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machine learning algorithms for Improved Product Design User Experience

IEEE ACCESS 2024年 12卷 112810-112821页

作者： Wang, Xueli Hu, Bo Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China Suzhou Univ Sch Mech & Elect Engn Suzhou 234000 Peoples R China

With the rapid advancement of technology and the increasing demand for user-centric products, the integration of machine learning techniques has become imperative. This paper explores the transformative potential of integrating Particle Swarm Optimization (PSO), Deep Reinforcement learning (DRL), and other machine learning algorithms such as neural networks, decision trees, and support vector machines into product design processes. Our novel hybrid framework leverages PSO's global search capabilities and DRL's adaptive learning to optimize product designs in a manner that traditional methods cannot achieve. By employing predictive modeling, clustering, and recommendation systems, designers can gain valuable insights into user needs and preferences, facilitating the creation of more intuitive and personalized products. We demonstrate that this integrated approach significantly improves design efficiency and user satisfaction. Key findings include a 25% reduction in design iteration time and a 30% increase in user satisfaction scores compared to traditional optimization methods. Additionally, our methodology provides a flexible and scalable solution adaptable to various product design contexts, showcasing its broad applicability and effectiveness. The incorporation of real-time feedback mechanisms allows for continuous refinement and adaptation of product designs to meet evolving user expectations. This study contributes to the field by presenting a comprehensive, multi-technique optimization framework that bridges existing gaps and sets a new standard for user-centric product design optimization. Ultimately, this research underscores the significance of embracing machine learning as a powerful tool for revolutionizing the product design landscape and delivering superior user experiences.

关键词： machine learning algorithms product design user experience enhancement user data analysis predictive modeling product design user experience enhancement user data analysis predictive modeling

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machine learning algorithms for improving security on touch screen devices: a survey, challenges and new perspectives

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NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 13651-13678页

作者： Bello, Auwal Ahmed Chiroma, Haruna Gital, Abdulsalam Ya'u Gabralla, Lubna A. Abdulhamid, Shafi'i M. Shuib, Liyana Abubakar Tafawa Balewa Univ Dept Math Sci Bauchi Nigeria Fed Coll Educ Tech Dept Comp Sci Gombe Gombe Nigeria Princess Nourah Bint Abdulrahman Univ Community Coll Dept Comp Sci & Informat Technol Riyadh Saudi Arabia Fed Univ Technol Dept Cyber Secur Sci Minna Minna Nigeria Univ Malaya Fac Comp Sci & Informat Technol Dept Informat Syst Kuala Lumpur Malaysia

Mobile phone touch screen devices are equipped with high processing power and high memory. This led to users not only storing photos or videos but stored sensitive application such as banking applications. As a result of that the security system of the mobile phone touch screen devices becomes sacrosanct. The application of machine learning algorithms in enhancing security on mobile phone touch screen devices is gaining a tremendous popularity in both academia and the industry. However, notwithstanding the growing popularity, up to date no comprehensive survey has been conducted on machine learning algorithms solutions to improve the security of mobile phone touch screen devices. This survey aims to connect this gap by conducting a comprehensive survey on the solutions of machine learning algorithms to improve the security of mobile phone touch screen devices including the analysis and synthesis of the algorithms and methodologies provided for those solutions. This article presents a comprehensive survey and a new taxonomy of the state-of-the-art literature on machine learning algorithms in improving the security of mobile phone touch screen devices. The limitation of the methodology in each article reviewed is pointed out. Challenges of the existing approaches and new perspective of future research directions for developing more accurate and robust solutions to mobile phone touch screen security are discussed. In particular, the survey found that exploring of different aspects of deep learning solutions to improve the security of mobile phone touch screen devices is under-explored.

