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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Review of Stress Detection Techniques for IT Professionals Using Image Processing with Optimized Machine learning Classifiers

Review of Stress Detection Techniques for IT Professionals U...

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intelligent Systems and Embedded Design (ISED), international conference on

作者： V Acharyulu Kallakuri S. Karthikeyan Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology (Deemed to be University) Chennai India

Stress is an inevitable aspect of every human life and is going to remain for an extended period of time. Every individual is surely faced with a variety of stressful circumstances from birth. Nevertheless, stress is not always detrimental. Stress is constantly required to inspire and stimulate us. As a result, moderate stress is quite healthy and stress is a part of everyone's life, and IT professionals are no exception. It has become a major source of concern among IT professionals. Individuals working in information technology experience additional stress since they have to constantly improve their knowledge. Moreover, several development tools, including as Machine learning (ML) and optimisation approaches, are extremely effective in accurately identifying and anticipating stress. As a result, in this study, many forms of ML and optimisation strategies are examined in order to determine the stress levels of IT employees. In this case, IT management needs to take corrective action to minimize the detrimental effects of stress on their personnel.

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data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications 28th

Data Engineering and AI-Powered Skin Cancer Identification f...

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28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Animal and Agriculture Department Hartpury University Gloucester United Kingdom Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. this work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. the process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. the Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. the proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. the trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. the CNN model's parameters and architecture are systematically presented, offering insights into its design rationale. the model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. the model's effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. data augmentation strategies are introduced to enhance model generalization further. the results underscore CNN's potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis © 2024 the Authors.

关键词： Convolutional neural networks

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Clustering model selection for reduced support vector machines

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5th international conference on intelligent data engineering and automated learning, IDEAL 2004

作者： Jen, Lih-Ren Lee, Yuh-Jye Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

ISBN: (纸本)3540228810

the reduced support vector machine was proposed for the practical objective that overcomes the computational difficulties as well as reduces the model complexity by generating a nonlinear separating surface for a massive dataset. It has been successfully applied to other kernel-based learning algorithms. Also, there are experimental studies on RSVM that showed the efficiency of RSVM. In this paper we propose a robust method to build the model of RSVM via RBF (Gaussian) kernel construction. Applying clustering algorithm to each class, we can generate cluster centroids of each class and use them to form the reduced set which is used in RSVM. We also estimate the approximate density for each cluster to get the parameter used in Gaussian kernel. Under the compatible classification performance on the test set, our method selects a smaller reduced set than the one via random selection scheme. Moreover, it determines the kernel parameter automatically and individually for each point in the reduced set while the RSVM used a common kernel parameter which is determined by a tuning procedure. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Radial basis function networks

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learning Analytics to study the use of virtual tutorials for teaching university general educational subjects 11

Learning Analytics to study the use of virtual tutorials for...

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11th IEEE international conference on Teaching, Assessment and learning for engineering, TALE 2022

作者： Lam, Kim-Hung Chan, Chun Sang Chan, Wai Ki Vicky Mark, Kai-Pan Gatrell, Dave The Hong Kong Polytechnic University Department of Applied Biology and Chemical Technology Hong Kong Hong Kong The Hong Kong Polytechnic University Educational Development Unit Hong Kong Hong Kong

ISBN: (纸本)9781665491174

the COVID-19 pandemic with social distancing converts most of the face-to-face (F2F) classes to online/hybrid mode of class delivery. To better facilitate online/hybrid teaching and learning, pre-class or after-class learning activities, online videos/pre-class or post-class recordings, self-learning modules in learning Management System (LMS) as well as interactive technologies, can enhance students' learning during the *** preliminary evaluation about the use of virtual tutorials to assist the teaching and learning for two of our General Educational Subjects ("ABCT1D09 Greenhouse Gases and Life"& "ABCT1D10 Food Color, Taste and Smell") was reported in this paper. To understand the learning behavior and students' engagement, we applied learning analytics (LA) and the ICAP (Interactive/Constructive/Active/Passive) model in this *** online survey about our students' learning experience was conducted and system data of our students' "click"number within the platform Blackboard Collaborate Ultra (BB) with uRewind (empowered by Panopto) was collected. In this study, we discovered that our students welcome the use of virtual tutorials with rating average >3.5 for the voluntary online survey. the number of "click"for recorded videos and online teaching materials within LMS reached highest values near the assessment due date. In general, students performed better with more frequent usage of LMS teaching materials and videos. this observation revealed by a better performance of ABCT1D10 students with respect to ABCT1D09 students. the online materials may help students' learning and maintain their performance although they encountered a sudden change of study mode. © 2022 IEEE.

