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DESI: Diversification of E-Commerce Recommendations Using Semantic Intelligence 1

6th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2023

作者： Deepak, Gerard Anavkar, Harshada Vinay Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Mechanical Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9783031554865

ISBN: (纸本)9783031554858

Due to the dynamism in the Web 3.0 there is need for semantically driven framework for ecommerce based recommendations. This paper proposes the DESI framework, which is a query-driven, semantically oriented, Web 3.0 conforming ecommerce recommendation framework. The pre-process query was enriched using the Latent Semantic Indexing. Ontologies are generated from the ecommerce product dataset. The classification of the metadata takes place using the LSTM classifier. The relevance computation is staged and is achieved using Lin similarity, Adaptive Pointwise Mutual Information measure. The semantics oriented reasoning on the basis of semantic similarity measures yield the matching products and Frog Leap algorithm. The normalized point wise mutual information measure is used to compute the intermediate results and probability algorithm ensures optimality computation from the initial feasible solution set. An overall precision accuracy measure of so and so on with the lowest value of False Discovery Rate has been achieved with a proposed framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantics

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Glass Wet Deep Etching for Fabricating Biomimetic Devices in Biosensing 15

Glass Wet Deep Etching for Fabricating Biomimetic Devices in...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Li, Yuxin Duan, Zhenxia Wang, Jie Zhou, Zijian Yang, Ming Wu, Enqi Du, Lin University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200433 China

ISBN: (纸本)9798350312980

The advancement of semiconductor fabrication methods has sped up the development of bionic structures and biosensing technologies. The device structure of an imitation lotus leaf and the digital biomolecular diagnostic technology are developed in this work using the wet etching procedure. The mold formed of high borosilicate glass BF33 has the benefit of low roughness and superior etching controllability over a calcium sodium glass, and the roundness parameter is raised by an average of more than 4% up to an etching depth of more than 100 μm. The produced droplet volume by device preparation can have a relative standard deviation as low as 4.5%, and the one-time filling rate is greater than 90%. The novel tool is used in biological Loop-Mediated Isothermal Amplification (LAMP) assessment to confirm that digital detection and biocompatibility are feasible. © 2023 IEEE.

关键词： BF33 glass droplet array LAMP wet deep etching

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OPTIMIZING DESIGN AND CONTROL OF RUNNING ROBOTS ABSTRACTED AS A TORQUE DRIVEN SPRING LOADED INVERTED PENDULUM

OPTIMIZING DESIGN AND CONTROL OF RUNNING ROBOTS ABSTRACTED A...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Truax, Reed Liu, Feng Chowdhury, Souma Pierre, Ryan St. Department of Mechanical and Aerospace Engineering University at Buffalo BuffaloNY14228 United States Department of Computer Science and Engineering University at Buffalo BuffaloNY14228 United States

ISBN: (纸本)9780791888377

Legged locomotion shows promise for running in complex, unstructured environments. Designing such legged robots requires considering heterogeneous, multi-domain constraints and variables, from mechanical hardware and geometry choices to controller profiles. However, very few formal or systematic (as opposed to ad hoc) design formulations and frameworks exist to identify feasible and robust running platforms, especially at the small (sub 500 g) scale. This critical gap in running legged robot design is addressed here by abstracting the motion of legged robots through a torque driven spring-loaded inverted pendulum (TD-SLIP) model, and deriving constraints that result in stable cyclic forward locomotion in the presence of system noise. Synthetic noise is added to the initial state in candidate design evaluation to simulate accumulated errors in an open-loop control. The design space was defined in terms of morphological parameters, such as the leg properties and system mass, actuator selection, and an open loop voltage profile. These attributes were optimized with a well-known particle swarm optimization solver that can handle mixed-discrete variables. Two separate case studies minimized the difference in touchdown angle from stride to stride and the actuation energy, respectively. Both cases resulted in legged robot designs with relatively repeatable and stable dynamics, while presenting distinct geometry and controller profile choices. Copyright © 2024 by ASME.