关键词： machine learning algorithms Deep learning Mobile phone touch screen Android Support vector machine Command attention Security

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machine learning algorithms for event detection

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machine learning 2010年第3期79卷 257-259页

作者： Margineantu, Dragos Wong, Weng-Keen Dash, Denver Boeing Res & Technol Seattle WA USA Oregon State Univ Corvallis OR 97331 USA Intel Labs Pittsburgh PA USA

A common task in many machine learning application domains involves monitoring routinely collected data for \'interesting\' events. This task is prevalent in surveillance, but also in tasks ranging from the analysis of scientific data to the monitoring of naturally occurring events, and from supervising industrial processes to observing human behavior. We will refer to this monitoring process with the purpose of identifying interesting occurrences, as event detection. We put together this special issue of the machine learning journal with the belief that principled machine learning approaches can and will be a differentiator in addressing event detection tasks, and that theoretical and practical advances of machine learning in this area have the potential to impact a wide range of important real-world applications such as security, public health and medicine, biology, environmental sciences, manufacturing, astrophysics, business, and economics. In the recent past, domain experts in these areas have had the laborious job of manually examining the collected data for events of interest. With the emergence of computers, many efforts have been made to replace manual inspection with an automated process. Data, however, have become increasingly complex, and the quantities of collected data have become extremely large in recent years. Multivariate records, images, video footage, audio recordings, spatial and spatio-temporal data, text documents, and even relational data are now routinely collected.

关键词： Event detection machine learning Process monitoring Data Collection Methods Religious Missions machine learning algorithms Artificial Intelligence DIFFERENTIATORS economics

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machine learning algorithms for Automated NIF Capsule Mandrel Selection

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FUSION SCIENCE AND TECHNOLOGY 2020年第6期76卷 749-757页

作者： Boehm, K. -J. Ayzman, Y. Blake, R. Garcia, A. Sequoia, K. Sundram, S. Sweet, W. Gen Atom POB 85608 San Diego CA 92186 USA Lawrence Livermore Natl Lab POB 808 Livermore CA 94550 USA

Small shells, approximately 2 mm in diameter, made from Poly(alpha-methylstyrene) (PAMS) are used as mandrels in the production of glow discharge polymer capsules located at the center of inertial confinement fusion experiments. The visual inspection process of microscope images of these shell mandrels, including detection of micron-sized defects on the shell surface as well as the handling and sorting, is a very labor-intensive, repetitive, and highly subjective process that stands to benefit greatly from automation. As part of an effort to decrease the number of labor hours spent in capsule handling, inspection, and metrology, the development of robotic systems was presented in a paper by Carlson et al., "Automation in Target Fabrication" [Fusion Sci. Technol., Vol. 70, p. 274 (2016)]. The current work expands the automated image acquisition systems developed previously and adds the use of convolutional neural networks to select capsules best suited for use in the downstream production process. Through the use of these machine learning algorithms, the selection process becomes robust, repeatable, and operator independent. As an added benefit the system developed as part of this work is able to provide defect statistics on entire shell batches and feed this information upstream to the production team.

关键词： machine learning algorithms image acquisition capsule mandrels convolutional neural networks

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machine learning algorithms for the evaluation of risk by tick-borne pathogens in Europe

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ANNALS OF MEDICINE 2024年第1期56卷 2405074页

作者： Estrada-Pena, Agustin de la Fuente, Jose Univ Zaragoza Fac Vet Med Dept Anim Hlth Zaragoza Spain Inst Invest Recursos Cineget SaBio Hlth & Biotechnol IRECCSIC UCLMJCCM Ciudad Real Spain Oklahoma State Univ Ctr Vet Hlth Sci Dept Vet Pathobiol Stillwater OK USA