关键词： Students

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UAV-Assisted Fresh data Collection with MCS in Wireless Powered IoT 11th

UAV-Assisted Fresh Data Collection with MCS in Wireless Po...

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11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Yang, Fei Liu, Juan Xie, Lingfu Ningbo University Ningbo315211 China

ISBN: (纸本)9789819923618

In this paper, we study the unmanned aerial vehicle (UAV) assisted data collection problem to improve the information freshness in wireless powered Internet of things (IoT) networks. In our system, one UAV collects the sensing information from the sensors timely. To improve the system sustainability, one unmanned vehicles acts as a mobile charging station (MCS) to recharge the sensors wirelessly and replace the battery of the UAV when necessary. We aim to design the trajectory of the UAV and the moving path of the MCS jointly to minimize the average age of information (AoI) collected from the sensors. the problem is formulated as a partially observed Markov decision process (POMDP) with large state and action spaces. To seek the optimal solution, a multi-agent deep reinforcement learning algorithm based on value-decomposition networks (VDN) is developed to make real-time decisions according to partial observations in the environment. Simulation results verify the effectiveness of the proposed algorithm. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Multi-objective Multigraph A* Search with learning Heuristics based on Node Metrics and Graph Embedding

Multi-objective Multigraph A* Search with Learning Heuristic...

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international IEEE conference on intelligent Systems, IS

作者： Songwei Liu Jun Chen Michal Weiszer School of Engineering and Materials Science Queen Mary University of London London UK

ISBN: (纸本)9781665492768

To solve general multi-objective multigraph shortest path problems, this paper proposes an algorithm (MOMGA*) that incorporates an online likely-admissible learning-based heuristic function to accelerate the solution-finding process. MOMGA* is an extended and generalised version of the airport multi-objective A* (AMOA*) algorithm that is tailored for a specific application problem. the online heuristic function is added and developed using artificial neural networks that estimate the costs between two nodes based on their metrics. To implement this metric-based prediction, a graph embedding technique is adopted to learn node feature representations. Results on a range of benchmark multi-objective multigraphs show that (i) in the absence of heuristic information, MOMGA* can deliver the same Pareto optimal solutions as AMOA* does, while requiring less computational time, and (ii) empowered by the likely-admissible learning- based heuristics, MOMGA* is able to provide a set of optimal and near-optimal solutions and strike a good balance between optimality and tractability.

关键词： Measurement Shortest path problem Costs Heuristic algorithms Benchmark testing Pareto optimization Search problems

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A Robust Multi-Modal Emotion Recognition Framework for intelligent Tutoring Systems

A Robust Multi-Modal Emotion Recognition Framework for Intel...

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international conference on Advanced learning Technologies (ICALT)

作者： Xiaoqing Liu Lei Zhang Jacob Yadegar Niranjan Kamat Utopia Compression Corporation Los Angeles CA USA

this paper presents a multi-modal emotion recognition framework that is capable of estimating the human emotional state through analyzing and fusing a number of non-invasive external cues. the proposed framework consists of a set of data analysis, feature extraction and emotion recognition modules for processing heterogeneous sensory data (e.g., visual appearance and speech) and a novel probabilistic information fusion model to accurately estimate the human emotional state. Experimental results demonstrate that the proposed emotion recognition framework can automatically and robustly recognize human emotional states. Our results also proof that by fusing complementary information such as facial expression analysis and voice intonation analysis results, the emotion recognition performance can be boosted and outperform each individual modal analysis. the proposed emotion recognition framework can be integrated into existing intelligent Tutoring Systems (ITSs) for improving the effectiveness of the learning systems by providing feedbacks to the ITSs.

