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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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Competition Dynamics in the Meme Ecosystem

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ACM Transactions on Social Computing 2023年第3-4期6卷 1-19页

作者： Ford, Trenton W. Krohn, Rachel Weninger, Tim Integrated Science Center College of William and Mary 540 Landrum Dr WilliamsburgVA23185 United States Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Moench Hall Terre HauteIN47803 United States Department of Computer Science and Engineering 380 Fitzpatrick Hall University of Notre Dame Notre DameIN46556 United States

Creating and sharing memes is a common modality of online social interaction. Because of the prevalence of memes, an abundance of research focuses on understanding how memes are shared and perceived within their online social environment and in what ways they differ from other modalities of online communication. In the present work, we present findings that suggest a relationship between how memes are introduced, shared, and diminish in online social systems and the behavior of biological species within an ecosystem. Framed by this perspective, we borrow ecological methodologies and concepts like resource availability, speciation, and competition to study human attention, creativity, and sharing dynamics (respectively) over a large collection of memes shared on Reddit. Specifically, we find that the population of memes has scaled almost exactly with the total amount of content created over the past decade. We find a consequence of limited human attention in the face of a growing number of memes is that the diversity of memes has decreased at the community level, albeit slightly, in the same period. A further consequence is that the average life span of memes has decreased dramatically, which is further evidence of an increase in competition and a decrease in a meme species’ primary resource: human attention. From this work, we have found reasonable preliminary support for linking memes to species and, thus, meme research to ecological research. We believe that future research should work to strengthen this relationship and transfer more methodologies to meme and information research from ecology. © 2023 Copyright held by the owner/author(s)

关键词： Ecosystems

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An Abstract Domain of Linear Templates with Disjunctive Right-Hand-Side Intervals 9th

An Abstract Domain of Linear Templates with Disjunctive Ri...

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9th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2023

作者： Xu, Han Chen, Liqian Fan, Guangsheng Yin, Banghu Wang, Ji College of Computer National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Software Engineering for Complex Systems Changsha410073 China HPCL National University of Defense Technology Changsha410073 China College of Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819986637

Abstract interpretation provides a general framework for analyzing the value ranges of program variables while ensuring soundness. Abstract domains are at the core of the abstract interpretation framework, and the numerical abstract domains aiming at analyzing numerical properties have received extensive attention. The template constraint matrix domain (also called the template polyhedra domain) is widely used due to its configurable constraint matrix (describing limited but user-concerned linear relationships among variables) and its high efficiency. However, it cannot express non-convex properties that appear naturally due to the inherent disjunctive behaviors in a program. In this paper, we introduce a new abstract domain, namely the abstract domain of linear templates with disjunctive right-hand-side intervals, in the form of ∑iaixi∈⋁j=0p[cj,dj] (where ai ’s and p are configurable and fixed before conducting analysis). Experimental results of our prototype are encouraging: In practice, the new abstract domain can find interesting non-convex invariants that are out of the expressiveness of the classic template constraint matrix abstract domain. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Abstract domain Abstract interpretation Invariant Template constraint matrix

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An Initial Solution Based Selective Harmonic Elimination Method for Multilevel Inverter

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power Electronics and Applications 2024年第1期9卷 63-78页

作者： Kala, Peeyush Sharma, Abhinav Jately, Vibhu Joshi, Jyoti Yang, Yongheng Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Ghaziabad201204 India Department of Electrical and Electronics Engineering Dehradun248007 India Dehradun248007 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun248002 India College of Electrical Engineering Zhejiang University Hangzhou310027 China

This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of iterations. Initial SHE solution is defined as a set of initial switching angles that has less accuracy as compared to the final SHE solution. To find initial SHE solution, tunicate swarm algorithm (TSA), grey wolf optimization (GWO) and whale optimization (WO) are used to solve five-dimensional SHE problem. A comparative analysis demonstrates that TSA achieved the desired initial SHE solution within lesser iterations as compared to GWO and WO for a wide range of modulation indices. The initial switching angles found using TSA are further optimized using Newton-Raphson (NR) method being the proposed TSANR method, which exploits the SHE search space with quadratic rate of convergence to find final SHE solution. The proposed TSANR method significantly minimizes the computational time and further improves the accuracy of the SHE solution as compared to the state-of-the-art SHE methods. Detailed simulations, experimental and harmonic analysis under dynamic change in modulation index are presented to show the efficacy of the proposed SHE method in terms of control over fundamental and detrimental harmonics. © 2017 CPSS.

关键词： Harmonic analysis

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Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique 1