BackgroundTick-borne pathogens pose a major threat to human health worldwide. Understanding the epidemiology of tick-borne diseases to reduce their impact on human health requires models covering large geographic areas and considering both the abiotic traits that affect tick presence, as well as the vertebrates used as hosts, vegetation, and land use. Herein, we integrated the public information available for Europe regarding the variables that may affect habitat suitability for ticks and hosts and tested five machine learning algorithms (MLA) for predicting the distribution of four prominent tick species across *** and methodsA grid of cells 20 km in diameter was prepared to cover the entire territory, containing data on vegetation, points of water, habitat fragmentation, forest density, grass extension, or imperviousness, with information on temperature and water deficit. The distribution of the hosts (162 species) was modelled and included in the dataset. We used five MLA, namely, Random Forest, Neural Networks, Naive Bayes, Gradient Boosting, and AdaBoost, trained with reliable coordinates for Ixodes ricinus, Dermacentor reticulatus, Dermacentor marginatus, and Hyalomma marginatum in *** Random Forest and Gradient Boosting best predicted ticks and host environmental niches. Our results demonstrate that MLA can identify trait-matching combinations of environmental niches. The inclusion of land cover and land use variables has a superior capacity for predicting areas suitable for ticks, compared to classic methods based on the use of climate data *** MLA-driven models may offer several advantages over traditional models. We anticipate that these results may be extrapolated to other regions and combinations of tick-vertebrates. These results highlight the potential of MLA for inference in ecology and provide a background for the evolution of a completely automatized tool to calculate the seasonality of ticks for ear

关键词： Tick habitat suitability modelling Europe hosts distribution landscape features machine learning algorithms human health

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machine learning algorithms ASSESSMENT FOR SNOW LWC RETRIEVAL FROM SAR DATA

MACHINE LEARNING ALGORITHMS ASSESSMENT FOR SNOW LWC RETRIEVA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Santi, Emanuele Paloscia, Simonetta Pettinato, Simone Baroni, Fabrizio Pilia, S. Colombo, Roberto Ravasio, Claudia Di Mauro, Biagio CNR Inst Appl Phys CNR IFAC Florence Italy Univ Milano Bicocca Dept Earth & Environm Sci Milan Italy ISP CNR Inst Polar Sci Venice Italy

ISBN: (纸本)9798350360332;9798350360325

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X-band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as "model-driven". The second one was defined as "data-driven". The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" R-ANN = 0.60, R-RF = 0.68;"data-driven" R-ANN = 0.50, R-RF = 0.88 at X-band). Moreover, the RF implementation trained with the "data-driven" approach outperformed the "model-driven" approach in terms of correlation coefficient R (R-RF = 0.88 at X-band).

关键词： SAR snow machine learning algorithms Artificial Neural Network Random Forest

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machine learning algorithms to Detect Penetrations ofPVDF 9

Machine Learning Algorithms to Detect Penetrations ofPVDF

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhai, Yayu Song, Ping Chen, Xiaoxiao Beijing Inst Technol Lab Biornimet Robots & Syst Beijing Peoples R China

ISBN: (纸本)9781538665657

Polyvinylidene fluoride (PVDF) has widely used in detecting the interplanetary dust, In the case of penetration and non-penetration, the output signals of the PVDF are quite different. Detecting whether particles penetrate PVDF is a crucial issue. We create a set of experimenral equipment for collecting the signals from the PVDF. The equipment consists of particle emitter, shield, conditioning circuits and data acquisition equipment. 600 experiments are conducted, Among 200 experiments, the particles penetrate PVDF. We successfully distinguish penetration of PVDF using four machine learning algorithms: Anomaly Detection (AD), Artificial Neural Network (ANN), K-Nearest-Neighbors (KNN), and Support Vector machines (SVM). We propose a unique evaluation criteria OP to evaluate the performance of four classifiers including their accuracy and computational time. The results show that ANN is the best machine learning algorithm for ow problem, and AD is not suitable for our problem.

关键词： machine learning algorithms PVDF classification feature extraction and reduction

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machine learning algorithms for population-specific risk score in coronary artery bypass grafting

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ASIAN CARDIOVASCULAR & THORACIC ANNALS 2023年第4期31卷 348-356页

作者： Swamy, Abhyuday Kumara Rajagopal, Vivek Krishnan, Deepak Ghorai, Paramita Auddya Palani, Santhosh Rathnam Narayan, Pradeep Medha Analyt Adv Analyt & AI Narayana Hlth Bengaluru India Narayana Hlth Dept Cardiothorac Surg Bengaluru India

