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A Fuzzy Inference System for Sustainable Outfit Recommendations in the Fashion Industry

A Fuzzy Inference System for Sustainable Outfit Recommendati...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Raunak Raj Veerpal Kaur Kewal Krishan Aadrita Nandy Jyoti Choudhary Ajay Kumar School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In 1826, Charles Frederick Worth, widely recognized as the first fashion designer, had no idea of the significant growth, financial success, and widespread recognition the fashion industry would achieve. the vast amounts of data produced by the fashion industry today can be analyzed using Big data techniques to help with pattern recognition. Given the substantial volume of data available, the potential for utilizing Artificial Intelligence within this industry is vast. However, what if someone wants to purchase an outfit that only exists in their imagination? To address this, a recommendation system must prioritize delivering sustainable fashion recommendation outcomes aligned with the Sustainable Development Goals. Most existing Artificial Intelligence (AI) and machine learning model-based recommendation systems are based on the time-consuming task of analyzing vast quantities of previously collected user data. the problem can be addressed using a Fuzzy Inference System technique. By employing this technique, the goal of sustainable fashion will be achieved, along with the fact that the system can provide outfit recommendations by engaging in intelligent inquiry and posing relevant and well-informed queries before searching. this approach not only fosters innovation but also promotes quality of life and emphasizes environmental sustainability, sustainable production, and resource efficiency, reducing consumer levels of waste. Integrating recycling and sustainable practices into the fashion industry contributes to energy efficiency and sustainable management. the advancement of the recommendation system in terms of technology transfer will be a crucial focus for future research and development.

关键词： Industries Fuzzy logic Technological innovation Technology transfer Production Transforms Big data

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Early Diabetes prediction with optimal feature selection using ML based Prediction Framework

Early Diabetes prediction with optimal feature selection usi...

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international conference on Signal Processing and Communication (ICSPC)

作者： G. R. Ashisha X. Anitha Mary H. Mohamed Ashif I. Karthikeyan J. Roshan Electronics and Instrumentation Engineering Karunya Institute of Technology and Sciences Coimbatore India Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India Artificial Intelligence and Data Science Karunya Institute of Technology and Sciences Coimbatore India

the notable developments in healthcare sciences and biotechnology have led to a significant increase in information technology, for instance, clinical data and high efficiency genetic information, produced from intensive Electronic Medical data. the use of data mining and machine learning techniques are essential in the biomedical sciences to distinctly transform all accessible health records into useful knowledge. Early detection of diseases like diabetes is crucial as the growing number of people with diabetes is rising rapidly. According to an international Diabetes Federation report, the number of diabetic cases globally is expected to reach 642 million by 2040. this growing disease requires a lot of attention. machine learning has quickly advanced, and many facets of medical health have benefited from its use. Due to their capacity for prediction, machine learning algorithms are presently significant in the healthcare industry. the major goal of this work is to design a model that can more accurately predict patients’ diabetes. the research work analyzed the hospital clinical examination data obtained from Sree Guru Hospital, Kanyakumari district, TamilNadu. To improve the accuracy of the system, imbalanced data in the real time dataset were eliminated using oversampling technique. Extra Trees classifier model, and Random Forest (RF) was used to predict diabetes. the result of this work shows that prediction of diabetes with the Extra Trees classifier had the highest accuracy of 98%. Finally, this work effectively determines the pervasiveness and detection of diabetes.

关键词： machine learning algorithms Hospitals Transforms Predictive models Signal processing Real-time systems Diabetes

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Knowledge Graph of Civil Aircraft Approach and Landing Flight Safety Research Based on Citespace Sustainability Analysis 4

Knowledge Graph of Civil Aircraft Approach and Landing Fligh...

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4th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Chen, Nongtian Man, Yongzheng Ning, Weifeng Civil Aviation Flight University of China College of Aeronautical Engineering Guanghan China Civil Aviation Flight University of China College of Civil Aviation Safety Engineering Guanghan China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

In order to grasp the academic development status and trend of civil aircraft approach and landing flight safety research, the web of science database was used to select the message texts of approach and landing academic papers from 1990 to 2022 as the basis, and CiteSpace information analysis and simulation software was used to analyze the approach and landing papers respectively. Production time, institutional cooperation, research content and hotspot situation are used for information data mining and clustering analysis to build a knowledge map. the research results show that the output of the paper basically conforms to the Rice logic curve, indicating that the investigation on flight approach and landing safety has become a hotspots in the pasture of aviation safety. Scholars have gradually formed a relatively concentrated journal group in the submission of academic papers on flight approach and landing research, which is embodied in journals such as Aerospace Science and Technology, Aeronautical Journal and international Journal of Aeronautical. In terms of flight approach and landing safety analysis and research, aviation safety, pilot guidance, aircraft design, and airport environment are key related content words in the field of flight approach and landing safety research. It is concluded that the field of flight approach and landing mainly focuses on pilot psychology research, ergonomics research and flight data processing ang risk measurement research, among which the variant words "air safety"and "machine learning"represent the present research hotspots and development pasture in the field of flight approach and landing flight safety. © 2022 IEEE.