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Webber, Julian L. Jayagopal, Vellingiri Mehbodniya, Abolfazl Sengan, Sudhakar Velayutham, Priya Rangasamy, Rajasekar Stalin David, D. Doha Kuwait Department of Software & System Engineering School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The significant growth of Wind Power (WP) has significant challenges due to the inconsistency and uncertainty of Wind Power Generation (WPG) to ensure the power system. Solar energy and WP calculation are great ways to overcome the problem. Mainly voltage and frequency fluctuations and harmonics have a high impact on the power generation and distribution system;in any high case, the power quality of grid systems is reduced. This can be primarily solved with proper design, advanced rapid response control facilities and optimization of power systems. A new type of Multi-term Wind Energy (WE) with an IEEE 30 bus forecasting system based on digital weather forecasting and error modification methods is presented. From the Evolutionary Energy- Preserve Rate Clustering (EEPRC) Technique-based technique for the evaluates the probability ratio in Solar and wind error energy prediction, the error assessment model is proposed, which has the advantage of estimating from real wind farms and statistical Weather Forecast (WF) data and has proven to be a nominal method for improving the predictive accuracy of Multi-term WPG. The primary step of the Energy-Preserve Rate Clustering (EPRC) method is to calculate the uncertainty of real-time data with the predicted value. The computed values are helpful for the increases in Solar WPG production. The proposed wind forecasting system is utilized in the Coastal area-based energy generation. Also, it has been demonstrated that wind speed and WPG with its probability ratio have been evaluated with the EEPRC-based forecasting method, especially for its powerful predictive capability for the complex properties of the controller function. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Synergistic applications of nanostructured boron nitride in photocatalysis and targeted drug delivery

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Nanotechnology for Environmental engineering 2024年第2期9卷 179-188页

作者： Mali, Priyadarshani Shivakumar Jagtap, Bhagyashree Krishnarao Gowda V, Dankan Ashreetha, B. Vasanthakumar, G.U. kaur, Mandeep Hariram, Venkatesan Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth’s college of Engineering Kolhapur Maharashtra416013 India Department of Electronics and Computer Engineering Sharad Institute of Technology college of Engineering Yadrav Maharashtra Ichalkaranji Kolhapur416115 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka560064 India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Yelahanka Bengaluru560064 India Department of Electronics and Communication Engineering Punjabi University Punjab Patiala147002 India Department of Practice of Medicine Vinayaka Mission’s Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission’s Research Foundation Deemed to be University Tamilnadu Salem636 308 India

This study explores the applications of nanostructured boron nitride (BN) in photocatalysis and targeted drug delivery, highlighting its notable thermal conductivity, chemical stability, and wide bandgap. Through the synthesis and detailed characterization of BN nanoparticles, employing Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), the material’s efficacy in light-induced chemical reactions and potential as a nanocarrier for drug delivery systems are investigated. Photocatalytic experiments reveal an enhanced ability of BN to absorb light and facilitate charge separation, suggesting a promising approach for environmental detoxification and sustainable energy generation. In parallel, investigations into BN’s drug loading capacity, release kinetics, and targeting efficiency, particularly in the context of cancer therapy, are conducted. Findings indicate that BN nanoparticles significantly enhance photocatalytic performance and offer a safe, effective platform for controlled drug release. This research underscores the dual utility of BN in addressing critical challenges in environmental engineering and medical treatment, pointing towards novel interdisciplinary applications of this versatile nanomaterial. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Photocatalysis

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Pose invariant non-frontal 2D, 2.5D face detection and recognition technique

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International Journal of Information technology (Singapore) 2023年第5期15卷 2603-2611页

作者： Thavani, Shiva Sharma, Sahil Kumar, Vijay Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India Computer Science and Engineering Department Punjab Engineering College Chandigarh India Information Technology Department Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

In this paper, the Pose Invariant Identity Recognition (PIIR) technique is proposed. It measures face resemblance using facial landmarks, which are further vigorous to pose disparity. The proposed technique utilizes the concepts of face frontalization and discriminative learning techniques. 3D Morphable Model is used in both techniques for pose invariant. The proposed technique is validated on CAS-PEAL face dataset. Experimental results signify that the proposed technique attains authentication accuracy of 90.24% on face datasets with 30° pan-angle. The proposed technique is further applied on 2.5D face dataset with variant facial positions and expressions. Results reveal that the proposed method can identify the face accurately. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： 2.5D depth-based face recognition Face reconstruction Feature extraction Non-frontal face recognition Pose-invariant identity recognition

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Optimizing Blood Bank Management with Cloud-Hosted Long Short-Term Memory Models for Inventory Forecasting and Utilization 3

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Devi, R. Aiyshwariya Sree Southry, S. Rajanarayanan, S. Sekaran, S. Chandra Sivaraman, Audithan Srinivasan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Department of Computer Science and Engineering Tamil Nadu Salem India P.S.V College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Krishnagiri India Department of Information Technology Jafferpur New Delhi India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research presents an innovative method for blood bank management using Cloud-based Long Short-Term Memory (LSTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LSTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LSTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts. © 2025 IEEE.

关键词： Efficiency

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A Virtual Network Mapping Algorithm Based on Time

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Chinese Journal of Electronics 2025年第1期23卷 31-36页

作者： Ming Jiang Zhiyang Zhao Min Zhang Jingfan Tang Chunming Wu Xiao Min Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Mathematics and Information Engineering College of Jiaxing University Jiaxing China

As one of the challenges for network virtualization, virtual network embedding which maps Virtual network (VN) to the substrate network and allocates resources according to the requirements of VN in an efficient way has gained great attention. Existing algorithms generally make their decision according to the present available substrate network resource, especially bandwidth. This paper proposes a time-based VN embedding algorithm. A probability model is formulated to obtain the maximum probability that the available resources of substrate network can be used by succeeding VN requests. The probability is set as the weight of the node and the greedy algorithm is employed to embed the virtual node. The reciprocal of the probability is set as the weight of the link and the shortest path algorithm is employed to embed the virtual link. Simulation experiments show that the proposed algorithm increases the acceptance rate and the revenue compared to the existing algorithms.

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