Background The aim of this study was to develop a new risk prediction score (NH Score) for patients undergoing coronary artery bypass grafting (CABG) specific to the Indian population and compare it to the Society of Thoracic Surgeon (STS) Score and the EuroSCORE II. Method The baseline features of adult patients who underwent CABG between the years 2015 and 2021 (n = 6703) were taken and split into training data (2015-2020;n = 5561) and validation data (2020-2021;n = 1142). The CatBoost algorithm was trained to predict risk scores (NH score), and the performance was tested on the validation set by Precision-Recall Curve and F1 Score. Model calibration was measured by the Brier Score, Expected Calibration Error and Maximum Calibration Error. Results The NH score outperformed both the STS and EuroSCORE II for all outcomes. For mortality, the PR AUC for NH Score was (0.463 [95% confidence interval [CI], 0.28-0.64]) compared to 0.113 [95% CI, 0.04-0.22] for the STS score and 0.146 [95% CI, 0.06-0.31] for the EuroSCORE II (p MUCH LESS-THAN 0.0001). With respect to morbidity NH Score was superior to the STS score (0.43 [95% CI, 0.33-0.50]) vs. (0.229 [95% CI, 0.18-0.3, p < 0.0001). The observed to the predicted ratio for NH score was superior to the STS Score and similar to EuroSCORE II. NH Score was also more accurate at predicting the risk of prolonged ventilation compared to the STS Score. Conclusion NH score shows an excellent improvement over the performance of STS score and EuroSCORE II for modelling risk predictions for patients undergoing CABG in Indian population. It warrants further validation for larger datasets.

关键词： machine learning algorithms risk-score CABG

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machine learning algorithms for Environmental Sound Recognition: Towards Soundscape Semantics 15

Machine Learning Algorithms for Environmental Sound Recognit...

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Audio Mostly (AM) Conference on Sound, Semantics and Social Interaction

作者： Bountourakis, Vasileios Vrysis, Lazaros Papanikolaou, George Aristotle Univ Thessaloniki AUTh Univ Campus Thessaloniki 54124 Greece

ISBN: (纸本)9781450338967

This paper investigates methods aiming at the automatic recognition and classification of discrete environmental sounds, for the purpose of subsequently applying these methods to the recognition of soundscapes. Research in audio recognition has traditionally focused on the domains of speech and music. Comparatively little research has been done towards recognizing non-speech environmental sounds. For this reason, in this paper, we apply existing techniques that have been proved efficient in the other two domains. These techniques are comprehensively compared to determine the most appropriate one for addressing the problem of environmental sound recognition.

关键词： Environmental Sound Recognition audio classification semantic audio analysis computer audition feature extraction feature selection machine learning algorithms

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machine learning algorithms for Early and Accurate Diabetic Detection 2nd

Machine Learning Algorithms for Early and Accurate Diabetic ...

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3rd International Conference on Digital Technologies and Applications

作者： Chaaouan, IIanae Bouhadda, Mohamed El Alami, Rachid Essahlaoui, Abdelouahed El Ghzaoui, Mohammed Qjidaa, Hassan Jamil, Mohammed Ouazzani Sidi Mohammed Ben Abdellah Univ Fac Sci Dhar El Mehraz LESSI BP 1796 Fes Morocco Sidi Mohammed Ben Abdellah Univ Multidisciplinary Fac Engn Sci Lab LSI Taza Morocco UPF LSEED Lab Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

The diagnosis of diabetes requires many physical and chemical tests, as well as many other tests, although untreated and undiagnosed diabetes causes the destruction of body organs such as the eyes, heart, kidneys, feet, and nerves and may result in loss of life. Therefore, early detection and analysis of diabetes can help reduce mortality rates. In this paper, we develop accurate machine learning models for detecting diabetes. These models are based on three algorithms: the first is Logistic Regression (LR), the second is Support Vector machine (SVM) and the third is Random Forest Classifier (RFC). They are performed on Diabetes data known as PIDD, which is obtained from the website Kaggle. Thereafter, the performance of predictive models is evaluated using accuracy, sensitivity, and F1 score measures. The model based on predictive learning of random forests emerged as one of the best performing models with 0.96 accuracy, 0.99 sensitivity, and 0.97 F1-score.

关键词： machine learning algorithms diabetes detection predictive model anomaly detection model

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