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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Myagmarsuren Orosoo Indrajit Goswami Fredrick Ruban Alphonse Gulnaz Fatma Manikandan Rengarajan B Kiran Bala Mongolian National University of Education General Management Strategy and HRM Alliance School of Business Alliance University Bengaluru Department of Languages Institute of Finance and International Management Bengaluru Dept. of English Jazan University Jazan Saudi Arabia Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

In order to promote smooth cross-cultural communication, this research focuses on improving Natural Language Processing (NLP) features within multilingual Chabots. The need for efficient communication across various linguistic and cultural barriers has grown as the world becomes more interconnected. In this context, multilingual Chabot's are essential tools that facilitate easy communication between users of various cultural backgrounds. In order to better understand and respond to a wide range of linguistic details, idioms, and cultural allusions, the research explores ways to improve NLP algorithms. This research investigates how machine learning techniques can be integrated to improve the Chabot's ability to adapt to various linguistic patterns, enabling it to acquire new language skills over time. By implementing an extensive and flexible approach, the proposed approach seeks to transform Natural Language Processing in Multilingual Chatbots for Cross-Cultural Communication. In order to identify language-specific patterns and specifications, the methodology starts with the collection of a diverse dataset that is representative of linguistic variations and cultural contexts. It then uses advanced linguistic analysis techniques like phonetic analysis. The next step is cultural context modelling, which entails building a database enhanced with cultural allusions and language variations unique to a given context that are obtained from various sources. Transfer learning, reinforcement learning for dialogue, and modern algorithms such as Multilingual Unsupervised and Supervised Embeddings (MUSE) for cross-lingual embeddings are all included in machine learning integration. In order to obtain information about language precision and cultural sensitivity, the methodology incorporates interactive surveys and sentiment analysis into a strong user feedback mechanism. The proposed model achieved 98% performance.

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Real-time Non-invasive Blood Glucose Monitoring using Advanced Machine Learning Techniques

Real-time Non-invasive Blood Glucose Monitoring using Advanc...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： L Jenitha Mary V. Vijayashanthi M. Parameswari E. Venitha T A Mohanaprakash S. Dhanush Hariharan Department of CSE DMI College of Engineering Chennai India Department of CSE Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India Department of CSE Kings Engineering College Chennai India Department of CSE Rajalakshmi Institue of Technology Chennai India Department of CSE Panimalar Engineeering College Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering college Chennai India

When left untreated, diabetes, a chronic ailment that affects a vast number of people overall, might result in major unanticipated problems. The risk of complications can be completely reduced and considerable improvements can be achieved with early detection of diabetes. Recently, the use of wearable technology has emerged as a potential tool for diagnosing and checking illnesses. Smartwatches with bioactive sensors are perfect for diabetes screening because they can provide continuous, painless monitoring of bodily vitals. This paper suggests a methodology for building a hybrid AI model to detect the existence of diabetes using patient data. The system combines body vitals calculated using a smartwatch equipped with a bioactive sensor to provide accurate and continuous information on the wearer’s health state. The hybrid model combines both deep learning and traditional AI computations to achieve a high level of accuracy while diagnosing diabetes. The framework collects data on many bodily parameters, including skin conductance, circulatory strain, and pulse — all of which are known to be strongly associated with diabetes. The acquired data is pre-processed before being utilized to create the hybrid model. The standard AI calculation is used to classify the information into diabetes or non-diabetic categories, while the profound learning calculation is used to eliminate important level highlights from the raw data. The hybrid approach combines the advantages of both deep learning and traditional AI to improve the accuracy of diabetes localization.

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Semantic data Management in data Lakes

arXiv

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arXiv 2023年

作者： Hoseini, Sayed Theissen-Lipp, Johannes Quix, Christoph Department of Electrical Engineering and Computer Science Hochschule Niederrhein University of Applied Sciences Reinarzstrasse 49 NRW Krefeld47805 Germany Databases and Information Systems RWTH Aachen University Ahornstraße 55 Aachen NRW Aachen52074 Germany Department of Data Science and Artificial Intelligence Fraunhofer Institute for Applied Information Technology FIT Schloss Birlinghoven NRW St. Augustin53757 Germany

In recent years, data lakes emerged as a way to manage large amounts of heterogeneous data for modern data analytics. One way to prevent data lakes from turning into inoperable data swamps is semantic data management. Some approaches propose the linkage of metadata to knowledge graphs based on the Linked data principles to provide more meaning and semantics to the data in the lake. Such a semantic layer may be utilized not only for data management but also to tackle the problem of data integration from heterogeneous sources, in order to make data access more expressive and interoperable. In this survey, we review recent approaches with a specific focus on the application within data lake systems and scalability to Big data. We classify the approaches into (i) basic semantic data management, (ii) semantic modeling approaches for enriching metadata in data lakes, and (iii) methods for ontology-based data access. In each category, we cover the main techniques and their background, and compare latest research. Finally, we point out challenges for future work in this research area, which needs a closer integration of Big data and Semantic Web technologies. © 2023, CC BY.

