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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Balakrishnan, T. Suresh Karthikeyan, L. Gururama Senthilvel, P. Ebenezar, U. Samson Preetham, B.S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha University Department of Computer Science and Engineering Chennai India Ms Ramaiah Institute of Technology Department of Medical Electronics Bangalore India

ISBN: (纸本)9798331538538

This has led to several challenges in attaining semantic compatibility of the large multi-center health care data systems obtained across different systems. IVERSITY of representation of data, formats, and terminologies make it difficult to match up and analyze data. In the light of these challenges, there is a proposed solution called the OntologyDriven Semantic Interoperability Framework which applies definitive ontologies of the domain to achieve the interlinking of health data. It thus facilitates data sharing across the different institutions, and also ensures that the data is consistent and can be interpreted in the same way. This component of our approach is called the graph-based query analysis where graph database and query optimization techniques are used to improve the efficiency of the data retrieval process. Our approach also effectively represents healthcare entities and their relationships as a knowledge graph to support human-controllable queries and various semantic reasoning. Moreover, for an automated matching of the different data sets to a common conceptual schema we employ machine learning techniques to automatically map various sources of input to the created ontology. The proposed framework facilitates the access of data, enables researchers and clinicians to make more-informed decisions and helps in big science in the biomedical field. Experimental analysis prove that arsenal is efficient in comparing to traditional relational databases in terms of execution of queries' time and their accuracy. Our work reveals that using the approaches based on ontology can effectively eliminate the issues of semantic mismatch and will contribute to the development of an intelligent environment for healthcare. © 2025 IEEE.

关键词： eHealth

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A Hybrid Approach on Lexical Indian Sign Language Recognition 1

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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Konoor, Athul Mathew Padmavathi, S. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

India is one of the countries with an increasing deaf population. The needs and problems of deaf communities are not adequately addressed. The majority of the previous approaches on Indian Sign Language (ISL) Recognition are based on convolutional neural networks (CNNs), whose performance depends on factors like lighting and the clothing of the sign language performer. The facial key points along with arm and body key points play a significant role in interpreting the ISL. The proposed approach identifies the facial key points and other body key points using the OpenPose model to generate pose video, which removes information unfavorable to CNN networks. The generated pose video is used by the pre-trained CNN model to extract spatial features and recurrent neural networks (RNNs) to extract temporal features necessary for ISL interpretation. We have used pre-trained models like Inception V3, DenseNet121 and MobileNetV2 to extract spatial features along with GRU, LSTM and bidirectional LSTM networks to extract the temporal features. Our model using the pre-trained MobileNetV2 model combined with GRU gave an accuracy of 99%. We also extended our approach using a combination of pose videos with background information. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Sparse Rev-Shift Coded Modulation with Novel Overhead Bound

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China Communications 2023年第10期20卷 17-29页

作者： Mingjun Dai Wanru Li Chanting Zhang Xiaohui Lin Bin Chen School of Computer Science and Technology Kashi UniversityKashi 844000China College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518000China

To provide reliability in distributed systems,combination property(CP)is desired,where k original packets are encoded into n≥k packets and arbitrary k are sufficient to reconstruct all the original ***-and-add(SA)encoding combined with zigzag decoding(ZD)obtains the CP-ZD,which is promising to reap low computational complexity in the encoding/decoding process of these *** densely coded modulation is difficult to achieve CP-ZD,research attentions are paid to sparse coded *** drawback of existing sparse CP-ZD coded modulation lies in high overhead,especially in widely deployed setting m

关键词： distributed system shift-and-add zigzag decoding sparse coded modulation

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ColorMamba: Towards High-quality NIR-to-RGB Spectral Translation with Mamba 16

ColorMamba: Towards High-quality NIR-to-RGB Spectral Transla...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Zhai, Huiyu Jin, Guang Yang, Xingxing Kang, Guosheng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science Hong Kong Baptist University Hong Kong

Translating NIR to the visible spectrum is challenging due to cross-domain complexities. Current models struggle to balance a broad receptive field with computational efficiency, limiting practical use. Although the Selective Structured State Space Model, especially the improved version, Mamba, excels in generative tasks by capturing long-range dependencies with linear complexity, its default approach of converting 2D images into 1D sequences neglects local context. In this work, we propose a simple but effective backbone, dubbed ColorMamba, which first introduces Mamba into spectral translation tasks. To explore global long-range dependencies and local context for efficient spectral translation, we introduce learnable padding tokens to enhance the distinction of image boundaries and prevent potential confusion within the sequence model. Furthermore, local convolutional enhancement and agent attention are designed to improve the vanilla Mamba. Moreover, we exploit the HSV color to provide multi-scale guidance in the reconstruction process for more accurate spectral translation. Extensive experiments show that our ColorMamba achieves a 1.02 improvement in terms of PSNR compared with the state-of-the-art method. Our code is available at https://***/AlexYangxx/ColorMamba/. © 2024 H. Zhai1, G. Jin1, X. Yang2 & G. Kang1.

