Generic Classification Research Articles

A tough nutlet to crack: Resolving the phylogeny of Thesium (Thesiaceae), the largest genus in Santalales

AbstractWith over 350 species, Thesium is the largest genus in Santalales. It is found on all continents except Antarctica; however, its highest diversity is in the Cape Floristic Region of South Africa where approximately half the species occur. Thesium samples of ca. 590 collections from throughout its entire geographic range were obtained and nuclear ribosomal ITS sequenced from 396 accessions representing 196 named taxa and 30 currently unnamed taxa for a total of ca. 230 species. In addition, two chloroplast genome spacers (trnDT, trnLF) were sequenced from 269 and 315 accessions, respectively. Maximum parsimony, maximum likelihood and Bayesian methods were employed to generate gene trees and infer phylogenies. The value of the morphological characters traditionally used in the taxonomy of the genus and previous infrageneric classifications are discussed. Broad scale relationships were generally congruent among the ITS and the chloroplast trees. For example, both the nuclear and chloroplast trees support the presence of Eurasian and African clades. In contrast, major incongruence was detected between nuclear and chloroplast trees for a number of taxa including the recently described T. nautimontanum that is sister to the entire African clade on the ITS tree. Although the causes of this incongruence are currently unknown, a novel form of chloroplast capture is hypothesized. A hypothesis of the biogeographical history of the genus based on our molecular phylogeny is presented.

Phylogeny of Prosthechea (Laeliinae, Orchidaceae) based on nrITS and plastid DNA sequences: Reassessing the lumper‐splitter debate and shedding light on the evolution of this Neotropical genus

AbstractAfter a confused taxonomic history, Prosthechea (Laeliinae, Orchidaceae) has been circumscribed in a broad sense to include species previously placed in Encyclia subg. Osmophytum. However, recent alternative classifications resurrecting old names such as Anacheilium and Hormidium, and proposing new genera such as Euchile, Panarica, Pollardia, and Pseudencyclia were presented, splitting Prosthechea into narrower genera. In this study, we provide a molecular phylogeny of Prosthechea to reassess alternative generic classifications and shed light on evolutionary aspects of this highly diverse Neotropical lineage. Eighty species of Prosthechea s.l. and 12 species of related genera were sampled. Bayesian and maximum parsimony analyses of sequences from three plastid regions (rpl32‐trnL, trnD‐trnT, ycf1) and nrITS were included. Possible conflicts between nuclear and plastid data were assessed with partitioned Bremer support. Ancestral state reconstructions of morphological characters relevant to the genus taxonomy are provided. Our results reinforce the monophyly of the lineage corresponding to a broad circumscription of Prosthechea. The small lineages corresponding to Euchile, Panarica, and Hormidium are monophyletic, whereas the larger Anacheilium, Pollardia, and Pseudencyclia are not monophyletic. Splitting Prosthechea into smaller genera is not supported by our phylogeny, and nonmonophyly also hinders the proposal of a comprehensive infrageneric classification. The phylogenetic relationships recovered reflect better the biogeographic patterns than the previously proposed morphological affinities, pointing to the existence of homoplastic floral traits within the genus. Conflicts between nuclear and plastid partitions were detected, suggesting a possible scenario of reticulate evolution.

Shotgun Metagenomics Reveals Minor Micro"bee"omes Diversity Defining Differences between Larvae and Pupae Brood Combs.

Bees represent not only a valuable asset in agriculture, but also serve as a model organism within contemporary microbiology. The metagenomic composition of the bee superorganism has been substantially characterized. Nevertheless, traditional cultural methods served as the approach to studying brood combs in the past. Indeed, the comb microbiome may contribute to determining larval caste differentiation and hive immunity. To further this understanding, we conducted a shotgun sequencing analysis of the brood comb microbiome. While we found certain similarities regarding species diversity, it exhibits significant differentiation from all previously described hive metagenomes. Many microbiome members maintain a relatively constant ratio, yet taxa with the highest abundance level tend to be ephemeral. More than 90% of classified metagenomes were Gammaproteobacteria, Bacilli and Actinobacteria genetic signatures. Jaccard dissimilarity between samples based on bacteria genus classifications hesitate from 0.63 to 0.77, which for shotgun sequencing indicates a high consistency in bacterial composition. Concurrently, we identified antagonistic relationships between certain bacterial clusters. The presence of genes related to antibiotic synthesis and antibiotic resistance suggests potential mechanisms underlying the stability of comb microbiomes. Differences between pupal and larval combs emerge in the total metagenome, while taxa with the highest abundance remained consistent. All this suggests that a key role in the functioning of the comb microbiome is played by minor biodiversity, the function of which remains to be established experimentally.

