Published: July 10, 2024
Language: Английский
Advanced Engineering Materials, Journal Year: 2024, Volume and Issue: 26(20)
Published: June 21, 2024
The traditional trial‐and‐error testing to develop high‐performance chemiresistive gas sensors is inefficient and fails meet the high demand for in various industries. Machine learning (ML) can address limitations of be effectively utilized enhancing, developing, designing sensors. This review first discusses prediction critical mechanism parameters gas‐sensitive materials by ML, including adsorption energy, bandgap, thermal conductivity, dielectric constant. Second, it proposes that ML improve five performance indexes: selectivity, response/recovery time, stability, sensitivity, accuracy. also facilitates development structural design new materials. In addition, potential optimize sensor arrays investigated, reducing number sensors, identifying best array combination, improving recognition detection capabilities. Finally, this article challenges machine‐learning assisted practical applications envisions their future development.
Language: Английский
Citations
5
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 71442 - 71452
Published: Jan. 1, 2024
Electronic Nose (E-Nose) systems, widely applied across diverse fields, have revolutionized quality control, disease diagnostics, and environmental management through their odor detection analysis capabilities. The decision of E-Nose systems often enabled by Machine Learning (ML) models that are trained offline using existing datasets. However, despite potential, training efforts prove intensive may still fall short in achieving high generalization ability specialization for considered application. To address these challenges, this paper introduces the e-rTPNN system, which leverages Recurrent Trend Predictive Neural Network (rTPNN) combined with online transfer learning. recurrent architecture system effectively captures temporal dependencies hidden sequential patterns within sensor data, enabling accurate estimation trends levels. Notably, demonstrates to adapt quickly new data during operation, requiring only a small dataset initial We evaluate performance two domains: beverage assessment medical diagnosis, publicly available wine Chronic Obstructive Pulmonary Disease (COPD) datasets, respectively. Our evaluation indicates proposed achieves accuracy exceeding 97% while maintaining low execution times. Furthermore, comparative against established reveals consistently outperforms significant margin terms accuracy.
Language: Английский
Citations
2
International journal of intelligent engineering and systems, Journal Year: 2024, Volume and Issue: 17(2), P. 700 - 714
Published: Feb. 28, 2024
Exhaled breath analysis comprises chemical compounds that can be utilized for diagnostic purposes, including asthma detection.An electronic nose offered as a means of monitoring patient circumstances.A significant problem often occurs when determining the appropriate number gas sensors while maintaining high accuracy.The firefly algorithm (FA) is very effective because its exploratory capabilities, presents theories are easy to understand and has relatively fewer parameters.This study aims reduce determine an in differentiating healthy asthmatic subjects using FA exhaled analysis.The experimental results indicate provides only four still maintain performance.The convolutional neural network model was favored ability classify entire dataset, making it best machine learning nose, with accuracy 97.8%.
Language: Английский
Citations
1
International Journal of Innovative Science and Research Technology (IJISRT), Journal Year: 2024, Volume and Issue: unknown, P. 2059 - 2063
Published: June 7, 2024
This study delves deeper into the realm of electronic devices and technologies for detection COVID-19, tuberculosis (TB), asthma, examining recent advancements future prospects. Electronics, with their versatility precision, have emerged as a critical tool in combating infectious diseases chronic conditions. Through comprehensive review, this paper explores diverse range used methods these diseases, including sensors, imaging systems, wearable devices, data analytics platforms. Moreover, it discusses integration emerging technologies, such artificial intelligence, machine learning, Internet Things (IoT) to enhance capabilities disease monitoring.
Language: Английский
Citations
1
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 97235 - 97247
Published: Jan. 1, 2024
Currently, the baking of cakes using an electric oven is based on cooking duration. Usually, colors can be used to determine levels food. However, many have similar at each stage, which cannot as indicators doneness. Through today's technology, sense smell imitated a gas sensor combined with artificial intelligence for food quality control. In this study, electronic nose system was developed distinguish cookies. This process involved 20 sensors and 10 classification algorithms aroma. The optimization technique correlation analysis distinguishing rate methods carried out obtain small number that still maintained high accuracy values. Several were eliminated, while remaining 13 retained. selected consisted 6 convolutional neural networks. It succeeded in levels, including undercooked, cooked, overcooked food, 90.0%, precision 89.7%, recall 92.6%, F1-measure 90.2%. has potential produce consistent
Language: Английский
Citations
1
Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 496, P. 154225 - 154225
Published: July 22, 2024
Language: Английский
Citations
1
Biosensors, Journal Year: 2024, Volume and Issue: 14(10), P. 502 - 502
Published: Oct. 14, 2024
Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting importance of monitoring evaluating freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, sensory evaluations, were conducted assess status oysters. Real-time measurements taken at various storage temperatures (4 °C, 12 20 28 °C) thoroughly investigate changes under different conditions. Principal component analysis utilized reduce 10-dimensional vectors 3-dimensional vectors, enabling clustering samples into fresh, sub-fresh, decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input solicited performance optimization suggestions enhanced efficiency applicability established prediction system. The results demonstrate that combining with indices is effective approach diagnosing spoilage mitigating safety risks industry.
Language: Английский
Citations
1
Indonesian Journal of Electrical Engineering and Computer Science, Journal Year: 2024, Volume and Issue: 34(1), P. 264 - 264
Published: Feb. 29, 2024
Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This identified with spirometry, but the patient feels uncomfortable, affecting diagnosis results. Other markers are being investigated, including exhaled breath. method applied easily, non-invasive, has minimal side effects, and provides accurate study applies electronic nose distinguish healthy people COPD suspects using breath samples. Twenty semiconductor gas sensors combined machine learning algorithms were employed as an system. Experimental results show frequency feature of sensor responses used principal component analysis (PCA) graph convolutional network (GCN) provide highest accuracy value 97.5% in distinguishing between subjects. improve detection performance systems, which help diagnose COPD.
Language: Английский
Citations
0
Published: Feb. 21, 2024
Beef
is
one
of
the
foods
most
consumed
by
humans.
However,
rotten
beef
often
found
in
markets.
This
indicates
omission
beef,
which
still
stored
warehouse.
Rotten
can
release
metabolic
products
such
as
ammonia
(NH
Language: Английский
Citations
0
Published: July 17, 2024
Language: Английский
Citations
0