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检索条件"任意字段=Conference on Intelligent Computing - Theory and Applications VI"

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Benefits and Limitations of Using Data-Driven Approach to Develop Inclusivity Networks for Young Disabled People: A Case Study of Assemble by the National Youth Theatre 12th

Benefits and Limitations of Using Data-Driven Approach to De...

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12th International conference on Frontiers of intelligent computing: theory and applications, FICTA 2024

作者： Yu, Qicheng Singh, Uttam Stirbu, Diana Nyby, Lina Digital Media and Computing London Metropolitan University London United Kingdom Social Sciences and Professions Holloway Road London LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601387

Inclusivity networks bridge the gap between disabled and non-disabled communities, empowering individuals. Organisations like the National Youth Theatre have run its local inclusivity networks programme to promote collaboration, reduce isolation, and empower young disabled voices. A data-driven approach is a powerful way to make informed decisions and solve complex problems based on empirical evidence and quantitative data. This paper explores the benefits and limitations of using data-driven approaches to support the development of local inclusivity networks to enhance young disabled people’s voices and lived experience into decision-making and discuss the creation of a dynamic data dashboard, enhancing research efficiency by drawing on existing good practice examples from a London local authority. By combining meaningful findings with data-driven recommendations, we contribute to building more inclusive networks for young disabled people. However, its limitations such as limited data sources, data quality must be addressed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data analytics Data dashboard Data visualisation Data-driven Disability Inclusivity network Quantitative Young people

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EEG Signal Analysis Using AI Model to Track Brain Activity 12th

EEG Signal Analysis Using AI Model to Track Brain Activity

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12th International conference on Frontiers of intelligent computing: theory and applications, FICTA 2024

作者： Ganesan, G. Poonkuntran, S. School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore India

ISBN: (纸本)9789819601424

This study looks into how artificial intelligence (AI) models can be used to track and understand brain activity by analyzing electroencephalogram (EEG) signals. Since EEG signals are a non-invasive way to measure brain activity, they can provide important information about neurological conditions and cognitive states. The proposed methodology involves a comprehensive pipeline, starting with the collection and preprocessing of EEG data, followed by feature extraction and the application of advanced AI models for accurate analysis. The preprocessing stage includes noise removal, artifact rejection, and segmentation of EEG data into meaningful epochs. Feature extraction focuses on capturing essential characteristics, such as power spectral density and temporal dependencies, providing a rich set of inputs for subsequent AI model training. Selecting a model entails deciding which architecture—convolutional neural networks (CNNs), recurrent neural networks (RNNs), or hybrid models—will best process the spatial and temporal information contained in the EEG signals. The AI models are trained on annotated datasets, leveraging advanced optimization techniques to learn intricate patterns indicative of different cognitive states. Performance evaluation is conducted using standard metrics, ensuring the model's accuracy, precision, recall, and generalization capability. Interpretability measures, such as visualizing relevant features and creating saliency maps, enhance the transparency of the AI model's decision-making process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Decision Systems in Cybersecurity: A Machine Learning Perspective 12th

Decision Systems in Cybersecurity: A Machine Learning Perspe...

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12th International conference on Frontiers of intelligent computing: theory and applications, FICTA 2024

作者： Dhablia, Anishkumar Ganesh, R. Senthil Rizvi, Nuzhat Arora, Himanshu Borade, Monali Ravindra Bhujbal, Pallavi Altimetrik India Pvt. Ltd Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Nagpur Campus Pune India Computer Science and Engineering Arya College of Engineering Jaipur India Artificial Intelligence and Data Science VIIT Pune India Department of Information Technology School of Computing MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9789819601462

The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This rev

关键词： Adversarial machine learning

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Cradle- A Simplified Decentralized Finance Platform

Cradle- A Simplified Decentralized Finance Platform

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intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International conference

作者： Nischala G. S Sheba Selvam Divya M. S Praveen Kumar Pavan Deshpande BNM Institute of Technology Bangalore Karnataka

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Financial Systems have been revolutionized by the introduction of Decentralized Finance (De-Fi).They are used for financial activities such as lending, staking and token swapping. However, there are challenges to the existing De-Fi platforms like the complexity that creates a barrier for new users to use them effectively. The proposed model Cradle tries to address these challenges.