关键词： Inverted pendulum

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Image Generation of Ultra-Thin Polymer Films Using Diffusion Models from Tensile Testing for mechanical Failure Prediction

Image Generation of Ultra-Thin Polymer Films Using Diffusion...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Suh, Sungho Lee, Haebom Kang, Tae Yeob RPTU Kaiserslautern Department of Computer Science Landau Germany Kaiserslautern Germany Heidelberg University Department of Computer Science Heidelberg Germany AIMMO Seoul Korea Republic of University of Suwon Department of Mechanical Engineering Hwaseong Korea Republic of

ISBN: (纸本)9798350371888

The mechanical reliability of ultra-thin polymer films, crucial in microelectronic applications, poses significant challenges. Polymethyl methacrylate (PMMA) is widely utilized in this context, yet conventional methods assessing mechanical reliability are limited in scope, often failing to predict failure accurately. Furthermore, these methods neglect the complex nanoscale physics of polymer films, which may lead to the formation of wrinkles during plastic deformation. In this paper, we present a novel approach that combines diffusion models with measured tensile properties to generate sequential PMMA images, enabling the prediction of mechanical failure in ultra-thin films. The proposed diffusion model not only accurately generates PMMA images from tensile testing but also simulates wrinkle formation, providing a more comprehensive and accurate assessment of PMMA thin film integrity. This research offers a promising avenue for enhancing the mechanical reliability of microelectronic devices. © 2024 IEEE.

关键词： Polymer films

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Predictive modeling for concrete properties under variable curing conditions using advanced machine learning approaches

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Asian Journal of Civil engineering 2024年第8期25卷 6249-6265页

作者： Mungle, Nischal P. Mate, Dnyaneshwar M. Mankar, Sham H. Tale, Vithoba T. Mehta, Ankita Dhale, Shrikrishna A. Vairagade, Vikrant S. Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Mechanical Engineering JSPM’s Rajarshi Shahu College of Engineering Maharashtra Pune 440033 India Department of Mechanical Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune 412101 India Department of Computer Science and Application Ramdeobaba University Maharashtra Nagpur 440013 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India

Such is the need for this work, since accurate concrete strength prediction at different curing conditions is critical to having structures that are both strong and long-lasting. Traditional methods for the prediction of concrete strength often lack the complexity that occurs in the interaction of the different environmental factors involved, hence leading to suboptimal practices in curing with potential structural weaknesses. Current research into this area has typically focused on disparate data sources and rather naïve modeling methods, further limiting predictive accuracy and creating a general lack of comprehensive knowledge of curing dynamics. Such limitations bring out the need for a more integrated, sophisticated predictive modeling approach to explain variability in concrete strength levels. This paper proposes a novel predictive modeling framework that will be powered by advanced machine learning techniques to take up these challenges. It will adopt a multimodal data integration approach driven by a combination of sensor data related to temperature, humidity, and strain gauges;environmental data related to weather conditions and atmospheric pressure;and historical records, such as mix design and curing duration, further leveraging techniques from data fusion, including the Kalman filter and Bayesian networks. This will be further integrated into a unified, enriched dataset, encapsulating the complex interaction of factors influencing concrete strength. In the present work, this is a chosen approach: hybrid modeling with ensemble learning using XGBoost for the prediction of static features, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. In this case, a combination of these models via weighted averaging or stacking improves the accuracy of the predictions to a very great extent: the R² increased from 0.85 to 0.92, and MAE levels by 10–15%. In addition to that, AutoML with Feature Tools implements advanced feature engineering

关键词： Concrete strength Data fusion Ensemble learning Machine learning Predictive modeling

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Exploratory Testing of Software Product Linesusing Distance Metrics

Exploratory Testing of Software Product Linesusing Distance ...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Saini, Ashish Kumar, Satendra Kumar, Raj Kumar, Mukesh Kumari, Amrita Quantum University Dept. of Computer Science and Engineering Roorkee India Moradabad Institute of Technology Dept. of Computer Science and Engineering Moradabad India Gurukula Kangri Deemed to Be University Dept. of Computer Science Haridwar India Quantum University Dept. of Mechanical Engineering Roorkee India

