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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Bhardwaj, Vivek Virender Kumar, Mukesh Thakur, Deepak Lamba, Vikas School of Computer Science and Engineering Lovely Professional University Jalandhar India Chandigarh University Punjab India School of Computer Application Lovely Professional University Jalandhar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India

ISBN: (纸本)9781665464086

Numerous advances and changes have been brought about by digitalization in both our daily lives and the business world. RPA is an all-purpose technology for developing specialized agents, or "bots," that communicate with GUI components to carry out routine, rule-based operations. Anyone can utilize the RPA to automate digital operations. RPA deployment in business processes can help to provide better service and guarantee that processes adhere to more efficiency and accuracy. The objective of this study is to enable human workers to assign their regular generic repetitive tasks to a software bot, freeing them up to concentrate on more challenging and difficult activities. This study presents a methodology for implementing RPA to extract employee information from the XMLpages. © 2023 IEEE.

关键词： Automation Anywhere Business Processes RPA Software BOT

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Towards a Self-contained Data-driven Global Weather Forecasting Framework 41

Towards a Self-contained Data-driven Global Weather Forecast...

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41st International Conference on Machine Learning, ICML 2024

作者： Xiao, Yi Bai, Lei Xue, Wei Chen, Hao Chen, Kun Chen, Kang Han, Tao Ouyang, Wanli Department of Computer Science and Technology Tsinghua University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China Qinghai University and Intelligent Computing Application Laboratory of Qinghai Province China School of Information Science and Technology Fudan University Shanghai China

Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i.e., analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in numerical weather prediction centers;it is accurate but computationally expensive. In this paper, we aim to couple the AI forecasting model, FengWu, with 4DVar to build a self-contained data-driven global weather forecasting framework, FengWu-4DVar. To achieve this, we propose an AI-embedded 4DVar algorithm that includes three components: (1) a 4DVar objective function embedded with the FengWu forecasting model and its error representation to enhance efficiency and accuracy;(2) a spherical-harmonic-transform-based (SHT-based) approximation strategy for capturing the horizontal correlation of background error;and (3) an auto-differentiation (AD) scheme for determining the optimal analysis fields. Experimental results show that under the ERA5 simulated observational data with varying proportions and noise levels, FengWu-4DVar can generate accurate analysis fields;remarkably, it has achieved stable self-contained global weather forecasts for an entire year for the first time, demonstrating its potential for real-world applications. Additionally, our framework is approximately 100 times faster than the traditional 4DVar algorithm under similar experimental conditions, highlighting its significant computational efficiency. Copyright 2024 by the author(s)

关键词： Data assimilation

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Understanding Fun in Cooking: Preliminary Study of Serious Games on Cooking Domain

Understanding Fun in Cooking: Preliminary Study of Serious G...

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International Conference on Information technology Systems and Innovation (ICITSI)

作者： Steven Andrew Halim Jalaluddin Rumi Galih Dea Pratama Dimas Ramdhan Game Application & Technology Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

Cooking is an important life skill to master. Despite the importance, a study suggests that there is a decline in cooking skills between generations. In accordance with that, serious games emerge as a form of solution which has purposes to solve the problems in an entertaining way. Serious games are often presented to solve numerous real-life problems, such as education, medical, and even culinary. This literature review studies a few of the many benefits of serious games that may help solve problems in the realm of cooking. The study focuses primarily on cooking serious games due to their capabilities to solve problems in an entertaining manner. The result indicates that there are positive benefits in utilizing serious games to solve certain problems not only in cooking, but also other issues such as cognitive skills. The findings from this research are intended to be the preliminary in developing serious game to compensate the problem from cooking domain.

关键词： Technological innovation Education Entertainment industry Games Serious games Complexity theory Usability Older adults Information technology Systematic literature review

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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An Evaluation of Interactive Communication Media Algorithms and Their Usage for Networking applications 5th

An Evaluation of Interactive Communication Media Algorithms ...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Kudari, Jayashree M. Yadav, Rakesh Kumar Yadav, Surendra Kapse, Vinod Mansiram Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This paper evaluates using interactive conversation media algorithms inside the context of networking applications. Exceptional forms of algorithms are mentioned, which includes media movement algorithms, interactive conversation protocols, recording algorithms, and video compression protocols. The overall performance of those algorithms is classified and evaluated thru a quantitative analysis that includes measurements of common latency and packet loss. Additionally, the paper investigates using interactive communication media algorithms for one of kind programs together with video conferencing, on line gaming, and voice over IP offerings. Sooner or later, a contrast of various algorithms is made, with hints for the fine answers primarily based on the evaluated performance and application necessities.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video conferencing

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Autonomous and Adaptive Communications Systems in Mobile Ad-Hoc Networks 5th

Autonomous and Adaptive Communications Systems in Mobile Ad-...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： J, Bhuvana Yadav, Dhyan Chandra Goyal, Manish Kumar Sharma, Himanshu Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Luckow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

