Envy Prediction from Users’ Photos using Convolutional Neural Networks

Raiaan, Mohaimenul Azam Khan; Mamun, Abdullah Al; Islam, Md. Adnanul; Ali, Mohammad; Mukta, Md. Saddam Hossain

doi:10.1109/iccece51049.2023.10085092

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…LLMs are harnessed for content generation [161], which encompasses automated article writing, social media posts [162], product descriptions, and more. This automation simplifies content creation processes and allows for scalable production of top-tier content.…”

Section: Empowering Content Generationmentioning

confidence: 99%

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Raiaan,

Mukta,

Fatema

et al. 2023

Preprint

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<p>Large Language Models (LLMs) recently demonstrated extraordinary capability, including natural language processing (NLP), language translation, text generation, question answering, etc. Moreover, LLMs are a new and essential part of computerized language processing, having the ability to understand complex verbal patterns and generate coherent and appropriate replies for the situation. Though this success of LLMs has prompted a substantial increase in research contributions, rapid growth has made it difficult to understand the overall impact of these improvements. Since a lot of new research on LLMs is coming out quickly, it is getting tough to get an overview of all of them in a short note. Consequently, the research community would benefit from a short but thorough review of the recent changes in this area. This article thoroughly overviews LLMs, including their history, architectures, transformers, resources, training methods, applications, impacts, challenges, etc. This paper begins by discussing the fundamental concepts of LLMs with its traditional pipeline of the LLM training phase. It then provides an overview of the existing works, the history of LLMs, their evolution over time, the architecture of transformers in LLMs, the different resources of LLMs, and the different training methods that have been used to train them. It also demonstrated the datasets utilized in the studies. After that, the paper discusses the wide range of applications of LLMs, including biomedical and healthcare, education, social, business, and agriculture. It also illustrates how LLMs create an impact on society and shape the future of AI and how they can be used to solve real-world problems. Then it also explores open issues and challenges to deploying LLMs in real-world aspects, including ethical issues, model biases, computing resources, interoperability, contextual constraints, privacy, security, etc. It also discusses methods to improve the robustness and controllability of LLMs. Finally, the study analyses the future of LLM research and issues that need to be overcome to make LLMs more impactful and reliable. However, this review paper aims to help practitioners, researchers, and experts thoroughly understand the evolution of LLMs, pre-trained architectures, applications, challenges, and future goals. Furthermore, it serves as a valuable reference for future development and application of LLM in numerous practical domains.</p>

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Section: Empowering Content Generationmentioning

confidence: 99%

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

Raiaan,

Mukta,

Fatema

et al. 2023

Preprint

View full text Add to dashboard Cite

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An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images

Azam,

Montaha,

Raiaan

et al. 2024

J Digit Imaging. Inform. med.

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Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection

Fahad,

Azam,

Montaha

et al. 2024

Multimed Tools Appl

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Cervical cancer is a prevalent disease affecting the cervix cells in women and is one of the leading causes of mortality for women globally. The Pap smear test determines the risk of cervical cancer by detecting abnormal cervix cells. Early detection and diagnosis of this cancer can effectively increase the patient’s survival rate. The advent of artificial intelligence facilitates the development of automated computer-assisted cervical cancer diagnostic systems, which are widely used to enhance cancer screening. This study emphasizes the segmentation and classification of various cervical cancer cell types. An intuitive but effective segmentation technique is used to segment the nucleus and cytoplasm from histopathological cell images. Additionally, handcrafted features include different properties of the cells generated from the distinct cervical cytoplasm and nucleus area. Two feature rankings techniques are conducted to evaluate this study’s significant feature set. Feature analysis identifies the critical pathological properties of cervical cells and then divides them into 30, 40, and 50 sets of diagnostic features. Furthermore, a graph dataset is constructed using the strongest correlated features, prioritizes the relationship between the features, and a robust graph convolution network (GCN) is introduced to efficiently predict the cervical cell types. The proposed model obtains a sublime accuracy of 99.11% for the 40-feature set of the SipakMed dataset. This study outperforms the existing study, performing both segmentation and classification simultaneously, conducting an in-depth feature analysis, attaining maximum accuracy efficiently, and ensuring the interpretability of the proposed model. To validate the model’s outcome, we tested it on the Herlev dataset and highlighted its robustness by attaining an accuracy of 98.18%. The results of this proposed methodology demonstrate the dependability of this study effectively, detecting cervical cancer in its early stages and upholding the significance of the lives of women.

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Envy Prediction from Users’ Photos using Convolutional Neural Networks

Cited by 6 publications

References 34 publications

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images

Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection

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