关键词： Big data

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Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

arXiv

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arXiv 2021年

作者： Chen, Tao Xie, Guo-Sen Yao, Yazhou Wang, Qiong Shen, Fumin Tang, Zhenmin Zhang, Jian The School of Computer Science and Engineering Nanjing University of Science and Technology China The Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates The School of Computer Science and Engineering University of Electronic Science and Technology of China China The School of Electrical & Data Engineering University of Technology Sydney Australia

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. However, these existing approaches simulate the test conditions too strictly during the training process, and thus cannot make full use of the given label information. Besides, these approaches mainly focus on the foreground-background target class segmentation setting. They only utilize binary mask labels for training. In this paper, we propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category. After integrating the target class cues into the query features, we then propose a pyramid feature fusion module to mine the fused features for the final classifier. Furthermore, to take more advantage of the support image-mask pair, we propose a self-prototype guidance branch to support image segmentation. It can constrain the network for generating more compact features and a robust prototype for each semantic class. For inference, we propose a fused prototype guidance branch for the segmentation of the query image. Specifically, we leverage the prediction of the query image to extract the pseudo-prototype and combine it with the initial prototype. Then we utilize the fused prototype to guide the final segmentation of the query image. Extensive experiments demonstrate the superiority of our proposed approach. The source codes and models have been made available at https: //***/NUST-Machine-intelligence-Laboratory/SMCP. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： artificial intelligence breast cancer digital pathology whole slide images

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3D CT Liver Images Lesion Extraction and Classification for Using 3D-CNN with GLRLM

3D CT Liver Images Lesion Extraction and Classification for ...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： S. Jayarathna R. Sowmiya G. Nallasivan G. Sahaana S. Ashok R. Saravanakumar Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed tomography images, we were able to categorize aggressive tumors that were found in the liver for the aim of this research. When it comes to the three-dimensional (3D) CT image, the dimension reduction is accomplished by the use of principal component analysis (PCA). The Random Forest Algorithm is responsible for identifying the abnormal hepatic region, and a median filter is used to reduce the amount of noise occurring. Methods of data augmentation are also employed in this process. To extract Gray Level Run Length Matrix (GLRLM) features, using Pyradiomics python package. These features are then fed into a 3D-CNN classifier to classify malignant tumors and haemangiomas. Our model was found to have greater performance after being compared to the findings of other models. This was revealed after the comparison was made. For the training dataset, our model has an accuracy of 97.2%, and for the test dataset, it has an accuracy of 98.25%. This accuracy is determined by the performance metrics that our model incorporates.

关键词： Training Solid modeling Accuracy Three-dimensional displays Computed tomography Liver Feature extraction data augmentation data models Principal component analysis

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Efficient voltage regulation: An RW-ARO optimized cascaded controller approach

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 9卷

作者： Eker, Erdal Izci, Davut Ekinci, Serdar Migdady, Hazem Zitar, Raed Abu Abualigah, Laith Vocational School of Social Sciences Muş Alparslan University Department of Computer Engineering Batman University Applied Science Research Center Applied Science Private University CSMIS Department Oman College of Management and Technology Faculty of Engineering and Computing Liwa College Computer Science Department Al al-Bayt University MEU Research Unit Middle East University Jadara Research Center Jadara University Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk School of Engineering and Technology Sunway University Malaysia

This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensure stable and reliable voltage output. A crucial innovation in this work is the introduction of the random walk aided artificial rabbits optimizer (RW-ARO). This novel optimization strategy incorporates a random walk approach, enhancing the efficiency of AVR control schemes. The proposed cascaded RPIDD²-PI controller, fine-tuned using the RW-ARO, stands out as a pioneering approach in the AVR domain. It demonstrates superior stability, faster response times, enhanced robustness, and improved efficiency compared to existing methods. Comparative analyses with established controller approaches reaffirm the exceptional performance of the proposed method. The new approach results in shorter rise times, quicker settling times, and minimal overshoot, highlighting its effectiveness and speed in achieving desired system responses. Moreover, the novel approach attains higher phase and gain margins, showcasing its superior performance in the frequency domain. The disturbance rejection and harmonic analysis are performed in order to demonstrate the efficacy of the proposed approach for potential real-world applications. The latter analyses further cement the superior capability of the proposed approach for the automatic voltage regulation. © 2024 The Author(s)

