223 research outputs found
AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning
No full textIn 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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کرنل فضلِ اکبر کمالؔ اور میجر نورالحسن رضوی کے شعری مجموعوں کا جائزہ
No full textThis article has presented the services of Army's two great poets. They continued military service as well as Urdu. Colonel Fazl-e-Akbar Kamal is the author of "Hareem o Hijab". Their poetry has fascinating trend and reality factor. And the rays of hope are obvious. His poetry covers various writings of Urdu literature. Maj. Syed Noor-ul-Hassan is the author of "Aks e Noor". In this book, he has tested many principles of Urdu literature. Your poetry has been tested on love virtual and glimpse of dream. you think Western Civilization greatly damaged Islamic civilization and culture. your poetry can be included in Urdu literature
Approximation of the Solution of Split Equality Fixed Point Problem for Family of Multivalued Demicontractive Operators with Application
No full textIn this paper, a new viscosity type iterative algorithm is used for obtaining a strong convergence result of split equality fixed point solutions for infinite families of multivalued demicontractive mappings in real Hilbert spaces. Our iterative scheme is based on choosing the step-sizes without calculating or estimating the operator norms and the condition of hemicompactness was relaxed to prove the strong convergence result. As an application, the solution of split convex minimization problem was approximated. The result presented herein unifies and extends several comparable results in the literature
Convergence of AA-Iterative Algorithm for Generalized α-Nonexpansive Mappings with an Application
No full textThe aim of this paper is to approximate the fixed points of generalized α-nonexpansive mappings using AA-iterative algorithm. We establish some weak and strong convergence results for generalized α-nonexpansive mappings in uniformly convex Banach spaces. A numerical example is also given to show that the AA-iterative algorithm converges faster than some others algorithms for generalized α-nonexpansive mappings. Lastly, using the AA-iterative algorithm, we approximate the weak solution of delay composite functional differential equation of the Volterra–Stieltjes type
Feasibility analysis for incorporating/deploying SIEM for forensics evidence collection in cloud environment
No full textDecision-making analysis of minimizing the death rate due to covid-19 by using q-rung orthopair fuzzy soft bonferroni mean operator
No full textThe q-Rung Orthopair Fuzzy Soft Set (q-ROFSS) theory is a significant extension of Pythagorean fuzzy soft set and intuitionistic fuzzy soft set theories for dealing with the imprecision and uncertainty in data. The purpose of this study is to improve and apply this theory in decision-making. To achieve this purpose, we firstly propose some Bonferroni Mean (BM) and Weighted Bonferroni Mean (WBM) aggregation operators for aggregating the data. Some desired properties are presented in detail and the existing aggregation operators are used as distinct cases of our proposed operators. Further, a decision-making analysis is presented based on our proposed operations and applied to decision-making in COVID-19 diagnosis. The preferred way is discussed to protect maximum human lives from COVID-19. A numerical example is given to support the claim. The experimental results demonstrate the proposed operators have an ability to make a precise decision with imprecision and uncertain information which will find a broad application in the decision-making area
Examining the Impact of Financial Inclusion, Tourism and Renewable Energy on Environmental Sustainability: Evidence from NARDL Approach
No full textThis paper aims at examining the effects of financial inclusion, renewable energy, tourism and economic growth on the environmental stability in Pakistan. It uses the Nonlinear Autoregressive Distributed Lag (NARDL) and Environmental Kuznets Curve (EKC) hypothesis. The goal of the study to uncover the non-linear relationship and long-run co-movement between such variables which spans through 2000-2023. Financial inclusion emerges as a critical factor, consistently reducing emissions in both the short and long run, with negative shocks having a more pronounced effect. The findings highlight the EKC pattern, where economic growth initially increases emissions, but beyond a certain point, further growth reduces them, signaling a transition toward environmental improvement. Renewable energy consumption plays a crucial role in lowering emissions, while tourism slightly increases them. The findings of this study can be useful for policy makers as this study also emphasized the fact that for the sustainability in Pakistan there is need to encourage financial commensurate with the need for green economy through sustainable tourism and renewable energy for the improved environmental sustainability
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