关键词： Emotion recognition Feature extraction Speech recognition Probabilistic logic Active appearance model Humans Robustness

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Analysis and Research on the Construction of Book Retrieval Platform Based on Disconnected Apriori Algorithm 11th

Analysis and Research on the Construction of Book Retrieval ...

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11th international conference on Frontier Computing, FC 2021

作者： Zhang, Zhu Guangzhou Vocational and Technical College of Foreign Economics Guangdong Guangzhou China

ISBN: (纸本)9789811680519

this paper introduces an improved Apriori algorithm. the algorithm can directly get the final frequent itemset by finding the maximum frequent itemset which is greater than the minimum holding count. the improved algorithm is applied to the library bibliography recommendation service. the analysis and Research on the construction of the book retrieval platform under the disconnected Apriori algorithm, the performance of the improved algorithm and Apriori algorithm is analyzed and the running time of the experimental data is compared. the experimental results show that the improved algorithm has a significant improvement in running speed and mining performance compared with the classical Apriori algorithm. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Cybersecurity Audits for Emerging and Existing Cutting Edge Technologies

Cybersecurity Audits for Emerging and Existing Cutting Edge ...

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intelligent Systems and Embedded Design (ISED), international conference on

作者： Sivasankaran Saravanan Anup Menon Kavya Saravanan Shanmugasundaram Hariharan Leema Nelson Jegadeesh Gopalakrishnan Department of Information Technology Sri Sairam Engineering College Chennai India Department of CSE Vardhaman College of Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Punjab India Department of IT Sri Sairam Engineering College Chennai India

the evolution of cybersecurity insurance is spurred by financial institutions aiming to manage costs from cyber incidents, particularly in the early stages like cloud-based services. Analyzing big data, secure assessments of cyber events are done, linking repository data for various risk scenarios. Challenges arise in securing cloud services offered by giants like Google, Amazon, and Microsoft, as control over security is ceded. Research develops models countering cloud attacks to boost security. the role of cybersecurity in cloud service and application development is stressed. A comprehensive approach addresses Distributed Denial of Service (DDoS) attacks, evaluating algorithms using metrics. Blockchain's integration with cybersecurity is explored via text mining, revealing themes, vulnerabilities, and risks. Businesses moving to the cloud raise security concerns. data-driven methods identify cloud vulnerabilities and propose solutions. Blockchain's potential and challenges in cybersecurity are highlighted. AI-driven defenses for Big data are crucial. AI, Big data, and blockchain's synergy is examined for security implications. Blockchain bolsters Cyber-Physical Systems security and healthcare record integrity. AI eases cybersecurity with machine learning. Collaborative mechanisms for sharing threat information are discussed. the study explores AI-driven cybersecurity frameworks for autonomous threat detection, analyzing their defensive and offensive roles amidst evolving challenges. the complex interplay between AI, cyber warfare, and modern cybersecurity is emphasized.

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InterCriteria Analysis of the Global Competitiveness Report for the Financial System EU Countries

InterCriteria Analysis of the Global Competitiveness Report ...

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international IEEE conference on intelligent Systems, IS

作者： Slaviiana Danailova - Veleva Lyubka Doukovska Peter Vassilev Intelligent Systems Department Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Bioinformatics and Mathematical Modelling Department Institute of Biophysics and Biomedical Engineering Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9781665492768

In recent years, innovative techniques for data analysis and decision-making, resulting from the processing of a large database, have been increasingly used. this trend is part of the management process, for example in the field of finance, where through the application of elements of artificial intelligence, data is processed, processes are analyzed, risk is assessed and intelligent solutions are *** this paper, data from the Global competitiveness report of the World Economic Forum is processed in order to analyze the financial systems of EU Countries. the emphasis is on the data from Pillar 9 - Financial System. It consists of the indicators - Debt and Stability. these in turn include several *** analysis was carried out by applying the Intercriteria Analysis.

关键词： Economics data analysis databases Decision making Finance Banking Market research

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