Transfer learning for galaxy feature detection: Finding giant star-forming clumps in low-redshift galaxies using Faster Region-based Convolutional Neural Network

Abstract Giant star-forming clumps (GSFCs) are areas of intensive star-formation that are commonly observed in high-redshift (z ≳ 1) galaxies but their formation and role in galaxy evolution remain unclear. Observations of low-redshift clumpy galaxy analogues are rare but the availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples much more feasible. Deep Learning (DL), and in particular Convolutional Neural Networks (CNNs), have been successfully applied to image classification tasks in astrophysical data analysis. However, one application of DL that remains relatively unexplored is that of automatically identifying and localizing specific objects or features in astrophysical imaging data. In this paper, we demonstrate the use of DL-based object detection models to localize GSFCs in astrophysical imaging data. We apply the Faster Region-based Convolutional Neural Network object detection framework (FRCNN) to identify GSFCs in low-redshift (z ≲ 0.3) galaxies. Unlike other studies, we train different FRCNN models on observational data that was collected by the Sloan Digital Sky Survey and labelled by volunteers from the citizen science project ‘Galaxy Zoo: Clump Scout’. The FRCNN model relies on a CNN component as a ‘backbone’ feature extractor. We show that CNNs, that have been pre-trained for image classification using astrophysical images, outperform those that have been pre-trained on terrestrial images. In particular, we compare a domain-specific CNN – ‘Zoobot’ – with a generic classification backbone and find that Zoobot achieves higher detection performance. Our final model is capable of producing GSFC detections with a completeness and purity of ≥0.8 while only being trained on ∼5000 galaxy images.

An integrated digital microfluidic electrochemical impedimetric lipopolysaccharide sensor based on toll-like receptor-4 protein

The spread of infectious diseases poses a global threat to human health and the economy. Conventional laboratory-based pathogen detection analytical techniques are reliable, but are labour and time consuming. Decentralized, rapid pathogen detection and classification devices are essential to boost biosecurity efforts and can aid in the advancement of modern medicine. Here, we describe the development of an integrated digital microfluidic (DMF) electrochemical impedimetric sensor for rapid and on-site detection of lipopolysaccharide (LPS), a molecular signature of Gram-negative bacteria. The sensor was fabricated by immobilizing toll-like receptor protein (TLR4) onto a gold sensing electrode that was fabricated on an indium tin oxide (ITO) DMF top plate. The top plate also housed lithographically patterned ITO pseudo-reference and auxiliary electrodes for a three-electrode electrochemical impedance (EIS) detection system. We exploited the unique feature of DMF to manipulate droplets consisting of samples, buffers, wash solutions and reagents to perform automated EIS measurements due to the interaction of TLR4 with LPS. The integrated sensor platform showed a detection limit of 35 ng/mL LPS and a linear range of up to 400 ng/mL. The small size and ease of operation of the integrated system holds great prospect for the development of portable, and automated generic pathogen detection and classification platform for point-of-need applications.

Fine-grained Recognition with Learnable Semantic Data Augmentation.

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories. Although commonly used image-level data augmentation techniques have achieved great success in generic image classification problems, they are rarely applied in fine-grained scenarios, because their random editing-region behavior is prone to destroy the discriminative visual cues residing in the subtle regions. In this paper, we propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Specifically, we produce diversified augmented samples by translating image features along semantically meaningful directions. The semantic directions are estimated with a covariance prediction network, which predicts a sample-wise covariance matrix to adapt to the large intra-class variation inherent in fine-grained images. Furthermore, the covariance prediction network is jointly optimized with the classification network in a meta-learning manner to alleviate the degenerate solution problem. Experiments on four competitive fine-grained recognition benchmarks (CUB-200-2011, Stanford Cars, FGVC Aircrafts, NABirds) demonstrate that our method significantly improves the generalization performance on several popular classification networks (e.g., ResNets, DenseNets, EfficientNets, RegNets and ViT). Combined with a recently proposed method, our semantic data augmentation approach achieves state-of-the-art performance on the CUB-200-2011 dataset. The source code will be released.

The regulatory mechanism of garlic skin improving the growth performance of fattening sheep through metabolism and immunity.