关键词： data visualization

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Fire Detection System using Deep learning Techniques 4

Fire Detection System using Deep Learning Techniques

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Srividhya, S. Sindhu, S. Sornalakshmi, K. Hemavathi, D. Rajaram, V. School of Computing Srm Institute of Science and Technology Data Science and Business Systems Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

An image-based fire detection system is designed using an image dataset to train the model, and further, any image capturing device will be used for later prediction purposes. the training and prediction are done using deep learning techniques. Convolution Neural Network is used for implementation. As another fire identification innovation, picture fire location has as of late assumed a pivotal part in diminishing property misfortune and harm during a fire by disturbing clients at a beginning phase through early fire discovery. Picture fire location depends on an algorithmic investigation of pictures. Nonetheless, there is a lower precision, postponed recognition, and much calculation in like manner identification calculations, including physically and machine naturally separating picture highlights. the model is designed to alert the user via SMS and email along with a captured image of fire when a fire occurs. the proposed system in development aims at providing a detection system that can alert a user instantly at the time of a hazardous fire. the aim is to create an accurate model that does not result in false alerts. the model is trained using accurate image datasets to differentiate between a fire and a non-fire scenario. Existing models used the RGB-HIS colour space for their model. Particularly, it is used to obtain pre-detected areas rather than the traditional motion differential method. the centroid variety of candidates is used to measure the flame colour dispersions in subsequent frames. Multi-expert systems have the following;dispersion of colour and centroid movement are entrenched to classify fire based on testing various clips acquired by a surveillance camera in private confinement. © 2022 IEEE.

关键词： Fires

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Attentive Visual Semantic Specialized Network for Video Captioning 25

Attentive Visual Semantic Specialized Network for Video Capt...

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25th international conference on pattern recognition (ICPR)

作者： Perez-Martin, Jesus Bustos, Benjamin Perez, Jorge Univ Chile Dept Comp Sci Millennium Inst Fdn Res Data Santiago Chile

ISBN: (纸本)9781728188089

As an essential high-level task of video understanding topic, automatically describing a video with natural language has recently gained attention as a fundamental challenge in computer vision. Previous models for video captioning have several limitations, such as the existence of gaps in current semantic representations and the inexpressibility of the generated captions. To deal with these limitations, in this paper, we present a new architecture that we call Attentive Visual Semantic Specialized Network (AVSSN), which is an encoder-decoder model based on our Adaptive Attention Gate and Specialized LSTM layers. this architecture can selectively decide when to use visual or semantic information into the text generation process. the adaptive gate makes the decoder to automatically select the relevant information for providing a better temporal state representation than the existing decoders. Besides, the model is capable of learning to improve the expressiveness of generated captions attending to their length, using a sentence-length-related loss function. We evaluate the effectiveness of the proposed approach on the Microsoft Video Description (MSVD) and the Microsoft Research Video-to-Text (MSR-VTT) datasets, achieving state-of-the-art performance with several popular evaluation metrics: BLEU-4, METEOR, CIDEr, and ROUGE(L).

关键词： Specialized long-short term memory (S-LSTM) adaptive attention teacher forcing video captioning

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Survey on Sentiment Analysis of User Reviews 4

Survey on Sentiment Analysis of User Reviews

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4th international conference on Innovative Computing (ICIC)

作者： Naseem, Saba Mahmood, Toqeer Asif, Muhammad Rashid, Junaid Umair, Muhammad Shah, Mohsin Natl Text Univ Dept Comp Sci Faisalabad Pakistan Lahore Garrison Univ Dept Comp Sci Lahore Pakistan Air Univ Islamabad Dept Comp Sci Kamra Campus Kamra Pakistan Univ Lahore Dept Software Engn Lahore Pakistan Hazara Univ Dept Informat Technol Mansehra Pakistan

ISBN: (纸本)9781665400916

User's sentiments or opinions stated on any website or app have enormous impact on the product sellers, customers, and readers. the formless data from the sites or apps is required to be explored and analyzed. To do this, sentiment analysis has familiar major interest. Sentiment Analysis (SA) is sometimes described as archeology or opinion mining as a research field to analyze people's feelings or ideas about organizations such as topics, events, individuals, issues, services, products, organizations, and their characteristics. As the number of reviews rapidly increases, online reviews' sentiment analysis has become a trending topic in research. In this paper, survey is done which highlights the latest research about the implementation of machine learning and deep learning models like support vector machine (SVM), linear regression (LR), Naive Bayes (NB), convolutional neural networks (CNN), Long short-term memory (LSTM), deep neural networks (DNN), and many others proposed for resolving various challenges in sentiment analysis such as product and app(s) review analysis, textual and visual analysis, classification of sentiments, and cross lingual problems.

