Research evidence for reshaping global energy strategy based on trend-based approach of big data analytics in the corona era

Park, Young-Eun

doi:10.1016/j.esr.2022.100835

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…12 Era globalisasi adalah bentuk perubahan global yang melanda seluruh dunia. 13 Saat ini, dunia perkembangan telah memasuki era globalisasi, hal ini merupakan era yang menuntut perubahan secara cepat. 14 Menurut Winaryo, mengemukakan bahwa globalisasi dapat memberikan banyak peluang yang bermanfaat pada seluruh sektor khususnya bagi warga negara Indonesia, jika memang negara mampu berperan secara efektif untuk mennyikapi beberbagai problematika yang menjadi persoalan yang sedang terjadi.…”

Section: Pendahuluanunclassified

Untitled

Ariani,

Ali,

Asy'arie

2023

Riayah

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Section: Pendahuluanunclassified

Untitled

Ariani,

Ali,

Asy'arie

2023

Riayah

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“…Online retailers must ensure user-friendly search functionalities and provide high-quality results that meet customers' requirements [3]. However, clothing suppliers need help providing online marketplaces with precise, systematic, and comprehensive product descriptions [4], as it necessitates automated equipment they may not currently possess. Moreover, the online retail landscape is highly diverse, with numerous retailers offering various clothing types and subcategories, each characterized by unique attributes.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

Abbas,

Zhang,

Asim

et al. 2024

Information

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In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness.

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“…Data computing using machine learning can provide optimal performance in data processing to be meaningful and have added value for its users [15]. Today and in the future, humans need strategies to manage information and data with various media and networks [16]. Various data management approaches increase innovative value and profitability in business and other strategic areas [17].…”

Section: Introductionmentioning

confidence: 99%

Prediction of linear model on stunting prevalence with machine learning approach

Mambang

Marleny²,

Zulfadhilah³

2023

Bulletin EEI

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An increase in the number of residents should be anticipated including in the health sector, especially the problem of stunting. Stunting in children disrupts height and lack of absorption of nutrients. Information and data drive change in many areas such as health, entertainment, economics, business, and other strategic areas. The stages carried out in this study are initiating, developing linear models, and making prediction results on linear machine learning models. The results of testing with the scikit-learn linear model with a minimum variable of 19 get the best test results, namely the polynomial regression with pipeline model with mean absolute percentage error (MAPE) 0.02, root mean square error (RMSE) 3.32, and coefficient of determination (R2) 1,00. Testing with the scikit-learn linear model with a maximum variable of 48 gets the best test results, namely the polynomial regression with pipeline model with MAPE 0.00, RMSE 3.79 and R2 1.00. Testing with the scikit-learn linear model with an average variable of 32 gets the best test results, namely the polynomial regression model with MAPE 0.01, RMSE 3.32, and R2 1.00. The results of testing with the scikit-learn linear model with the minimum, maximum, and average variables get the best test results, namely the polynomial regression with pipeline model.

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Research evidence for reshaping global energy strategy based on trend-based approach of big data analytics in the corona era

Cited by 6 publications

References 45 publications

Untitled

Untitled

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

Prediction of linear model on stunting prevalence with machine learning approach

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