Protocols, enabling communication, manipulation, and coordination among cell nodes. Those techniques can efficiently use the community topology, connectivity, to-be-had assets, and consumer requirements to alter communication parameters and path selection for record transmissions robotically. Self-sufficient/adaptive systems also manage user requests and sensor measurements in real-time to dynamically regulate verbal exchange protocols and network configurations for higher verbal exchange performance. Furthermore, those structures also provide capabilities, including self-configuration, self-organization, fault-tolerance, and communique reliability. Various mechanisms may permit autonomous and adaptive verbal exchange in MANETs, including geo-vicinity-based total routing, location-established network coding, allotted garages, energy-conscious communication protocols, and allotted coordination techniques. Additionally, simulated annealing and particle swarm optimization can be used to optimize conversation and routing protocols. Self-sufficient and adaptive communications systems in cellular ad hoc networks have become a need in today’s international, as cell gadgets are used extra often and for more complex tasks. These structures are essential for preserving a clean float of verbal exchange without a centralized infrastructure or constant direction topologies. Self-reliant communications systems offer sturdy, self-configuring, self-healing, and self-organizing communication networks. Such structures are designed to constantly alter parameters primarily based on facts amassed from community resources and a person’s desired preference. Adaptive systems, on the other hand, dynamically create a superior topology by way of considering to-be-had assets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Simulated annealing

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Optimizing Drug Dosage and Medication Management for Geriatric Patients with Artificial Neural Networks 3

Optimizing Drug Dosage and Medication Management for Geriatr...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Beemkumar, N. Singh, Daljeet Pal Ahmed, Zahid Faculty of Engineering and Technology Department of Mechanical Engineering Bagalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources. © 2023 IEEE.

关键词： Optimization

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Investigating Wireless Networks with Network Coding Algorithms 5th

Investigating Wireless Networks with Network Coding Algorith...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Ahmed, Zahid Gupta, Anjali Preethi, D. Singh, Akhilendra Pratap Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronic Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Community coding algorithms had been demonstrated to have capability to improve the performance of wireless networks. This paper offers a research, assessment, and implementation of community coding algorithms for Wi-Fi networks. The paper affords an overview of community coding algorithms and discusses the related blessings, drawbacks, and tradeoffs that have to be taken into account while evaluating the performance of these algorithms. Furthermore, simulation outcomes are offered to assess the overall performance of one of a kind network coding algorithms while implemented in a wireless community surroundings. Ultimately, the paper outlines the hardware and software elements required to correctly put into effect and test those algorithms in an operational wireless community. The promising consequences of the experiments imply that these community coding algorithms can be used to efficiently improve the overall performance of Wi-Fi networks within the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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Addressing the role and opportunities of machine learning utilization in brain tumor detection

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Procedia computer Science 2024年 245卷 869-878页

作者： Vallerie Delia Lesmana Holly Agustine Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science - Binus University Jakarta 11480 Indonesia Mobile Application & Technology Program Computer Science Department - Binus University Jakarta 11480 Indonesia

This research aims to develop a brain tumor detection model by utilizing the machine learning techniques and Convolutional Neural Network (CNN). A significant matter to address is revolving around early detection and the proper handling regarding the brain tumor. This research's methodology consists of collecting the dataset, identifying the tools and language to use, prepare and preprocessing the data, data augmentation, splitting and label encoding, building the model architecture, compiling the model, training, and evaluating the model, predicting the model, and comparing it with other models. Dataset consists of 7022 MRI images, divided into training and testing subsets; and four classes: glioma, meningioma, pituitary, and no tumor. There are four different CNN models that have been built and evaluated, namely VGG16, InceptionV3, ResNet50, and DenseNet121. The result gained shows VGG16 with the best performance achieving an accuracy rate of 96.43%, followed by DenseNet121 (94,96%), InceptionV3 (92,40%), and ResNet50 (78,69%). Although there is still room for improvement regarding overfitting and increasing the models’ overall performance, this result is promising enough to enhance early diagnosis and offer an appropriate and effective treatment for patients.

关键词： Machine Learning Deep Learning CNN Brain Tumor Detection MRI VGG16 InceptionV3 ResNet50 DenseNet121

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Improved Training of GMM-Based TSK Fuzzy System from Stability Perspective 18

Improved Training of GMM-Based TSK Fuzzy System from Stabili...

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18th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2023

作者： Zhou, Erhao Wang, Guanjin Wang, Shitong School of Information Technology Murdoch University Australia School of AI and Computer Science Jiangnan University Wuxi China TaiHu Univ. Key Construction Lab. of IoT Application Technologies Wuxi China

ISBN: (纸本)9798350318401

With the help of the conditional mean, the mathematically equivalent bridge between a Gaussian mixture model (GMM) and a Takagi-Sugeno-Kang (TSK) fuzzy system has been built to form a GMM-based fuzzy system so as to share mutual support for their respective training from rich achievements about GMM and fuzzy system. However, only the conditional mean without the consideration of stability (i.e., conditional variance) may perhaps hinder the modeling performance of the GMM-based fuzzy system, especially for noisy data scenarios. In this study, with strict mathematical derivations, it is revealed that the stability of a GMM-based fuzzy system depends on the three factors, i.e., stability of each fuzzy rule, stability of each input/output subspace associated with each fuzzy rule, and weighted stability of all fuzzy rules and their input/output subspaces. Accordingly, based on the derived result about the above stabilities, the training method of the proposed EGMM-based (Enhanced GMM-based) TSK fuzzy system, which considers both accuracy and stability is developed to improve the modeling performance of the original GMM-based TSK fuzzy system by optimizing the GMM's parameters therein. The effectiveness of the proposed training method is manifested by the experimental results on 10 real regression datasets. © 2023 IEEE.

关键词： Fuzzy rules

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