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Does UI Labeling Data Quality Matter for Predicting Website Aesthetics 25th

Does UI Labeling Data Quality Matter for Predicting Website ...

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25th international conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2023

作者： Abbasov, Elnur Bakaev, Maxim Novosibirsk State Technical University Pr. K. Marksa 20 Novosibirsk630073 Russia

ISBN: (纸本)9783031678257

the adoption of today’s data-intensive digital services relies on the overall user experience (UX), which is shaped not just by "hard" functionality, but also by "soft" subjective satisfaction. In the latter, aesthetic impression plays an important role (particularly since visually pleasing products are known to be perceived as more usable) and became a popular prediction objective for machine learning (ML) based user behavior models. Since datasets in the field of Human-computer Interaction are generally too scarce for application of deep learning methods that could operate on raw website screenshots, they often undergo preliminary labeling. Although the common notion is that the quality of the labeling is important for the end quality of the predictive models, there were few attempts to quantify the effect. In a previous study, we unexpectedly found significant negative correlations between the input data quality and the models’ quality for Aesthetics and Orderliness subjective impressions. Our current paper is dedicated to validating the findings with another 557 website screenshots, 31 human participants labeling them, and 22 participants verifying the quality of their work. the non-parametrical models (Nadaraya-Watson kernel regression) with feature selection demonstrated somehow better performance, and the combined dataset better aligned with the expected effect of the labeling quality. Although our overall results are inconclusive, they might be of interest to ML practitioners and web designers who seek to automate the prediction of UX dimensions. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Automatic Irrigation and Crop Monitoring using machine learning and Internet of things

Automatic Irrigation and Crop Monitoring using Machine Learn...

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Sustainable Expert Systems (ICSES), international conference on

作者： Inderpreet Kaur Snehal Pradeep Patil Bhukya Shankar G H Kerinab Beenu G. Suresh Kumar D. V. Lokeswar Reddy G. Ramachandran Rayat Bahra University Mohali MCA D. Y. Patil Institute of Master of Computer Applications and Management Pune Maharashtra Department of Electronics and Communication Engineering CVR College of Engineering Ibrahimpatnam Hyderabad MBA Velammal Engineering College Surapet ECE Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Deemed to be University Salem India Humanities and Basic Sciences Department JNTU College Of Engineering Kadapa (D) Andhra Pradesh India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

the Crop environmental monitoring system with intelligence multidimensional and multi-angle crop environment monitoring system is created to satisfy the demands of contemporary society for agricultural crop environmental monitoring. the system gathers, processes, and forwards data to the network module by connecting the appropriate data gathering sensors. Ultimately, the data is sent to the information processing center for accurate processing and analysis before being visually displayed. Ultimately, the system's ability to achieve the desired result, have a high application value, and effectively increase agricultural environment surveillance efficiency is fully demonstrated by the testing and verification of its operation. Low-cost intelligent modules for smart irrigation systems powered by IoT and AI In India, agriculture is the primary source of income for the majority of people. It is essential to use cutting edge technologies to safeguard priceless water supplies. In addition to serving as Industry 4.0's cornerstone, IoT improves smart agriculture. the goal of the research is to develop a cutting-edge, affordable system for intelligent irrigation. Self-talk and inter-device networking are made possible by IoT.

关键词： Irrigation Accuracy Crops Moisture Soil Control systems Systems simulation Environmental monitoring System analysis and design Testing

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Design and application of the Digital Human Identity Attribute Data Synthesis System

Design and Application of the Digital Human Identity Attribu...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Yapeng Ji Yangyang Li Yunji Liang Bin Guo Yangzhao Yang School of Computer Science Northwestern Polytechnical University Xi’an China Academy of Cyber Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In the era of big data, increasing demand for identity data sharing in fields like healthcare and tourism, along with stricter cybersecurity regulations, has made generating realistic digital human identity attribute data and ensuring its efficient use a critical challenge. However, existing methods struggle with complex data types and distributions, often neglecting the social and logical consistency of the synthetic data. To address this, we propose a Conditional Generative Adversarial Network (CGAN)-based system for synthesizing digital human identity attribute data. the system features three modules: data preprocessing, data synthesis, and data verification. the preprocessing module uses a composite encoder to handle discrete, continuous, and mixed data types, with a Variational Gaussian Mixture Model (VGM)-based normalization for continuous variables. the synthesis module introduces conditional vector-based sampling to resolve data imbalance while preserving data distribution characteristics, enhancing the realism of the generated data. the verification module uses association rule mining for secondary validation, ensuring logical consistency. this modular design improves scalability, paving the way for multimodal data synthesis. Experimental results show that the system excels in statistical similarity and machine learning utility, with the verification module boosting rule retention by 15%, significantly improving data quality and practical value. this study provides an effective solution with important theoretical and practical implications for digital human identity attribute data synthesis.