ISBN: (纸本)9798350323917

Quality is very important in any software product, so that the customer trusts the efficiency of the product. The quality or validity of a product is measured by its testing. The test measures the quality of a product as well as shows the validity of the product. For example, testing and measuring the validity of a product in a single software system is not easy. Similarly, the software product line, which produces many different products of the same family at once, makes it even more difficult to check their validity and quality. Several techniques and models have been proposed to test the validity of the software line products and to ascertain the quality. In the same direction, we also propose a method for testing the products of software product line. The proposed method isbased on the distance between the features of the product (Desired and Original). We have used the proposed technique on product line with less features and found that the method presented gives fast and better results than traditional methods. © 2023 IEEE.

关键词： Software testing

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Fault Diagnosis of Indium Tin Oxide Electrodes by Multi-channel S-parameter Patterns

Fault Diagnosis of Indium Tin Oxide Electrodes by Multi-chan...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Suh, Sungho Lee, Haebom Kang, Tae Yeob RPTU Kaiserslautern-Landau Department of Computer Science Germany Kaiserslautern Germany Heidelberg University Department of Computer Science Heidelberg Germany AIMMO Seoul Korea Republic of University of Suwon Department of Mechanical Engineering Hwaseong Korea Republic of

ISBN: (纸本)9798350371888

Indium tin oxide (ITO) electrodes are integral components in a wide array of applications, from displays and sensors to solar cells. Ensuring the optimal performance and reliability of these devices necessitates effective fault diagnosis of ITO electrodes. Traditional visual inspection is challenging due to their transparency, and existing fault diagnosis methods often necessitate destructive evaluations and secondary material characterization techniques, limiting their root cause determination capabilities. In this study, we introduce a fault diagnosis approach utilizing scattering parameter (S-parameter) patterns, which provides early detection, remarkable diagnostic accuracy, and noise robustness. The proposed method incorporates Convolutional Neural Networks (CNNs) for the concurrent analysis of defect causes and severity. Experimental results demonstrate that combining different S-parameter channels significantly enhances diagnostic performance under additive noise conditions. This work not only provides an advanced fault diagnosis method for ITO electrodes but also demonstrates the importance of combining S-parameter channels to enhance fault detection. © 2024 IEEE.

关键词： Transparent electrodes

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Systematic Elastostatic Stiffness Model of Over-Constrained Parallel Manipulators Without Additional Constraint Equations

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Chinese Journal of mechanical engineering 2024年第4期37卷 258-276页

作者： Chao Yang Wenyong Yu Wei Ye Qiaohong Chen Fengli Huang College of Mechanical and Electrical Engineering Jiaxing UniversityJiaxing 314001ZhejiangChina Zhejiang Siekon Transmission Technology Co. Ltd.Jiaxing 314001ZhejiangChina National and Local Joint Engineering Research Center of Reliability Analysis and Testing for Mechanical and Electrical Products Zhejiang Sci-Tech UniversityHangzhou 310018ZhejiangChina School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018ZhejiangChina

The establishment of an elastostatic stiffness model for over constrained parallel manipulators(PMs),particularly those with over constrained subclosed loops,poses a challenge while ensuring numerical *** study addresses this issue by proposing a systematic elastostatic stiffness model based on matrix structural analysis(MSA)and independent displacement coordinates(IDCs)extraction *** begin,the closed-loop PM is transformed into an open-loop PM by eliminating constraints.A subassembly element is then introduced,which considers the flexibility of both rods and *** approach helps circumvent the numerical instability typically encountered with traditional constraint *** IDCs and analytical constraint equations of nodes constrained by various joints are summarized in the appendix,utilizing multipoint constraint theory and singularity analysis,all unified within a single coordinate ***,the open-loop mechanism is efficiently closed by referencing the constraint equations presented in the appendix,alongside its elastostatic *** proposed method proves to be both modeling and computationally efficient due to the comprehensive summary of the constraint equations in the Appendix,eliminating the need for additional *** example utilizing an over constrained subclosed loops demonstrate the application of the proposed *** conclusion,the model proposed in this study enriches the theory of elastostatic stiffness modeling of PMs and provides an effective solution for stiffness modeling challenges they present.

