Cyanobacteria blue-green algae prediction enhancement using hybrid machine learning–based gamma test variable selection and empirical wavelet transform

The olfactory system can detect and recognize tens of thousands of volatile organic compounds (VOCs) at low concentrations in complex environments. Bioelectronic nose (B‐EN), which mimics olfactory systems, is becoming an emerging sensing technology for identifying VOCs with sensitivity and specificity. B‐ENs integrate electronic sensors with bioreceptors and pattern recognition technologies to enable medical diagnosis, public security, environmental monitoring, and food safety. However, there is currently no commercially available B‐EN on the market. Apart from the high selectivity and sensitivity necessary for volatile organic compound analysis, commercial B‐ENs must overcome issues impacting sensor operation and other problems associated with odor localization. The emergence of nanotechnology has provided a novel research concept for addressing these problems. In this work, the structure and operational mechanisms of biomimetic olfactory systems are discussed, with an emphasis on the development and immobilization of materials. Various biosensor applications and current developments are reviewed. Challenges and opportunities for fulfilling the potential of artificial olfactory biohybrid systems in fundamental and practical research are investigated in greater depth.

Artificial Olfactory Biohybrid System: An Evolving Sense of Smell

Zinc selenide (ZnSe) semiconductor belongs to a class of wide energy gap material with low visible region optical absorption, high optical coefficient (nonlinear) and fascinating optoelectronic as well as photocatalytic features which foster and strengthen its applications in resisting thermal shock, photoelectric fields (which include dielectric mirrors, shot wavelength lasers and light emitting diode), buffer layer of solar cell and many photocatalytic environmental cleanliness applications. Nanostructured ZnSe semiconductors occupy a special place in application domain due to the characteristic features attached to the crystallite size of the material which include high fluorescence quantum yields, solvent dispersibility and adjustable emission color among others. Effective utilization of nanostructured ZnSe semiconductor involves tuning of its energy gap through doping mechanisms which is accompanied with distortion in the parent lattice structure. In this contribution, extreme learning machine (ELM) computational intelligent method is proposed for predicting the band gap energy of doped ZnSe nanostructured semiconductor using the material crystallite size and lattice parameter as descriptors to the models. The developed ELM based model using sine (Sine), sigmoid (Sig) and transig (Trang) activation functions were compared with the existing SVR-GA and SPR models in the literature using various performance metrics. The developed Sine-ELM model shows superior performance over Sig-ELM, Trang-ELM, SVR-GA (2021) and SPR (2021) models with performance improvement of 12.74%, 64.89%, 68.87% and 75.62% using mean squared error (MSE) metric for testing data samples. The superiority of the model developed was further justified through validation with external set of data. The precision and superiority of the ELM based models developed would eventually ease quick characterization and tuning of energy gap of ZnSe nanostructured semiconductors for various applications. 

Extreme learning machine computational method of modeling energy gap of doped zinc selenide nano-material semiconductor

The problem of drift in the electronic nose (E-nose) is an important factor in the distortion of data. The existing active learning methods do not take into account the misalignment of the data feature distribution between different domains due to drift when selecting samples. For this, we proposed a cross-domain active learning (CDAL) method based on the Hellinger distance (HD) and maximum mean difference (MMD). In this framework, we weighted the HD with the MMD as a criterion for sample selection, which can reflect as much drift information as possible with as few labeled samples as possible. Overall, the CDAL framework has the following advantages: (1) CDAL combines active learning and domain adaptation to better assess the interdomain distribution differences and the amount of information contained in the selected samples. (2) The introduction of a Gaussian kernel function mapping aligns the data distribution between domains as closely as possible. (3) The combination of active learning and domain adaptation can significantly suppress the effects of time drift caused by sensor ageing, thus improving the detection accuracy of the electronic nose system for data collected at different times. The results showed that the proposed CDAL method has a better drift compensation effect compared with several recent methodological frameworks.

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Cross-Domain Active Learning for Electronic Nose Drift Compensation

In the tea market, it is common for low-quality tea to cheat consumers by pretending to be high-quality. This work proposes a fast and nondestructive tea quality detection method, which combines an electronic nose (e-nose) system with an adaptive gas information recognition method. First, based on a PEN3 e-nose system, the gas information of different levels of tea is obtained. Second, a lightweight group convolution (LGC) module is proposed to adaptively focus on the important features that affect the classification performance of gas information and reduce the number of parameters. Finally, residual dense block (RDB) is introduced to fuse the shallow and deep features to avoid feature degradation, and residual LGC neural network (RLGCNet) is designed to recognize the different levels of tea gas information effectively. In the comparison results of the ablation study and multiclassification model, RLGCNet obtained the best classification accuracy of 98.50%, the precision of 98.49%, and the recall of 97.69%. In conclusion, the theoretical research results provide an effective detection method for the quality supervision of the tea market. 

A Residual Dense Lightweight Group Convolution Neural Network for Identifying the Gas Information of Different Levels of Tea

The drift caused by gas sensors has always been a bottleneck in the development of electronic nose (E-nose) systems. Traditional drift compensation methods directly correct the drift components, making such approaches time-consuming and laborious. In the field of E-nose drift compensation, cross-domain adaption learning is an efficient technique. In this paper, we propose a novel subspace alignment extreme learning machine (SAELM) that considers multiple criteria to construct a unified extreme learning machine (ELM)-based feature representation space and thus achieve domain alignment. First, the method minimizes both the geometric and statistical distributions between different domains. Second, the dependence between features and labels is enhanced using the Hilbert–Schmidt independence criterion (HSIC) to alleviate the blurring of the correspondence between the two caused by drift. Third, to improve the feature extraction ability of the subspace learning method, the l 2,1 norm is leveraged to constrain the output weights of the ELM. The aim of this method is to learn a robust subspace to increase the consistency between domains and enhance the feature–label​ dependency of the source domain while preserving the intrinsic information of both domains. Extensive experiments on sensor drift data are conducted, and the proposed SAELM method yields the greatest improvements on E-nose drift datasets.

Tao Liu

Papers

Subspace alignment based on an extreme learning machine for electronic nose drift compensation