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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sreekar, K. Aswin, V. Narayanan, Vishnu Thangavel, Senthil Kumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Ashland University Department of Mathematics and Computer Science OH United States

ISBN: (纸本)9798350370249

In this age of technology, with digital devices being accessible by almost everyone, have made our lives much better. With the advent of Machine Learning and advanced algorithms at our tips, the applications are immensurate. Such an application can be found in the health-care sector. The need for better and accessible health-care is of immense precedence. In this paper, we employed Machine Learning algorithms like Logistic Regression and other state of the art techniques to predict diabetes, given patient data. Diabetes is an incessant condition that is affecting millions worldwide, every year. Early detection of diabetes and prompt diagnosis is crucial. We have addressed that using a chat-bot to predict diabetes, which we believe is a portable and accessible solution for first-level assessment. We have employed PIMA Indian Diabetes Dataset into our chat-bot, providing interactive medical advice, and enhancing patient engagement, using Telegram API, resulting in an accuracy of 77%. © 2024 IEEE.

关键词： Electronic health record

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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COST OPTIMIZATION OF OFFSHORE WIND FARM COMBINATION WITH REVERSIBLE SOLID OXIDE CELL SYSTEM PRODUCING HYDROGEN USING THE PYPSA POWER SYSTEM MODELLING TOOL 7

COST OPTIMIZATION OF OFFSHORE WIND FARM COMBINATION WITH REV...

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7th Offshore Energy and Storage Symposium, OSES 2023

作者： Guichard, Jessica Rawlinson-Smith, Robert Greaves, Deborah COAST Engineering Research Group School of Engineering Computing and Mathematics University of Plymouth Plymouth DevonPL4 8AA United Kingdom

ISBN: (纸本)9781839539596

In the context of reaching the net zero carbon target, the UK has set an ambitious target of having a green hydrogen production capacity of 5 GW by 2030. As part of the EPSRC-funded project on high efficiency reversible solid oxide cells (rSOC) for the integration of offshore renewable energy (ORE) using hydrogen, eight scenarios where hydrogen is combined with offshore renewable energy were identified. A model using the PyPSA power system modelling tool combined with a sensitivity study, investigated optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities under various combinations of infrastructure cost, rSOC system efficiencies, and electricity prices for one of the scenarios. Preliminary results for a 600 MW wind farm situated 60 km from shore combined with offshore hydrogen production illustrate the impact of electricity price on decision-making in energy dispatch and on optimization of infrastructure of an ORE-rSOC system. Results indicate that high electricity price fluctuations call for large amounts of hydrogen production and storage capacity. Further refinement of input data would make this approach a promising decision-making tool for the use in the design of an ORE-rSOC system. © The Institution of engineering & Technology 2023.

关键词： Hydrogen production

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Integer multiple quantum image scaling based on NEQR and bicubic interpolation

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Chinese Physics B 2024年第4期33卷 259-273页

作者：蔡硕周日贵罗佳陈思哲 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China College of Merchant Marine Shanghai Maritime UniversityShanghai 201306China School of Mathematics and Computational Science Shangrao Normal UniversityShangrao 334001China

As a branch of quantum image processing,quantum image scaling has been widely ***,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been *** this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral ***,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT ***,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic ***,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these *** simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.

关键词： quantum image processing image scaling bicubic interpolation quantum circuit

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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Enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis on Chest X-Rays Using Data-Efficient Image Transformers

Enhancing Diagnostic Accuracy of Drug-Resistant Tuberculosis...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Mnyambo, Joan Jonathan Aly, Amir Zhou, Shang-Ming Wei, Yinghui Mullin, Stephen Ifeachor, Emmanuel School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom School of Nursing and Midwifery University of Plymouth Plymouth United Kingdom Peninsula Medical School University of Plymouth Plymouth United Kingdom