关键词： Sentiment analysis opinion mining review analysis CNN DNN SVM LR NB LSTM machine learning Deep learning

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Performance Comparison of Adaptive Neuro Fuzzy Inference System and Support Vector machine Algorithm in Balanced and Unbalanced Multiclass data Classification 4

Performance Comparison of Adaptive Neuro Fuzzy Inference Sys...

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4th IEEE international conference on Computer and Informatics Engineering (IC2IE)

作者： Saputra, Muhammad Irfan Budiman, Irwan Kartini, Dwi Nugrahadi, Dodon Turianto Faisal, Mohammad Reza Lambung Mangkurat Univ Comp Sci Banjarbaru Indonesia

ISBN: (纸本)9781665442886

data is a record collection of facts. At first the data in the real world were largely unbalanced. Although, the existence of data on fewer categories is much more important to know data on more categories. However, there are some balanced data. this balanced data is the possibility of a ratio of 1:1 in which, the data in the dataset is the same. In this study, using the ANFIS algorithm and SVM to see affected performance on balanced and imbalanced data with multiclass. data is taken from the UCI machine learning named Iris dataset and Wine dataset. there are four step taken from this research which is selection, preprocessing, data mining, and conclusion. From the research conducted using SVM and ANFIS, it is known that the SVM method on the Wine dataset has an accuracy of 96.6 percent and the ANFIS method on the Iris dataset has an accuracy of 94.7 percent.

关键词： ANFIS SVM balanced data imbalanced data multiclass

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Ensemble Classification for Human Activity recognition with Feature Reduction

Ensemble Classification for Human Activity Recognition with ...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Dinesh Kumar Vishal Bharti Department of Computer Science and Engineering Chandigarh University Mohali India

Recognizing human action is an efficient method for identifying, deciphering, and identifying human patterns and activities. the hardest part is to obtain data about human activities. the ability to recognize another person's behaviours is the primary difficulty that existed prior to the methodical disciplines of artificial intelligence and machine learning. the technique for recognizing human activity is put into action in a variety of steps, including pre-processing the data, extracting the attributes, and classifying the data. this study suggests a hybrid version for recognizing human behavior that combines KMC, Principal Component Analysis, and several algorithms that are combined using voting mechanism. Python is used to test the methodology that is presented, and several metrics like accuracy, precision, and recall are taken into account to study the outcomes.

关键词： Measurement machine learning algorithms Clustering algorithms machine learning Classification algorithms Behavioral sciences data mining

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A component search method by feature Mixing based Continual learning 23

A component search method by feature Mixing based Continual ...

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Proceedings of the 2023 4th international conference on machine learning and Computer Application

作者： Xiaoyu Fang Liu Yuan School of Computer Science Shanxi Normal University China

ISBN: (纸本)9798400709449

Component platforms are widely used in developing large web and mobile applications to improve development efficiency and test coverage. Searching for the appropriate component in the component platform and introducing it into the project for use makes development efficient and reduces redundant code. machine learning methods are now widely used in large component platforms to enhance automatic retrieval or identification. However, current machine learning models are less accurate in identifying components or icons with differences due to the difficulty of collecting a large amount of label data in online environments that can help learning. To address this problem, this paper improves the feature mixing method in active learning and embeds it into a classical continuous learning model to solve the problem of accuracy degradation when the model handles discrepant tasks and tasks with limited training data. the model proposed in this paper ensures that the accuracy and performance of the model do not degrade significantly when facing a series of tasks with less data in the dataset after training on the first few tasks. Compared with large neural network recognition tasks, the improved continuous learning model is more lightweight and can learn multiple tasks using a single model. Easy to use when combined with front-end engineering and can be added to projects through dependency libraries. Compared to various classical benchmark models in continual learning, the accuracy of the proposed model can be improved by about fifteen percent.

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Document Detection using Probabilistic Principal Component Analysis-Based Naïve Bayes Model

Document Detection using Probabilistic Principal Component A...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), international conference on

作者： T. Elavarasi R. Nagarajan Department of computer and information science Annamalai University

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Document detection is where algorithms are trained to detect and classify document types such that automating sorting and data extraction becomes more efficient in document management. Common limitations of existing methods include reliance on large volumes of labelled training data, sensitivity to differences in document formats, and problems with low-quality images. therefore, this research performs the document detection using machine learning methodology. Initially, the data is manually collected from online sources in the form of documents together with their categories. these collected data are subjected to the pre-processing using the log-linear lemmatization approach. the features are extracted from these pre-processed data using the probabilistic Principal Component Analysis (PCA) approach. Finally, the document detection is accomplished using the Naive Bayes (NB) model that produces the final outcome. Simulation findings revealed that the proposed Probabilistic PCA-NB model returns better results than the other considered conventional methods respectively. the proposed Probabilistic PCA-NB for the document detection model is 0.62%, 0.72%, 0.71%, and 1.59% better than the other considered traditional methods in terms of accuracy, precision, sensitivity, and specificity respectively.

关键词： Analytical models Accuracy Computational modeling machine learning Sensitivity and specificity Probabilistic logic Feature extraction Bayes methods data mining Principal component analysis

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