关键词： Computational modeling Finance Decentralized applications Blockchains Complexity theory

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Congress on Smart computing Technologies, CSCT 2023

Congress on Smart Computing Technologies, CSCT 2023

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Congress on Smart computing Technologies, CSCT 2023

ISBN: (纸本)9789819780952

The proceedings contain 40 papers. The special focus in this conference is on Smart computing Technologies. The topics include: Artificial Intelligence in Fake News Detection and Analysis for Low-Resource Languages;machine Learning-Based Selection of Type 2 Diabetes Specific Genes and Microarray Datasets;Is Artificial Intelligence Trustworthy? An Empirical Investigation to Adopt Generative AI for Cyber Threat Intelligence Using Valence Framework;WOAGE-MobileNetV2 Model for Plantain Tree Pest Classification and Recognition;ERFC: Crop Prediction-Based Agricultural Environment Using Enhanced Random Forest Classification;Auto-encoder Convolutıonal Neural Network (AECNN) for Apple Fruit Flower Detection;optimizing Distribution of Print Media Using Metaheuristic Algorithms;intelligent Automatic Gate Control System Based on Deep Learning;challenges to Plagiarism Detection and the Use of Voice Assistants in Everyday Life and Education;exploring the Factors that Influence Cambodian Consumers’ Purchase Intention After viewing Smartphone Ads;A Novel Remote Sensing Image Retrieval Method Using Intersected Similarity and Clustering Index (ISCI) Transfer Learning;machine Learning-Based Predictive Modeling for Diabetes and Anemia Disease;diabetic Retinopathy Diagnosis Using a Two Stage Convolutional Neural Network;p300 Classification with ConvNets for Brain Invader;land Rate Prediction Using Computational Intelligence;Optimization-Based Cash Management Model for Microfinance applications Using GSA and PSO;the Use of Graph theory in Development of Routes for Thematic Objects;An Assessment of Requirement Engineering Practices in the IT Companies of South Gujarat Region of India;A Novel Integrated Collaborative Intelligence Framework with Innovative BI Features for Industry 5.0;zero-Knowledge Proofs in Biometric Authentication Systems: A Review;cardiovascular Disease Diagnosis Prediction System Using Machine Learning.

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Measuring Customer Perception on Promotion of Tourism Destinations Using AR and VR applications: Model Testing and Validation 12th

Measuring Customer Perception on Promotion of Tourism Destin...

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12th International conference on Frontiers of intelligent computing: theory and applications, FICTA 2024

作者： Kotni, V. V. Devi Prasad Bogala, Mahendra Reddy Vanapalli, Gowri Lakshmi Nagaraj, K.V. Nitcherla, Somasekhar Vemula, Rajesh GITAM School of Business Gandhi Institute of Technology and Management Deemed to Be University Rushikonda Andhra Pradesh Visakhapatnam530045 India Rushikonda Andhra Pradesh Visakhapatnam530045 India Andhra Pradesh Vizianagaram535003 India School of Business and Management Christ University Karnataka Bangalore560029 India

ISBN: (纸本)9789819601387

The study aims to propose and develop a model to measure the customer perception toward promotional videos created using Augmented Reality (AR) and virtual Reality (VR) technologies to promote tourism places. Using judgment sampling, 400 tourists were chosen, all of whom had visited various tourist spots in visakhapatnam and had seen at least two promotional movies highlighting various tourist attractions using AR/VR technology. A properly written questionnaire was produced ahead of time to gather visitors’ perception for the qualities of augmented and virtual reality advertising attempts. The study revealed that passengers expect full information and appropriate motivation from digital marketing efforts that promote specific tourist locations using Augmented Reality and virtual Reality. Furthermore, visitors anticipate high-quality visual and audio features in digital advertising materials for tourist destinations, with the goal of improving the entire customer experience and inspiring future visits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sales

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A Hybrid Machine Learning and Statistical Analysis Approach for Sales Forecasting and Patterns Discovering in the UK Housing Market 12th

A Hybrid Machine Learning and Statistical Analysis Approach ...

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12th International conference on Frontiers of intelligent computing: theory and applications, FICTA 2024

作者： Ali Bensaad, Cheima Singh, Dileep School of Computing and Digital Media London Metropolitan University LondonN78DB United Kingdom School of Engineering and Computer Science University of Hertfordshire HatfieldAL109AB United Kingdom

ISBN: (纸本)9789819601462

In the landscape of housing market research, there exists a predominant focus on analytical methodologies and economic influencers. However, housing markets stand as pivotal pillars shaping stakeholder contributions to macroeconomic growth. This study explores extensive UK housing data spanning several years, employing an integrated statistical and machine learning techniques approach. By facilitating dynamic updates on comprehensive fluctuations and offering tailored forecasts while quantifying reliability, this research aims to streamline the complexities of market dynamics by harnessing the power of different statistical and machine learning methodologies, data analysis, and visualisation techniques. The project dissects significant factors impacting the UK housing market from 1995 to 2023. Additionally, it seeks to extend its foresight to 2033. This research novelty resides in the proposed integrated framework combining macroeconomic interpretation with Machine Learning in R and interactive Power BI tool with statistical analysis to forecast sales volume. By leveraging and comparing methods like ARIMA and the combination of STL with ETS, alongside neural network models like LSTM, this framework aims to offer valuable market insights and predictions. The results of RMSE and MSE using ARIMA and LSTM models validate the proposed data modelling and analysis in our specific research context. These insights are crucial for stakeholders like policymakers, aiding informed decision-making and enhancing understanding of the UK housing market dynamics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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intelligent Decision-Making in Cybersecurity Using Machine Learning 12th

Intelligent Decision-Making in Cybersecurity Using Machine L...