Tuberculosis is an infectious disease with increasing fatalities around the world. The diagnosis of the disease is a major challenge to its control and management due to the lack of adequate diagnostic tools, contributing significantly to the prevalence of drug-resistant tuberculosis. Convolutional Neural Network (CNN) models have recently been developed to detect drug-resistant tuberculosis by analyzing chest radiograph images from the TB portal, but the classification results are low. This is because CNNs struggle to capture complex global and overlapping features in medical imaging, such as chest radiographs of drug-resistant tuberculosis. In contrast, transformers excel in these areas by utilizing self-attention mechanisms that detect inherent subtle and long-range dependencies across images. In this study, we used a pretrained data-efficient image transformer (DEiT) model to enhance the diagnosis of drug-resistant tuberculosis and differentiate it from drug-sensitive tuberculosis. The new model achieved an AUC of 80% in the detection of drug-resistant tuberculosis, an improvement of 13% in the AUC compared to current CNN models using data from the same source. The bootstrap significance test shows that the difference in AUCs is statistically significant. The results of the study can help healthcare providers improve drug-resistant tuberculosis diagnostic accuracy and treatment outcomes. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Disease control

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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Streaming Algorithms for Non-Submodular Maximizationon the Integer Lattice

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Tsinghua Science and Technology 2023年第5期28卷 888-895页

作者： Jingjing Tan Yue Sun Yicheng Xu Juan Zou School of Mathematics and Information Science Weifang UniversityWeifang 261061China Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Mathematical Sciences Qufu Normal UniversityQufu 273165China

Many practical problems emphasize the importance of not only knowing whether an element is selectedbut also deciding to what extent it is selected,which imposes a challenge on submodule *** this study,we consider the monotone,nondecreasing,and non-submodular maximization on the integer lattice with a *** first design a two-pass streaming algorithm by refining the estimation interval of the optimal *** element,the algorithm not only decides whether to save the element but also gives the number of ***,we introduce the binary search as a subroutine to reduce the time ***,we obtain a one-passstreaming algorithm by dynamically updating the estimation interval of optimal ***,we improve the memorycomplexity of this algorithm.

关键词： integer lattice non-submodular streaming algorithm cardinality constraint

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Global Attention-Guided Dual-Domain Point Cloud Feature Learning for Classification and Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5167-5178页

作者： Li, Zihao Gao, Pan You, Kang Yan, Chuan Paul, Manoranjan Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing211106 China George Mason University Creativity and Graphics Lab FairfaxVA22030 United States Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia

Previous studies have demonstrated the effectiveness of point-based neural models on the point cloud analysis task. However, there remains a crucial issue on producing the efficient input embedding for raw point coordinates. Moreover, another issue lies in the limited efficiency of neighboring aggregations, which is a critical component in the network stem. In this paper, we propose a global attention-guided dual-domain feature learning network (GAD) to address the above-mentioned issues. We first devise the contextual position-enhanced transformer (CPT) module, which is armed with an improved global attention mechanism, to produces a global-aware input embedding that serves as the guidance to subsequent aggregations. Then, the dual-domain K-nearest neighbor feature fusion (DKFF) is cascaded to conduct effective feature aggregation through novel dual-domain feature learning which appreciates both local geometric relations and long-distance semantic connections. Extensive experiments on multiple point cloud analysis tasks (e.g., classification, part segmentation, and scene semantic segmentation) demonstrate the superior performance of the proposed method and the efficacy of the devised modules. © 2020 IEEE.

关键词： Semantics

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RSSI ESTIMATION FOR CONSTRAINED INDOOR WIRELESS NETWORKS USING ANN

arXiv

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arXiv 2024年

作者： Arif, Samrah Khan, M. Arif Rehman, Sabih ur School of Computing Mathematics and Engineering Charles Sturt University Australia

In the expanding field of the Internet of Things (IoT), wireless channel estimation is a significant challenge. This is specifically true for low-power IoT (LP-IoT) communication, where efficiency and accuracy are extremely important. This research establishes two distinct LP-IoT wireless channel estimation models using Artificial Neural Networks (ANN): a Feature-based ANN model and a Sequence-based ANN model. Both models have been constructed to enhance LP-IoT communication by lowering the estimation error in the LP-IoT wireless channel. The Feature-based model aims to capture complex patterns of measured Received Signal Strength Indicator (RSSI) data using environmental characteristics. The Sequence-based approach utilises predetermined categorisation techniques to estimate the RSSI sequence of specifically selected environment characteristics. The findings demonstrate that our suggested approaches attain remarkable precision in channel estimation, with an improvement in MSE of 88.29% of the Feature-based model and 97.46% of the Sequence-based model over existing research. Additionally, the comparative analysis of these techniques with traditional and other Deep Learning (DL)-based techniques also highlights the superior performance of our developed models and their potential in real-world IoT applications. © 2024, CC BY-NC-ND.

关键词： Channel estimation

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