关键词： Scalability Data integrity Data preprocessing Medical services Generative adversarial networks Regulation Digital humans Digital twins Gaussian mixture model Synthetic data

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Optimizing Energy Management and Load Balancing through AI-Driven Quantum Approximate Optimization

Optimizing Energy Management and Load Balancing Through AI-D...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Manjunath Kamath K Manasa M Anitha B. S Mahadevi Falguni Tlajiya Sangramjit Chavan Department of Cyber Security NMAM Institute of Technology NITTE (Deemed to be University) Karkala India Department of Artificial Intelligence and Machine Learning Brindavan College of Engineering Bengaluru India Department of Computer Science Sarada Vilas College Mysuru India Department of Electrical Engineering Parul institute of Engineering and Technology Parul University Gujarat India Symbiosis Law School (SLS) Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

the integration of Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) offers a promising approach to addressing energy management and load balancing challenges in modern power systems. this study explores the application of these quantum computing techniques, coupled with Artificial Intelligence (AI), to optimize energy distribution across smart grids. the proposed hybrid model leverages the problem-solving capabilities of QAOA for discrete optimization and the real-time adaptability of QA for minimizing energy consumption and operational costs. AI techniques are employed to predict energy demand, balance loads, and ensure efficient resource allocation. Simulations were conducted on a smart grid scenario involving renewable energy sources, varying demand, and distributed energy generation. the results demonstrate that the hybrid model achieved a 25% improvement in load balancing efficiency and a 30% reduction in energy wastage compared to traditional optimization methods. Additionally, the model reduced peak energy demand by 20%, contributing to overall grid stability. the quantum-enhanced approach also decreased computational complexity by 40%, enabling faster decision-making in dynamic environments. these findings highlight the potential of combining quantum algorithms with AI for sustainable and efficient energy management.

关键词： Data centers Renewable energy sources Quantum algorithm Computational modeling Power system stability Load management Quantum annealing Artificial intelligence Optimization Load modeling

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application of Homomorphic Encryption in Blockchain Data Security 20

Application of Homomorphic Encryption in Blockchain Data Sec...

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proceedings of the 2020 4th international conference on Electronic Information Technology and computer Engineering

作者： Jingjing Chen Fucheng You Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781450387811

With the continuous development of the Internet era, people's demand for network security is increasingly high, information is an essential component of the network, which implies numerous privacy, secrets, but also contains a large number of value, thus generating a multiparty trust issues of data security. Blockchain technology itself is still in the early stages of rapid development, the existing blockchain system in the design and implementation of the use of distributed systems, cryptography, game theory, network protocols and many other disciplines, for learning the principles and practical applications have brought considerable challenges. the blockchain, with its decentralized quality, quickly captures the attention of the public and solves many security problems derived from data security, thus attracting wide attention from people. In recent years, along with the increasing maturity of homomorphic encryption technology and push new, it is more and more people's favour and *** encryption is an encryption technology that provides the ability to perform various operations on data in an encrypted state without compromising its confidentiality. Its concern is the security of data processing. that is, others can process the encrypted data, but the process does not reveal any of the original content. Also, the user who has the Key decrypts the processed data and gets the result of the processing *** main encryption technique used in blockchain applications is the RSA encryption algorithm, [1] which is used to ensure that the user's private key is not compromised, but no project has been noticed about the homomorphic encryption method for smart contracts. [2] the purpose of this paper is to analyze the principle of homomorphic encryption and discuss its practical application in data security in combination with blockchain technology.

关键词： Blockchain technology Homomorphic encryption Data security

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machine learning Technique for Energy, Performance and Cost-Effective Resource Management in Multi-Access Edge Computing 22

Machine Learning Technique for Energy, Performance and Cost-...