关键词： artificial rabbits optimization algorithm Automatic voltage regulator Cascaded RPIDD2-PI controller Optimization Random Walk strategy

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The International Monetary Funds intervention in education systems and its impact on childrens chances of completing school

arXiv

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arXiv 2021年

作者： Daoud, Adel Institute for Analytical Sociology Department of Management and Engineering Linköping University Norrköping Sweden The Division of Data Science and Artificial Intelligence The Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

Enabling children to acquire an education is one of the most effective means to reduce inequality, poverty, and ill-health globally. While in normal times a government controls its educational policies, during times of macroeconomic instability, that control may shift to supporting international organizations, such as the the International Monetary Fund (IMF). While much research has focused on which sectors has been affected by IMF policies, scholars have devoted little attention to the policy content of IMF interventions affecting the education sector and children's education outcomes: denoted IMF-education policies. This article evaluates the extent which IMF-education policies exist in all programs and how these policies and IMF programs affect children's likelihood of completing schools. While IMF-education policies have a small adverse effect yet statistically insignificant on children's probability of completing school, these policies moderate effect heterogeneity for IMF programs. The effect of IMF programs (joint set of policies) adversely effect children's chances of completing school by six percentage points. By analyzing how IMF-education policies but also how IMF programs affect the education sector in low- and middle-income countries, scholars will gain a deeper understanding of how such policies will likely affect downstream outcomes. © 2021, CC BY-NC-ND.

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Retraction note: Blockchain multi-objective optimization approach-enabled secure and cost-efficient scheduling for the Internet of Medical Things (IoMT) in fog-cloud system (Soft Computing, (2022), 26, 13, (6429-6442), 10.1007/s00500-022-07167-9)

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Soft Computing 2024年第2期28卷 921-921页

作者： Lakhan, Abdullah Mohammed, Mazin Abed Elhoseny, Mohamed Alshehri, Mohammad Dahman Abdulkareem, Karrar Hameed Department of Computer Science Dawood University of Engineering and Technology Sindh Karachi74800 Pakistan College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China College of Computer Science and Information Technology University of Anbar Mohamed Elhoseny Anbar31001 Iraq College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Computer Science College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia College of Agriculture Al-Muthanna University Samawah66001 Iraq

The publisher has retracted this article in agreement with the Editor-in-Chief. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation’s findings the publisher no longer has confidence in the results and conclusions of this article. Mazin Abed Mohammed has stated on behalf of all authors that all authors disagree with this retraction. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility

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Geomatics, Natural Hazards and Risk 2023年第1期14卷

作者： Janizadeh, Saeid Bateni, Sayed M. Jun, Changhyun Im, Jungho Pai, Hao-Thing Band, Shahab S. Mosavi, Amir Department of Civil and Environmental Engineering and Water Resources Research Center University of HI at Manoa HonoluluHI United States Department of Civil and Environmental Engineering College of Engineering Chung-Ang University Seoul Korea Republic of Department of Urban & Environmental Engineering Ulsan National Institute of Science and Technology Ulsan Korea Republic of Department of Environmental Resources Engineering State University of NY College of Environmental Science and Forestry SyracuseNY United States International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Informatics J. Selye University Komarno Slovakia

In this study, the generalized linear model (GLM) and four ensemble methods (partial least squares (PLS), boosting, bagging, and Bayesian) were applied to predict forest fire hazard in the Chalus Rood watershed in the Mazandaran Province, Iran. data from 108 historical forest fire events collected through field surveys were applied as the basis of the analysis. About 70% of the data were used for training the models, while the remaining 30% was used for testing. A total of 14 environmental, climatic, and vegetation variables were used as input features to the models to predict forest fire probability. After conducting a multicollinearity test on the independent variables, the GLM and the ensemble models were applied for modeling. The efficiency of the models was evaluated using receiver operating characteristic (ROC) curve parameters. Results from the validation process, based on the area under the ROC curve (AUC), showed that the GLM, PLS-GLM, boosted-GLM, Bagging-GLM, and Bayesian-GLM models had efficiencies of 0.79, 0.75, 0.81, 0.84, and 0.85, respectively. The results indicated that all ensemble methods, except the PLS algorithm, improved the performance of the GLM model in modeling forest fire hazards in the Chalus Rood watershed, with the Bayesian algorithm being the most efficient method among them. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Watersheds

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