on novel data, leading some to speculate they may contain early sparks of artificial general intelligence. However, true AGI remains an unachieved goal requiring breakthroughs beyond current techniques. LLMs have poor mathematical reasoning capabilities, inherit biases present in training data, and can hallucinate and deliver false information. In this chapter, we discuss several methods that improve the current state-of-the-art models through the use of external tools such as web browsing, better prompting techniques, scaling, using Reinforcement learning (RL) and tree-based search algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Optimizing Power Consumption for Business Networks through Cooperative Communication in Mixed Wireless Environments

Optimizing Power Consumption for Business Networks through C...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Sanjay, C.P. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the evolving landscape of business networks, the imperative to optimize power consumption has become paramount, particularly within the context of mixed wireless environments. This study introduces a novel approach to enhancing energy efficiency through cooperative communication strategies, tailored for heterogeneous networks that comprise a mix of cellular, Wi-Fi, and other wireless technologies. The core proposition of this research lies in leveraging the inherent diversity and redundancy in mixed wireless environments to facilitate energy-efficient communication. By employing advanced cooperative communication techniques, such as relay-assisted transmission and network coding, the proposed method significantly reduces the power required for data transmission and reception across various network segments. The methodology encompasses a comprehensive framework that models the network as a unified system, enabling the identification and exploitation of opportunities for power savings without compromising communication reliability or throughput. A set of sophisticated algorithms underpins the proposed framework, ensuring optimal decision-making regarding routing, resource allocation, and mode selection in real-time. The effectiveness of the proposed approach is validated through rigorous simulation studies, which demonstrate substantial improvements in power efficiency compared to conventional communication strategies. The results highlight the potential of cooperative communication in achieving sustainable and energy-efficient business networks, paving the way for greener and more cost-effective operations in mixed wireless environments. © 2024 IEEE.

关键词： Business Networks Cooperative Communication Energy Efficiency Mixed Wireless Environments Resource Allocation

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Artificial Intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach 3

Artificial Intelligence, Intelligent Manufacturing, and Sust...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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Fusing Multimodal Streams for Improved Group Emotion Recognition in Videos 27th

Fusing Multimodal Streams for Improved Group Emotion Recogni...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Kumar, Deepak Dhamdhere, Piyush Raman, Balasubramanian Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee247667 India

ISBN: (纸本)9783031783043

Recognizing social cues and emotions is vital for navigating daily interactions, understanding emotions in conversations, interpreting body language in meetings, and supporting friends in difficult situations. This work focuses on analyzing group-level emotions in videos captured in natural settings, marking an attempt at multimodal group-level emotion analysis. Automatic group emotion recognition is pivotal for understanding complex human-human interactions. Group emotion recognition in videos presents several challenges because existing work predominantly focuses either on individual emotion recognition or group emotion analysis in static images. To address this challenge, we introduce a deep-learning-based multimodal fusion model that integrates diverse modalities, including audio, video, and scene. Feature extraction employs advanced models like TimeSformer for video description and wav2vec2.0 for audio analysis. All the experiments are conducted on the VGAF dataset. Our key findings include: (1) Multimodal approaches outperform their unimodal counterparts, (2) Experimental results confirm the superior performance of proposed approach compared to benchmark methods on the given dataset, and (3) There is a strong correlation between modalities and respective emotions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Emotion Recognition

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An Asynchronous Multi-Cluster Network System for AUV Swarm Communication and Positioning: Design and Trial

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IEEE Internet of Things Journal 2025年第13期12卷 25393-25406页

作者： Wang, Guolin Zhu, Jifeng Hu, Shuibo Peng, Zheng Cui, Jun-Hong Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen518110 China Smart Ocean Technology Co. Ltd Shenzhen518057 China

This paper proposes a novel multi-cluster network system for autonomous underwater vehicle (AUV) swarm communication and positioning, addressing challenges of low throughput, high end-to-end delay, and significant ranging errors in traditional asynchronous underwater acoustic networks. The proposed system introduces a novel hybrid Medium Access Control (MAC) protocol, which combines intra-cluster Time Division Multiple Access (TDMA) with inter-cluster Frequency Division Multiple Access (FDMA) to enhance channel utilization. Additionally, the system introduces an adaptive time-offset estimation method based on network data exchange and the Ultra-Short Baseline Moving Ranging (USBL-MR) algorithm, which significantly improves network throughput and reduces delay and improves positioning accuracy. Field trials demonstrate that the system achieves a throughput of 398.3 bps with an end-to-end delay of 21.2 s. Furthermore, the ranging Root Mean Square Error (RMSE) has been reduced from 7.87 to 2.42 m, marking a 14% improvement in positioning accuracy. © 2014 IEEE.

关键词： Autonomous underwater vehicles

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Maximizing Weighted Dominance in the Plane 21st

Maximizing Weighted Dominance in the Plane

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21st International Colloquium on Theoretical Aspects of computing, ICTAC 2024

作者： Akram, Waseem Saxena, Sanjeev Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur208016 India

ISBN: (纸本)9783031770180

Let P be a set of n weighted points, Q be a set of m unweighted points in the plane, and k a non-negative integer. We study the k-maxDominance problem, where the objective is to find a subset Q′⊆Q with size at most k such that the sum of the weights of the points in P dominated by at least one point in Q′ is maximized. A point q in the plane dominates another point p if and only if x(q)≥x(p) and y(q)≥y(p), and at least one inequality is strict. An algorithm for the k-maxDominance problem can be used to solve problems previously examined in scheduling, graph optimization, and database systems. We present a simple algorithm solving the problem in O(km2+nlogm) time and O(n+m) space. The algorithm is faster than the existing one when m=o(n). Additionally, we provide another solution to the problem with O(kmin{n+m,nk+m2}logm) time and O(n+m) space. These bounds are the same or better than those of the existing solution (the bounds are better when m=o(n)). The expected number of skyline points is θ(logn), a term much less than n. To obtain the second solution, we design a general technique that can be used to reduce the number of points n in P to min{n,m2} in any algorithm for the k-maxDominance problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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