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SFARDE: A Knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of Ontologies 6th

SFARDE: A Knowledge-Centric Semantic Strategic Framework for...

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6th International Conference on Knowledge Graphs and Semantic Web, KGSWC 2024

作者： Chadalawada, Archit Deepak, Gerard Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9783031812200

Strategic models for heritage artifact recommendation that integrates generative AI and ontologies is the need of the hour. This paper proposes a knowledge-centric semantic model for heritage artifact recommendation which outgrows knowledge from the user perspective and the perspective of the dataset. The user query is enhanced with generated metadata from the Web 3.0 and classified using a strong Bi-LSTM model, and the artifacts are outgrown through generative AI using Gemini Pro as well as ontologies through standard knowledge repositories. This framework computes enhancing differential semantics through Second Order Co-occurrence Pointwise Mutual Information and CoSimRank used at several stages in the pipeline. The Dolphins Echolocation Algorithm is encompassed for optimization of the initial solution set to transform it into a more optimal solution set, using CoSimRank as an objective function. The overlap of generation of tags and the populated knowledge graphs in the dataset perspective to yield a matching instance reprioritization vector is the underlying strategy of the model, which uses both knowledge which is generated from the user perspective through preprocessed user queries, as well as the dataset perspective. An overall precision of 96.07%, recall of 97.07%, and F-measure of 96.57%, with a low False Discovery Rate of 0.04 makes the proposed SFARDE framework a best-in-class model for heritage artifact recommendation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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DeepFake Detection using Convolutional Neural Networks (CNN) and Recurrent Neural Network(RNN) 5

DeepFake Detection using Convolutional Neural Networks (CNN)...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Anjaneyulu, Battula Prasanna Reddy, Chadipiralla Pavan Kunar Reddy, Gangireddy Venkata Ajaykumar Yogitha, Chava Pandiyarajan, Pandiselvam Maheshwaran, Baskaran Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science Engineering Tamilnadu Krishnankoil India Ayya Nadar Janaki Ammal College Department of Bio-Technology Tamilnadu Sivakasi India

ISBN: (纸本)9798350389609

To analyse the DeepFakes, which are AI-generated synthetic media that impersonate real people and pose substantial threats to digital content security, privacy, and authenticity. This research investigates a unique technique to DeepFake detection that employs Recurrent neural networks (RNN) are used for temporal sequence analysis and convolutional neural networks (CNN) for extracting spatial features. The suggested method seeks to improve detection accuracy across a wide range of DeepFake datasets by combining the strengths of CNNs and RNNs. The experimental results show that this hybrid architecture is excellent at identifying DeepFakes with good precision and recall.) © 2024 IEEE.

关键词： Convolutional neural networks

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Integrating Cloud-based Data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

This study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. The analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. The model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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Examining the Application of Machine Learning to Improve Organizational Performance in the Medical Setting 15

Examining the Application of Machine Learning to Improve Org...

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

作者： Shelke, Ganesh C. Dutta, Abhishek Katariya, Jitendra Kumar Sakhare, Nitin N. Anandhasilambarasan, D. Mahalakshmi, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350370249

This paper examines the capacity utility of machine gaining knowledge of within the clinical putting to enhance organizational overall performance and affected person effects. The paper begins by means of offering a top level view of ways gadget gaining knowledge of is being utilized in other industries and the way it is able to be applied inside the scientific placing. It then goes on to speak about the important thing challenges related to growing and deploying gadget learning inside the clinical putting which include statistics privateness and security, records availability, and they want for comprehensive expertise of clinical contexts. It examines the modern-day literature on machine studying inside the scientific placing to perceive satisfactory practices and proposes regions of destiny studies. In the end, the paper summarizes the implications from the studies, highlighting the capacity advantages and demanding situations that want to be addressed earlier than machine gaining knowledge of may be efficiently implemented in the medical field. Universal, this paper offers an informative evaluation of the capability programs of machine gaining knowledge of inside the clinical placing and emphasizes the need for greater studies in this place. © 2024 IEEE.

关键词： clinical gaining greater satisfactory situations

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Reduced Carbon Foot Print Based Localized Energy Demand Supply Using Micro Grids 4

Reduced Carbon Foot Print Based Localized Energy Demand Supp...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Mariappan, Umasree Balakrishnan, D. Ponraj, Anitha Maheswari, S. Balaji, Rayeni Sri P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil626126 India Sri Vidya College of Engineering & Technology Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar626005 India

ISBN: (纸本)9798350393354

Microgrid systems are the most used devices which can support the storage of a higher amount of energy with increased reliability and flexibility. In this study, a grid-connected microgrid (MG) integrated with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is introduced as a test system for optimizing costs and battery charging/discharging strategies. The framework developed represents a multi-objective function along with constraints, suitable for addressing this stochastic multi-objective problem. The performance of these physics-based optimization techniques is compared based on their effectiveness in achieving cost savings and optimizing battery charging. Upon analysis, it was found that Black Hole Optimization (BHO) exhibited significant cost savings overall, while Lightning Search Algorithm (LSA) proved more effective in optimizing battery charging strategies. This evaluation and comparison of different optimization techniques provide insights into their respective strengths and effectiveness when applied to the proposed MG test system. Specifically, BHO excelled in cost savings, whereas LSA showed superior performance in optimizing battery charging. These findings can guide researchers and practitioners in selecting appropriate optimization techniques based on the specific objectives and constraints of similar MG systems. © 2024 IEEE.