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proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Vanjipriya V Suresh Annamalai Department of Networking and Communications SRM Institute of Science and Technology India

ISBN: (纸本)9781450399937

Modern cloud interconnects on efficient resource allocation and provisioning to reduce their energy footprint. Resource management is complicated by factors such as data centre energy utilisation, virtual machine migration, operating expense, and overhead. Researchers have been using virtualized technologies and methods as optimal-Multi-objective particle swarm optimization, Dynamic Power Saving Resource Allocation (DPRA), Least Squares Regression, etc. to improve the management of their study. Accurately allocating resources to cloud users to meet their requests and offer QoS is a difficult task because of the preceding steps. Allocating cloud infrastructure's resources in the most efficient way possible benefits both users and service providers. the difficulties of resource management are tackled in this study by employing novel approaches, heuristics, authentication, and virtualization. In order to distribute workloads over several physical nodes, cloud computing relies on dynamic scheduling with load balancing. Using the help of host load prediction and a Markov chain model with Particle Swarm Optimization (PSO), VM resources are dynamically allocated to appropriate input requests. High quality of service (QoS) for cloud applications is achieved by SLA-based resource optimization with deadline, cost, storage, and bandwidth targets. Compliance with Service Level Agreements (SLAs), efficient use of resources, and low energy consumption are all achieved using a prioritisation technique based on SLAs. Scheduled users can receive resources in a predetermined order thanks to queuing. We developed the M/M/c/K queuing paradigm for numerous users per server to lessen the burden on data centres. Hardware resource models, such as CPU, I/O, and memory use, reveal VM resource allocation. Information gathering enhances resource utilisation and reduces energy consumption.

关键词： Dynamic Power Saving Resource Allocation

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MEDSAT: a public health dataset for england featuring medical prescriptions and satellite imagery 23

MEDSAT: a public health dataset for england featuring medica...

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proceedings of the 37th international conference on Neural Information Processing Systems

作者： Sanja Šćepanović Ivica Obadic Sagar Joglekar Laura Giustarini Cristiano Nattero Daniele Quercia Xiao Xiang Zhu Nokia Bell Labs and Technical University of Munich Technical University of Munich and Munich Center for Machine Learning Nokia Bell Labs and Intercom WASDI Nokia Bell Labs and Centre for Urban Science and Progress King's College London

As extreme weather events become more frequent, understanding their impact on human health becomes increasingly crucial. However, the utilization of Earth Observation to effectively analyze the environmental context in relation to health remains limited. this limitation is primarily due to the lack of fine-grained spatial and temporal data in public and population health studies, hindering a comprehensive understanding of health outcomes. Additionally, obtaining appropriate environmental indices across different geographical levels and timeframes poses a challenge. For the years 2019 (pre-COVID) and 2020 (COVID), we collected spatio-temporal indicators for all Lower Layer Super Output Areas in England. these indicators included: i) 111 sociodemographic features linked to health in existing literature, ii) 43 environmental point features (e.g., greenery and air pollution levels), iii) 4 seasonal composite satellite images each with 11 bands, and iv) prescription prevalence associated with five medical conditions (depression, anxiety, diabetes, hypertension, and asthma), opioids and total prescriptions. We combined these indicators into a single MEDSAT dataset, the availability of which presents an opportunity for the machine learning community to develop new techniques specific to public health. these techniques would address challenges such as handling large and complex data volumes, performing effective feature engineering on environmental and sociodemographic factors, capturing spatial and temporal dependencies in the models, addressing imbalanced data distributions, developing novel computer vision methods for health modeling based on satellite imagery, ensuring model explainability, and achieving generalization beyond the specific geographical region.

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Deep learning Dermoscopy: Unveiling CNN-SVM Synergy in Skin Lesion Detection

Deep Learning Dermoscopy: Unveiling CNN-SVM Synergy in Skin ...

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Intelligent Technologies (CONIT), international conference on

作者： Ankita Suryavanshi Shiva Mehta Ayushi Jain Siddhant thapliyal Shanmugasundaram Hariharan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

the created algorithm was assessed rigorously, employing numerous performance metrics that were calculated per the categories of skin lesion detection. the precision for each class ranged from 75.00% to 87.85%, indicating that the majority of the model’s positive predictions were events that did indeed occur. To manage Class 5 to be the best in precision compared to other levels. the recall metrics, which means the announced level of the model’s accuracy in searching for all examples from each class, has a performance range of 72.84% to 84.81%, with Class 2 having the greatest recall. the F1-Score, which includes accuracy and recall, corresponds to a percentage that ranges from 73.90% to 83.00%. Further, the study applied various types of averages in order to establish how the model would apply to different classes. the model workout was done on the wise 79.30%, 79.40%, and 79.23% micro-average for accuracy, recall, and F1-score. Results thus attest that an equal performance in all the classes was achieved without the effect of any class imbalance. the weighted mean, giving similar consideration to the occurrence of real cases in each class, generated accuracy, recall, and F1 scores of 79.48%, 79.24%, and 79.24%, respectively, having a small better bent for classes having greater prevalence. Finally, the Micro-average is demonstrated as it is made up of contributions of all classes, and it shows consistent performance across all those Groups, having accuracy, recalling, and F1-Score all equal to 79.24%. As a result of this research, AI skin lesion identification systems are exposed to the application of CNN-SVM models that can perfectly manage medical diagnostics in future, and the shorter the adaptions and the further model simplification, the higher the relevance of performance evaluation options.