关键词： Parallel manipulator Elastostatic stiffness model Matrix structural analysis Subassembly element Independent displacement coordinates

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A Framework for Identifying the Learners' Engagement in E-learning Environments Using a Hybrid CNN Architecture 6

A Framework for Identifying the Learners' Engagement in E-le...

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6th International Conference on Computing, Electronics and Communications engineering, iCCECE 2023

作者： Anny, Tasfia Nuzhat Chowdhury, Jafrin Iqbal Ahsan, Tanveer Zahur, Istiaque Patwary, Muhammed J. A. Miraz, Mahdi H. International Islamic University Computer Science and Engineering Chittagong Bangladesh Sonargaon University Mechanical Engineering Bangladesh Xiamen University Malaysia School of Computing and Data Science Malaysia

ISBN: (纸本)9798350340921

In the realm of online education, precise identification of the participants and their engagement is critical for optimal learning results. With the aim of precisely monitoring attendance and engagement in an online learning environment, this study suggests an end-To-end framework based on a hybrid convolutional neural network (CNN) architecture. The complete evaluation employs two separate CNN models: CNN-I for identifying learners' faces in individual images and CNN-II for measuring engagement through facial expressions, eye gaze and head movement. Two custom datasets have been developed and annotated to allow for an in-depth assessment of the proposed approach. Dataset-I contains 4000 individual face images, whereas Dataset-II has 6720 images depicting various interactions of the learners, such as engaged, bored, frustrated, confused, drowsy and looking away. However, on Dataset-I and the VGG-face dataset, the CNN-I model surpasses a deep CNN (DCNN) with superior accuracy rates of 95.25% and 88.60%, respectively. On Dataset-II and the FER2013 dataset, the CNN-II model obtains similarly outstanding accuracy rates of 93.45% and 83.74%, respectively. The specialised CNN-I and CNN-II models outperform the hybrid CNN model in their particular tasks, albeit having an accuracy rate of 88%. The research highlights the enormous potential of straightforward architecture integrated with specific datasets, emphasising their computational effectiveness and efficiency in the domains of engagement and facial recognition for online learning. © 2023 IEEE.

关键词： Face recognition

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Prediction analysis for Parkinson disease using multiple feature selection & classification methods

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Multimedia Tools and Applications 2023年第27期82卷 42995-43012页

作者： Hema, M.S. Maheshprabhu, R. Reddy, K. Sudheer Guptha, M. Nageswara Pandimurugan, V. Computer Science and engineering Sri Venkateshwara college of engineering Bengaluru India Mechanical Engineering Aurora Scientific and Technological Institute Hyderabad India Information Technology Anurag University Hyderabad India School of Computing Networking and Communications SRMIST Kattankulathur Chennai India

Diagnosis of Parkinson's disease (PD) is a difficult undertaking that requires a variety of testing and clinical trials. Despite all these testing and trials, there is a considerable risk of misdiagnosis of Parkinson's disease, which causes a delay in making decisions about the best treatment for the patients. computer aided diagnosis can improve the experiences and helps the doctors to predict PD patients. Four sorts of classification algorithms with various types of feature selection approaches are proposed in this paper for successful PD diagnosis. Support Vector Machine-SVM, Nave Bayes-NB, K-Nearest Neighbor-KNN, and Random Forest-RF are the categorization techniques employed. All the dataset's features are not necessary for classification. To choose the relevant characteristics, four feature selection approaches are used. Least Absolute Shrinkage and Selection Operator (LASSO), Backward-forward, rough set, and tree-based feature selection techniques are used. Experimentation is carried out using the Parkinson's Progressive Markers Initiative (PPMI) dataset. Four classification algorithms are constructed, together with and without feature selection procedures, and comparative research is conducted. To measure the performance of the classifiers, various evaluation methodologies are used. Overall Random Forest classifier with four feature selection methods’ gives best results with average accuracy is 96% and rough set feature selection for all three classifiers’ average accuracy is 97%. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Classification (of information)

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