Garlic skin (GAS) has been proven to improve the growth performance of fattening sheep. However, the mechanism by which GAS affects fattening sheep is not yet clear. The aim of this study is to investigate the effects of adding GAS to feed on the growth performance, rumen and fecal microbiota, serum and urine metabolism, and transcriptomics of rumen epithelial cells in fattening sheep. GAS with 80 g/kg dry matter (DM) was added to the diet of fattening sheep to study the effects of GAS on gut microbiota, serum and urine metabolism, and transcriptome of rumen epithelial tissue in fattening sheep. Twelve Hu sheep (body weights; BW, 23.0 ± 2.3 kg and ages 120 ± 3.5 d) were randomly divided into two groups. The CON group was the basal diet, while the GAS group was supplemented with GAS in the basal diet. The trial period was 10 weeks, with the first 2 weeks being the pre-trial period. The daily average weight gain of fattening sheep in the GAS group was significantly higher than that in the CON group (p < 0.05), and the serum GSH-Px of the GAS group fattening sheep was significantly increased, while MDA was significantly reduced (p < 0.05). Based on the genus classification level, the addition of garlic peel in the diet changed the intestinal microbial composition, and the relative abundance was significantly upregulated by Metanobrevibater (p < 0.05), while significantly downregulated by Akkermansia, Parasutterella, and Guggenheimella (p < 0.05). Metabolomics analysis found that there were 166 significantly different metabolites in serum and 68 significantly different metabolites in urine between the GAS and CON groups (p < 0.05). GAS had an impact on amino acid metabolism, pyrimidine metabolism, methane metabolism, riboflavin metabolism, and unsaturated fatty acid synthesis pathways (p < 0.05). Transcriptome sequencing showed that differentially expressed genes were mainly enriched in immune regulatory function, improving the health of fattening sheep. Adding GAS can improve the energy metabolism and immune function of fattening sheep by altering gut microbiota, metabolome, and transcriptome, thereby improving the growth performance of fattening sheep.

Leaf Anatomy of Morinda citrifolia L. in Pahang, Malaysia, and its Taxonomic Significance

Leaf anatomical study has been conducted on Morinda citrifolia L. from family Rubiaceae in Kuantan, Pahang. The aim of this study was to identify and list characteristics of leaf anatomy that can be used to identify and classification of species and genus in the Rubiaceae family. Methods used in this study were cross section using sliding microtome, epidermis peeling and leaf clearing and observation under a light microscope. Results from this study showed few characteristics can be used in identification in species studied. The result of this study showed that the presence of collenchyma cells, sclerenchyma cells, cell inclusions such as raphide at the petiole and midrib and hypostomatic stomata can be useful as an additional data to identify species studied. In conclusion, leaf anatomy characteristics can be used as an additional characteristic in the identification and classification of selected Rubiaceae species.Keywords: Leaf anatomy, Morinda citrifolia

A feature selection based on genetic algorithm for intrusion detection of industrial control systems

With the popularity of Internet technology, industrial control systems (ICS) have started to access the Internet, which significantly facilitates engineers to manage ICS remotely but also exposes risks. Usually, an intrusion detection system (IDS) is used to secure network systems. Feature selection plays a crucial role in IDSs because detecting anomalies from high-dimensional network traffic features is time-consuming. However, few specific studies have been conducted for ICS. Many redundant features and data imbalance problems in ICS data lead to poor performance and low efficiency of generic IDS classification. In this paper, we design a genetic algorithm-based feature selection method for ICS characteristics. The proposed method incorporates a feature ranking fusion mechanism in the genetic algorithm for eliminating redundant features, enhances the global merit-seeking speed using the growing tree clustering idea, and we also design a new fitness function for ICS characteristics. The effectiveness and advancement of the proposed method are demonstrated on a real ICS dataset.

Generic dual-phase classification models through deep learning semantic segmentation method and image gray-level optimization

Two generic deep learning models for automatic classification of dual-phase microstructures were constructed through the semantic segmentation method of DeepLab v3+, based on a small data including 6 SEM images of the U-2Nb alloys with dual-phase microstructure and the corresponding ground truth. One model is suitable for directly classifying images with different gray-level ranges and has a good average classifying accuracy. Although the other model is only suitable for a specific gray-level range, but it cost less for training and can realize a higher classifying accuracy than the former model combined with optimal gray-level adjustment for images. The two DL models were applied to classify dual-phase microstructures of different morphologies and materials, including the isothermal cooling and continuous cooling microstructures of U-2Nb alloys, and the dual-phase steel microstructure, the results of which manifest that both the two models have high classification accuracy and excellent general applicability.