关键词： Support vector machines Performance evaluation Adaptation models Accuracy Neural networks Predictive models Prediction algorithms

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A Hybrid Deep learning Model for Breast Cancer Detection and Classification 23

A Hybrid Deep Learning Model for Breast Cancer Detection and...

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proceedings of the 2023 13th international conference on Communication and Network Security

作者： Shanshan Tu Wenlong Li Xin Ai Hongchen Li Qingqing Yue Sadaqat Ur Rehman Department of Informatics Beijing University of Technology China Department of Informatics University of Salford UK

ISBN: (纸本)9798400707964

One of the main areas of study in diagnostic radiology and medical imaging is computer-aided diagnosis (CAD). In reality, a significant number of CAD systems have been used to help doctors identify breast tumours early on mammograms. Medical image analysis algorithms are helpful in providing a better and more accurate comprehension of medical images as well as in boosting the dependability of medical images in the healthcare system because therapy and illness diagnosis are so crucial in medical imaging. Leveraging advanced machine learning techniques has become an invaluable tool for healthcare professionals, enhancing early detection and personalizing treatment ***, in this work, we began by mentioning several cutting-edge techniques for detecting breast cancer using a deep learning methodology. the primary drawback of current research is that existing models only concentrate on identifying or detecting benign or malignant tumours, rather than specific types of tumours such adenosis, phyllodes tumour, or lobular carcinoma. We used a number of lightweight deep learning models, like ShuffleNet, to create a flexible model. [11]Additionally, in order to achieve recognition, we create the Resnet50 classical CNN model based on transfer *** incorporating transfer learning, the model can effectively use pre-trained networks to enhance its learning capability, potentially yielding better performance than training from *** of lightweight deep learning models, like ShuffleNet, to create a flexible model. [4] A new multi-label breast cancer tissues classification model that combines the benefits of Resnet and the Attention mechanism is also taken into *** innovative approach is designed to focus on specific regions of interest within the images, which can potentially lead to a higher accuracy in detecting subtler signs of various tumour types.

关键词： Attention Mechanism Breast cancer ResNet detection

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Apply machine-learning Model for Clustering Rowing Players 23

Apply Machine-Learning Model for Clustering Rowing Players

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proceedings of the 2023 12th international conference on Networks, Communication and Computing

作者： Patcharawit Wilaikaew Watchara Noisriphan Chien Chang Chen Jirawan Charoensuk Somchoke Ruengitinun Chalothon Chootong Department of Computer Science and Information Faculty of Science at Sriracha Kasetsart University Sriracha Campus Thailand Department Of Computer Science and Information Engineering Faculty of Engineering Tamkang University Taiwan Department of Computer Science Faculty of Science Kasetsart University Thailand

ISBN: (纸本)9798400709265

Rowing, as a sport composed of single player or multiple players, performs body movements under certain rhythm with slight variation. the analysis of rhythm alternation or match is important on rowing research and merit our study. therefore, this study analyzes the rowing movements by the following three procedures, rowing cycle segmentation, feature extraction, rowing cycle clustering. the rowing cycle segmentation procedure segments each player's video to videos of single cycle under the analysis of MediaPipe detected joint points. the feature extraction procedure calculates features from each rowing cycle by selecting amplitudes, angles, angular speeds of 4 selected joint points. At last, the rowing cycle clustering procedure analyzes all one-cycled videos using above features by different clustering and scoring methods. three clustering methods, including K-means, Birch, and Gaussian-mixture, are experimented in this study for finding the most efficient one. A hybrid measurement from the Silhouette score, the Calinski-Harabasz index, and the Davies-Bouldin index, is proposed for finding the optimal clusters number. Experimental results of 15 players’ videos show that applying K-means clustering algorithm with the proposed hybrid measurement performs better for finding the rowing group.

关键词： Birch clustering Fast Fourier Transform Gaussian mixture-clustering K-means clustering Pose Detection Rowing Sports

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