关键词： Stochastic systems

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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Cross-Modal Recipe Retrieval With Fine-Grained Prompting Alignment and Evidential Semantic Consistency

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IEEE Transactions on Multimedia 2025年 27卷 2783-2794页

作者： Huang, Xu Liu, Jin Zhang, Zhizhong Xie, Yuan Tang, Yongqiang Zhang, Wensheng Cui, Xiaohui Wuhan University School of Computer Science Wuhan430072 China Ministry of Education Wuhan430062 China East China Normal University School of Computer Science and Technology Shanghai200062 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Alignment between the food images and the corresponding recipes is an emerging cross-modal representation learning task. In this task, the recipes are composed of three components, i.e., food title, ingredient lists, and cooking instructions, which require a fine-grained alignment between the features of the two modalities. Existing methods usually aggregate the recipes into global embeddings and then align them with the global image embeddings. Meanwhile, semantic classification is frequently used in these methods to regularize the embeddings of the two modalities. While these methods are efficient, there remain two problems. (1) Forcing the alignment between the global images and recipes embeddings may result in losing the component-specific information. (2) The high diversity of food appearance leads to high uncertainty in the semantic classification of food images and recipes. To solve these problems, we propose a Fine-grained Prompting and Alignment (FPA) model to enhance the feature extraction and bring more component-specific information for fine-grained alignment. Furthermore, to regularize the semantic information contained in the cross-modal features, we design an Evidential Semantic Consistency (ESC) loss to keep the cross-modal semantic consistency. We have conducted comprehensive experiments on the benchmark dataset Recipe1M and the state-of-the-art results on the cross-modal recipe retrieval task demonstrate the effectiveness of our method. © 1999-2012 IEEE.

关键词： Alignment

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An Improved and Optimized Random Forest Based Approach to Predict the Software Faults

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SN computer science 2024年第5期5卷 1-18页

作者： Thomas, Nikhil Saji Kaliraj, S. Department of Computer Science and Engineering Manipal Academy of Higher Education Dubai United Arab Emirates Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 576104 India

Effective software fault prediction is crucial for minimizing errors during software development and preventing subsequent failures. This research introduces an enhanced Random Forest-based approach for predicting software faults, specifically focusing on the NASA JM1 dataset. The dataset comprises 21 software metrics indicating the presence or absence of faults in a module, and it is utilized to evaluate the proposed approach. The study delves into the intricacies of the NASA dataset, detailing the cleaning process and addressing class imbalance through Synthetic Minority Over-sampling Technique (SMOTE). The core of our approach involves the implementation and fine-tuning of the Random Forest classifier, with a specific focus on optimizing hyperparameters to enhance predictive accuracy. In comparative evaluations with standard machine learning models, our proposed approach demonstrated superior performance, achieving an accuracy of 82.96% and an F1 score of 89.53%. Notably, we emphasize the significance of software defects and their potential to cause failures and crashes during software development, leading to substantial organizational losses. The paper provides a comprehensive examination of different aspects of the machine learning model, offering detailed insights, examples, and illustrative figures to enhance the understanding of our proposed approach. © The Author(s) 2024.

关键词： Class Imbalance NASA Defect Data Random Forest, Optimization SMOTE Software Fault Prediction

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Triboelectric-electromagnetic hybrid generator with swing-blade structures for effectively harvesting distributed wind energy in urban environments

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Nano Research 2023年第9期16卷 11621-11629页

作者： Yangyang Yan Jinzhi Zhu Yuejun Zhang Zhaohui Wang Lin Jiang Zhong Lin Wang Jianyang Zhu Tinghai Cheng Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China Georgia Institute of Technology AtlantaGA 30332-0245USA Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

The wind energy in cities cannot be exploited effectively because natural wind is unstable and ***,a triboelectricelectromagnetic hybrid generator with swing-blade structures(SBS-TEHG)was designed to effectively harvest intermittent and continuous wind energy in an urban ***,the spring structure and base were considered to realize the maximum output performance of triboelectric ***,the computational fluid dynamics method was applied to optimize the structure of the SBS-TEHG to improve its aerodynamic *** starting wind speed of the SBS-TEHG was 2 m/s,and its energy conversion efficiency was 9.04%,159%higher than that of the SBS-TEHG without guide plates at 4 m/*** results demonstrated that the SBS-TEHG lit 105 light-emitting diodes(LEDs)under the intermittent-wind harvesting mode at a wind frequency of 1 Hz when the single swing blade operated,while a wireless PM_(2.5)&PM_(10)sensor was powered by the SBS-TEHG after a period of operation under the continuous-wind harvesting *** findings of this study provide a novel solution for lowspeed wind energy harvesting in cities and demonstrate the potential of SBS-TEHG as a distributed energy source.

关键词： triboelectric nanogenerator swing-blade structure low-speed wind energy computational fluid dynamics distributed energy source

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PMFRO: Personalized Men’s Fashion Recommendation Using Dynamic Ontological Models 22nd

PMFRO: Personalized Men’s Fashion Recommendation Using Dyna...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Arunkumar, S. Deepak, Gerard Priyadarshini, J. Sheeba Santhanavijayan, A. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Bengaluru India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

There is a thriving need for an expert intelligent system for recommending fashion especially focusing on men’s fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for men’s recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the men’s fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the men’s fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending men’s fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

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