Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review

Yang, Yizhuo; Xu, Fang; Chen, Jisen; Tao, Chunxu; Li, Yunxin; Chen, Quansheng; Tang, Sheng; Lee, Hian Kee; Shen, Wei

doi:10.1016/j.bios.2023.115233

Cited by 24 publications

(7 citation statements)

References 120 publications

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“…Different selection mechanisms may be used for that purpose, including but not limited to proportional selection (roulette wheel, stochastic universal sampling), tournament selection, rank-based selection and truncation selection (elitism). ( 5) Operators are applied to the existing sets to create new candidates that will replace those rejected during selection step (4). Depending on the specific metaheuristics used they may be velocity and position update in particle swarms, crossover or mutation in genetic algorithms, etc.…”

Section: Forward Design Using Metaheuristicsmentioning

confidence: 99%

“…Nowadays, there is probably no literate person who has not at least heard about artificial intelligence (AI) [1][2][3]. With the number of its practical applications steadily rising, many people have already been in contact with at least some form of AI-be it in a modern smartphone with built-in AI chips [4,5], the Internet [6] and Internet of Things [7], public services [8], or even domestic appliances in smart homes [9]. Generative AI already has natural conversations with people [10] and generates art (paints and draws pictures or generates them in a photorealistic 3D manner, composes music, writes fiction, creates animations or concept art, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Jakšić

2024

Photonics

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The interplay between two paradigms, artificial intelligence (AI) and optical metasurfaces, nowadays appears obvious and unavoidable. AI is permeating literally all facets of human activity, from science and arts to everyday life. On the other hand, optical metasurfaces offer diverse and sophisticated multifunctionalities, many of which appeared impossible only a short time ago. The use of AI for optimization is a general approach that has become ubiquitous. However, here we are witnessing a two-way process—AI is improving metasurfaces but some metasurfaces are also improving AI. AI helps design, analyze and utilize metasurfaces, while metasurfaces ensure the creation of all-optical AI chips. This ensures positive feedback where each of the two enhances the other one: this may well be a revolution in the making. A vast number of publications already cover either the first or the second direction; only a modest number includes both. This is an attempt to make a reader-friendly critical overview of this emerging synergy. It first succinctly reviews the research trends, stressing the most recent findings. Then, it considers possible future developments and challenges. The author hopes that this broad interdisciplinary overview will be useful both to dedicated experts and a general scholarly audience.

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Section: Forward Design Using Metaheuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Jakšić

2024

Photonics

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“…Researchers should allocate sufficient resources and efforts to train the models using diverse data sets, considering factors such as model architecture, hyperparameter tuning, and regularization techniques. Furthermore, validation using independent data sets and rigorous evaluation metrics provides confidence in the model’s generalizability and performance …”

Section: Maximizing the Potential Of Ai: A Practical Approach To Sens...mentioning

confidence: 99%

“…Furthermore, validation using independent data sets and rigorous evaluation metrics provides confidence in the model's generalizability and performance. 12 By following these actionable recommendations, researchers can overcome potential challenges and seamlessly integrate AI into their sensor research workflow. However, it is important to note that AI should be viewed as a tool to enhance human capabilities rather than replace them entirely.…”

Section: ■ Maximizing the Potential Of Ai: A Practical Approach To Se...mentioning

confidence: 99%

AI in Sensor Research: A Reality Check and the Underestimated Potential of ChatGPT

Ray

2023

ACS Sens.

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“…In our modern intelligent era, mobile phones play a key role in everyday life, integrating functions such as communication, entertainment, education, and work [1][2][3]. Studies have indicated that the number of Chinese netizens is growing, with 99.8% of them using mobile phones to access the Internet [4].…”

Section: Introductionmentioning

confidence: 99%

The Relationships between Effortful Control, Mind Wandering, and Mobile Phone Addiction Based on Network Analysis

Qiu,

Guo,

Wang

et al. 2024

Healthcare

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Background: The prevailing mobile phone use brought the problem of addiction, which might cause negative consequences. Effortful control and mind wandering were associated with addictive behavior. The present study aimed to investigate the dimension-level relationships between effortful control, mind wandering, and mobile phone addiction. Methods: A total of 1684 participants participated this study. The mobile phone addiction, effortful control, and mind wandering were measured through self-report scales, respectively. Dimension-level network of these psychological variables was estimated and bridge expected influence (BEI) values for each node was calculated. Results: Dimensions of mobile phone addiction, effortful control, and mind wandering exhibited distinct and complex links to each other. The node “activation control” exhibited the highest negative BEI value (BEI = −0.32), whereas “spontaneous thinking” showed the highest positive BEI value (BEI = 0.20). Conclusions: Different dimensions of effortful control and mind wandering had varied yet significant connections with distinct dimensions of mobile phone addiction, facilitating understanding of the specific pathways underlying the three constructs. The identified dominant bridge nodes can provide potential targets for the intervention of mobile phone addiction.

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Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review

Cited by 24 publications

References 120 publications

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

AI in Sensor Research: A Reality Check and the Underestimated Potential of ChatGPT

The Relationships between Effortful Control, Mind Wandering, and Mobile Phone Addiction Based on Network Analysis

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