A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Gabor filter-based statistical features for ADHD detection

Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective intervention. The aim of this study is to develop a computer-aided approach to detecting ADHD using electroencephalogram (EEG) signals. Specifically, we explore a Gabor filter-based statistical features approach for the classification of EEG signals into ADHD and healthy control (HC). The EEG signal is processed by a bank of Gabor filters to obtain narrow-band signals. Subsequently, a set of statistical features is extracted. The computed features are then subjected to feature selection. Finally, the obtained feature vector is given to a classifier to detect ADHD and HC. Our approach achieves the highest classification accuracy of 96.4% on a publicly available dataset. Furthermore, our approach demonstrates better classification accuracy than the existing methods.

Deep learning for medicinal plant species classification and recognition: a systematic review

Knowledge of medicinal plant species is necessary to preserve medicinal plants and safeguard biodiversity. The classification and identification of these plants by botanist experts are complex and time-consuming activities. This systematic review's main objective is to systematically assess the prior research efforts on the applications and usage of deep learning approaches in classifying and recognizing medicinal plant species. Our objective was to pinpoint systematic reviews following the PRISMA guidelines related to the classification and recognition of medicinal plant species through the utilization of deep learning techniques. This review encompassed studies published between January 2018 and December 2022. Initially, we identified 1644 studies through title, keyword, and abstract screening. After applying our eligibility criteria, we selected 31 studies for a thorough and critical review. The main findings of this reviews are (1) the selected studies were carried out in 16 different countries, and India leads in paper contributions with 29%, followed by Indonesia and Sri Lanka. (2) A private dataset has been used in 67.7% of the studies subjected to image augmentation and preprocessing techniques. (3) In 96.7% of the studies, researchers have employed plant leaf organs, with 74% of them utilizing leaf shapes for the classification and recognition of medicinal plant species. (4) Transfer learning with the pre-trained model was used in 83.8% of the studies as a future extraction technique. (5) Convolutional Neural Network (CNN) is used by 64.5% of the paper as a deep learning classifier. (6) The lack of a globally available and public dataset need for medicinal plants indigenous to a specific country and the trustworthiness of the deep learning approach for the classification and recognition of medicinal plants is an observable research gap in this literature review. Therefore, further investigations and collaboration between different stakeholders are required to fulfilling the aforementioned research gaps.

Two-dimensional code enables visibly mapping herbal medicine chemome: an application in Ganoderma lucidum

BACKGROUND

Chemical profile provides the pronounced evidence for herbal medicine (HM) authentication; however, the chemome is extremely sophisticated. Fortunately, two-dimensional (2D) code, as a quick response means, is conceptually able to store abundant information, exactly fulfilling the chemical information storage demands of HMs.

METHODS

We here attempted to denote both MS[Formula: see text] and MS[Formula: see text] dataset of HM with a single 2D-code chart. Measurement of Ganoderma lucidum that is one of the most famous HMs with LC-MS/MS was employed to illustrate the "coding-decoding" workflow for the conversion amongst MS/MS dataset, 2D-code, and chemical profile, and to evaluate the applicability as well. After data acquisition, and m/z value of each deprotonated molecular signal was divided into integer and decimal portions, corresponding to x and y coordinates of 2D-plot, respectively. On the other side, m/z values of all its fragment ions were exactly assigned to serial x values sharing an identical y value being equal to the precursor ion. 2D-code was thereafter produced by plotting these defined dots at a 2D-chart. Regarding a given 2D-code map, the entire chart (x coordinate: 0-600; y coordinate: 0-600) was fragmented into two regions by the line of y=x. MS[Formula: see text] spectral signals always located below the line, whereas all fragment ions lay at the left zone. After extracting information from the edges of each square frame, m/z values of both precursor ion and fragment ions could be harvested and putatively deciphered to a compound through applying some empirical mass fragmentation rules.

RESULTS

The entire code of Ganoderma lucidum fruit bodies therefore corresponded exactly to a compound set. The elution program, even the employment of direct infusion, couldn't significantly impact the code, and dramatical differences occurred between different species and amongst different parts of Ganoderma lucidum as well. Not only ganoderic acid cluster but also certain primary metabolites served as the diagnostic compounds towards species differentiation.

CONCLUSION

2D-code might be a meaningful, practical visual way for rapid HM recognition because it is convenient to achieve the conversion amongst MS/MS dataset, 2D-barcode plot, and the chemome.