关键词： RGB color model

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Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 738-749页

作者： Babu C. N, Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran Nagaraju, Komatigunta Maheswari, G. Uma Kiran, Ajmeera Department of Computer Science and Engineering BMS Institute of Technology & amp Management Karnataka Bengaluru India Department of Artificial Intelligence and Data Science GITAM (Deemed to be University) Hyderabad India Department of CSE School of Technology GITAM (Deemed to Be University) Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P India Department of Computer Science and Engineering RMK College of Engineering and Technology Pudhuvoyal Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial neural networks Deep learning techniques Deforestation rate LiDAR image processing Temperature rise analysis

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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

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computer Modeling in engineering & sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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Ventricular Vision: AI-Driven Heart Disease Detection with CNN-SVM-Based Classification

Ventricular Vision: AI-Driven Heart Disease Detection with C...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Gupta, Richa Garg, Navin Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This study builds upon the CNN-SVM model as a sound diagnostic method for identifying five distinct heart diseases. We utilize the effectiveness of the deep learning procedures of various CNNs and the ability of the SVMs, which, in the end, might be customized and optimized to solve particular cardiac diseases. Two classifications examined are atrioventricular block, arrhythmia, bradycardia, tachycardia, second-degree heart block, and annexation. The appraisal of CNN-SVM model performance accomplished through various parameters was detailed. The precision of the correct classes arrayed from 94.89% to 97.00%, the recall ranged between 93.70% and 98.91%, and the F1-scores were also high, extending from 94.87% to 97.25%. A significant enhancement in the precision and accuracy of the model was registered, which demonstrated statistics close to 0.99 for Class 1, 0.98 for Classes 2 and 3, and 0.99 for Classes 4 and 5, highlighting its high performance. Various types of averages were used to evaluate the model's effectiveness. The superiority of the macro average in accuracy, recall, and F1-score was demonstrated. It is calculated for all classes equally. The results were 96.14%, 96.20%, and 96.16%. We applied the weighted average (taking into account class imbalance) and got 96.15% for the straight as well as for the recall and the F1-score. The micro average, which selects the best from among all classes with identical scores, is kept at 96.15% for all tips, showing a well-suited classifier. © 2024 IEEE.

关键词： Convolutional neural networks

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Range Optimal Dummy Location Selection Based on Query Probability Density 2

Range Optimal Dummy Location Selection Based on Query Probab...

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2nd International Conference on Cloud Computing, Big Data Application and Software engineering, CBASE 2023

作者： Fan, Hengxu Wang, Yongli Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350331448

Location semantics are helpful for some location-based services in urban life, such as location retrieval and recommendation. The location semantics in these services may include a very rich and diverse range of location tags and comments. In addition, when using dummy location selection to protect location privacy, if a user initiates a service request in a location with a zero-query probability, the generated location set will only have a zero-query probability in the dummy location set. Attackers can easily obtain the user's true location. Most dummy location selection algorithms do not fully consider the uneven probability of historical queries and richer location semantics. To address this issue, a range optimal dummy location selection algorithm based on query probability density is proposed. This method first uses kernel density estimation to find a replacement location within an acceptable loss range for the user's actual request location. Then, word vectors and cosine similarity are used to describe the semantic distance of the location. Finally, a quadrant dichotomy location selection method is used to generate a dummy location set. Conduct simulation experiments on public datasets, compare with existing methods in terms of location entropy, dummy location set coverage area, minimum physical distance, query probability uniformity, dummy location set generation time, and θ- The comparison of the six aspects of safety values proves the effectiveness of the proposed method. © 2023 IEEE.

关键词： Semantics

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Outlier Selection with Local Intrinsic Dimensionality and Visualization Using t-SNE 17th

Outlier Selection with Local Intrinsic Dimensionality and Vi...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Bheekya, Dharamsotu Moiz, Salman Abdul Rao, C. Raghavendra Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Telangana Hyderabad501301 India School of Computer and Information Sciences University of Hyderabad Telangana Hyderabad500046 India

ISBN: (纸本)9789819606917

The k-Nearest Neighbor (k-NN) graph is an essential technique in data mining, machine learning, and computer vision for identifying local data patterns;however, its efficacy is significantly hindered in high-dimensional datasets due to the challenges of the curse of dimensionality, which includes issues like distance concentration and heightened computational requirements. To mitigate these challenges, various strategies, including dimensionality reduction and sophisticated graph construction methods, have been proposed. Outlier detection, vital in domains such as fraud detection and healthcare, similarly faces obstacles in high-dimensional contexts, leading to the development of diverse statistical and learning-based methodologies. This research aims to enhance k-NN graph construction and outlier detection by utilizing sparsification and subspace selection techniques, which effectively capture both local and global outliers through precise estimation of intrinsic dimensionality in localized areas. Furthermore, the study underscores the significance of visualizing high-dimensional data in reduced dimensions to enhance human interpretability, proposing an algorithm that leverages sparsified data representations to improve the accuracy of outlier detection and provide a more efficient framework for high-dimensional data analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization

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Applied Mathematics and Mechanics(English Edition) 2025年第2期46卷 323-340页

作者： Mengting LIN Bin LI C.C.ECATI School of Aeronautics and Astronautics Sichuan UniversityChengdu 610065China Department of Information Engineering Computer Science and MathematicsUniversity of L’AquilaL’Aquila 67100Italy

A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is *** ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the *** power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)*** the exchange of information and energy flow,each microgrid can achieve its local supply-demand ***,the closed-loop stability and recursive feasibility of the proposed algorithm are *** comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.

关键词： distributed stochastic model predictive control(DSMPC) distributionally robust optimization(DRO) islanded multi-microgrid energy dispatch strategy

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