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12th International conference on Frontiers of intelligent computing: theory and applications, FICTA 2024

作者： Sakhare, Nitin N. Maruthi Shankar, B. Kalnawat, Aarti Sharma, Abha Rege, P.R. Wagh, Kapil Adhar Department of Computer Engineering BRACT’s Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, the need for advanced decision-making tools is more crucial than ever. This study explores machine learning in order to introduce an innovative method for strengthening cybersecurity protocols. The methodology involves combining Random Forests (RF) and Support Vector Machines (SVM) using the AdaBoost algorithm in an ensemble framework. The ensemble model is intricately crafted to address the complexities of intrusion detection, utilizing the combined capabilities of Random Forest’s data handling and Support Vector Machine’s pattern discrimination. AdaBoost enhances the ensemble’s predictive abilities by iteratively adjusting weights to focus more on misclassified instances. This adaptive learning process enables the model to constantly develop and adjust to new cyber threats. Validation against benchmark datasets demonstrates an impressive accuracy rate of 97.78%, highlighting the strength of the ensemble methodology. The True Positive Rate (TPR) of 98.33% demonstrates the ensemble’s effectiveness in accurately detecting and responding to potential cyber threats. The results demonstrate the superior performance of the proposed ensemble method and highlight its ability to achieve a balance between accuracy and adaptability. This research significantly advances intelligent decision-making in cybersecurity by introducing a potent ensemble approach that smoothly integrates various machine learning algorithms. The method’s proven accuracy and sensitivity make it a powerful tool in combating the increasing complexity of cyber threats. The combination of RF, SVM, and AdaBoost sets a standard for future research in strengthening digital defenses and ensuring the resilience of critical information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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applications of Topological Data Analysis with Morphology Operators on Hypergraphs

Applications of Topological Data Analysis with Morphology Op...

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International conference on Mathematics of intelligent computing and Data Science, ICMICDS 2022

作者： Ramkumar, P.B. Dhanya, P.M. Department of Mathematics Rajagiri School of Engineering and Technology Kerala Kakkanad India Department of Computer Science and Business Systems Rajagiri School of Engineering and Technology Kerala Kakkanad India

ISBN: (纸本)9789819615049

A hypergraph is a type of graph, H=(V,E) where V is the set of nodes and E is the set of hyperedges;where one edge consists of may nodes. Morphological operators are nonlinear operators which can be applied on images, graphs, hypergraphs etc. Dilation and erosion are morphological operators and other operators like opening, closing can be obtained by repeated application of these operators. Topological space consists of a set X together with topology τ defined on X, which satisfies the axioms namely Nullset, arbitrary union of opensets and finite intersection of opensets. Such a topological space S=(X,T) is compatible with a hypergraph H if X=VH∪EH where VH and EH are the vertex set and edge set respectively. The morphological operators induces several topology on hypergraph with its compatible topological space S=(X,T). Neighborhood of a node is defined in many ways which can be considered as open sets in the corresponding topological space. The purpose of this paper is to apply topological properties on hypergraphs using morphological operators. Separation axiom and closure function are illustrated using hypergraph structure. The work is extended to crime analysis, text processing and patient disease analysis. Topological feature extraction is done on Barcodes created from crime hypergraphs. This gives a better visualization of crime analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph theory

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Performance Metrics Comparison between Half Bridge DC Link Inverter and Traditional Multilevel Inverter

Performance Metrics Comparison between Half Bridge DC Link I...

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intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International conference

作者： Champa P N Abhay A Deshpande Raksha S Dept. of EEE BNM Institute of Technology Bengaluru India Dept. of ECE RVCE Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This paper focuses on a comparative performance evaluation of conventional multilevel inverters over DC-link inverters, examining key metrics such as harmonic distortion, voltage balancing, power quality, efficiency, and operational flexibility. The circuit configuration with symmetrical topology is modelled and results are verified using MATLAB Simulink. The analysis demonstrates that conventional/traditional multilevel inverters with 5.97% of VTHD consistently outperform DC-link inverters with 20.22% in terms of waveform quality and scalability, albeit with higher complexity and component requirements. The findings underscore the continued relevance of conventional MLIs for applications demanding superior harmonic suppression and high-power capability, while also identifying their limitations compared to simpler alternatives like the DC-link inverter. Simulation results confirm its capability to enhance power quality and system dependability, positioning it as a practical solution for renewable energy systems, drives, and grid-connected applications.

关键词： Renewable energy sources Software packages Simulation Scalability Power quality Multilevel inverters Complexity theory Topology Voltage control Stress

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