Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery

Wu, Dan; Lu, Jianhua; Zheng, Nan; Elsehrawy, Mohamed Gamal; Alfaiz, Faiz Abdulaziz; Zhao, Huajun; Alqahtani, Mohammed S.; Xu, Hongtao

doi:10.1016/j.envres.2023.117784

Environmental Research

2024

DOI: 10.1016/j.envres.2023.117784

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Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery

Dan Wu,

Jianhua Lu,

Nan Zheng

et al.

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Cited by 2 publications

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

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Machine learning (ML) and nanotechnology interfacing are exploring opportunities for cancer treatment strategies. To improve cancer therapy, this article investigates the synergistic combination of Graphene Oxide (GO)‐based devices with ML techniques. The production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the investigation. GO is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal chemicals. Then, complicated biological data are analyzed using ML algorithms, which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular characteristics. The study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using ML. Predictive modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery systems. Furthermore, tracking treatment outcomes in real time is made possible by ML algorithms, which permit adaptive modifications to therapy regimens. By optimizing medication doses and delivery settings, the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Das,

Mazumdar,

Khondakar

et al. 2024

BMEMat

View full text Add to dashboard Cite

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Aspects-based representative significance of Machine Learning algorithms & natural language processing applications in nanotechnology.

Mah

2024

EAI Endorsed Trans Int Sys Mach Lear App

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Introduction: The rapid changes in computational power of machine learning algorithms and natural language processing applications have led to multi-scale and many core designs in nanotechnology. Machine learning algorithms and natural language processing applications are easing the burden engineers have to go through to understand nanoparticles.Problem: There is still a challenge to predict and control particles of nanomaterials at nanoscale. Aspect-based climatic conditions are negatively impacting the world with huge modification on nanoparticles, nanomaterials and nanostructures.Objective: Study examines aspects of machine learning algorithms and natural language processing applications that can be used to predict and control particles, and structure of nanomaterials at nanoscale. Method and materials. The study examines significance of machine learning algorithms & applications in nanotechnology, examines aspects of machine learning algorithms & natural language processing applications applied in nanotechnology, and discusses current-future trends of nanotechnology based on learning algorithms & natural language processing applications.Results and conclusions. The findings result in the conclusion that machine learning & natural language processing application in nanotechnology is implementing an advanced microscopic revolution with the potential to metamorphose the world's industrialization and scale human existence. Machine learning algorithms have the potential to predict and classify nanomaterials and natural language processing has the potential to retrieve relevant data hidden within the classified nanomaterials which results has a huge significance in the pharmaceutical industry

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Utilizing nanotechnology and advanced machine learning for early detection of gastric cancer surgery

Cited by 2 publications

References 74 publications

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

Aspects-based representative significance of Machine Learning algorithms & natural language processing applications in nanotechnology.

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