RECORDING OF RESCUE ACTIVITY OF FIRE PROTECTION UNITS

The interventions of fire protection units (FPUs) are the response of the state security system toemergent threats characteristically inscribed as appropriate to be dealt with by the FPU. All rescueinterventions undertaken by the FPU are recorded in the Decision Support System of the State FireService (SWD SFS), where an ongoing record of all current incidents is kept. Each of the incidentshandled by the FPU, after a multi-level verification of the baseline information concerning a giventhreat, is classified in accordance with the applicable Principles (Headquarters of the State FireService, 2022) as: a fire (F), a local threat (LT), false alarm (FA).The types of incidents handled by the Fire Protection Units (FPUs) and the answer to the questionof whether the adopted classification fully captures the essence of the FPU intervention will bethe subject of discussion by the authors who will analyse the area in question with referenceto both the actual conditions and the formal and legal status. In compliance with the currentlegislation, the authors will present legal boundaries for the commencement of a rescue actionwith an indication of its systematic allocation to the appropriate group of incidents (P, MZ, AF),while demonstrating the importance of the correct identification of circumstances indicating itstermination and the proper documentation of the above fact. In the subsequent part of the article,the authors propose to correct the classification of the type of incidents undertaken by the FPUs,stating that they may be recorded taking into account events of an authoritative nature as well asthose intended to support the activities of other services, entities and inspections. The authors’scientific considerations in the final part of the article will be presented in the form of clarifiedproposals for the revision of the rules for the generic classification of events undertaken by FPUs.

A revised genus-level classification for Cerrenaceae (Polyporales, Agaricomycetes).

Cerrenaceae is a small family of polypores and hydnoid fungi in the order Polyporales (Basidiomycota). The family consists of white-rot fungi, some of which are serious tree pathogens. Combining morphological evidence with a phylogenetic dataset of six genetic markers, we revise generic concepts in the family and propose a seven-genus classification system for the family. Two genera are introduced as new: the monotypic Acanthodontia for Radulodon cirrhatinus, and Lividopora for the Rigidoporus vinctus complex. We re-introduce the name Somion for the Spongipellis delectans complex. Other recognized genera in the family are Cerrena, Irpiciporus, Pseudolagarobasidium, and Radulodon. New species introduced are Irpiciporus branchiformis from Tanzania, Lividopora armeniaca, and L. facilis from Southeast Asia, and Somion strenuum from East Asia. We provide nomenclatural comments on all the names combined to the above Cerrenaceae genera and typify Cerrena unicolor, C. zonata, Polyporus carneopallens (= L. vincta), Somion occarium, and S. unicolor. The genus Hyphoradulum belongs to Cystostereaceae (Agaricales), and we transfer the type species H. conspicuum to Crustomyces. Our study highlights the importance of integrating different basidiocarp types in analyses when revising genus classification in macrofungi. Citation: Miettinen O, Vlasák J, Larsson E, Vlasák J Jr., Seelan JSS, Hernawati, Levicky Q, Larsson K-H, Spirin V (2023). A revised genus-level classification for Cerrenaceae (Polyporales, Agaricomycetes). Fungal Systematics and Evolution 12: 271-322. doi: 10.3114/fuse.2023.12.14.

Classification of Genus Three Zero-Divisor Graphs

Classification of Genus Three Zero-Divisor Graphs

SpiderID_APP: A User-Friendly APP for Spider Identification in Taiwan Using YOLO-Based Deep Learning Models

Accurate and rapid taxonomy identification is the initial step in spider image recognition. More than 50,000 spider species are estimated to exist worldwide; however, their identification is still challenging due to the morphological similarity in their physical structures. Deep learning is a known modern technique in computer science, biomedical science, and bioinformatics. With the help of deep learning, new opportunities are available to reveal advanced taxonomic methods. In this study, we applied a deep-learning-based approach using the YOLOv7 framework to provide an efficient and user-friendly identification tool for spider species found in Taiwan called Spider Identification APP (SpiderID_APP). The YOLOv7 model is integrated as a fully connected neural network. The training of the model was performed on 24,000 images retrieved from the freely available annotated database iNaturalist. We provided 120 genus classifications for Taiwan spider species, and the results exhibited accuracy on par with iNaturalist. Furthermore, the presented SpiderID_APP is time- and cost-effective, and researchers and citizen scientists can use this APP as an initial entry point to perform spider identification in Taiwan. However, for detailed species identification at the species level, additional methods like DNA barcoding or genitalic structure dissection are still considered necessary.

Assembly and Comparative Analysis of the Complete Mitochondrial Genome of Ilex macrocarpa

The plant mitochondrial genome (mitogenome) has a complex organization and carries genetic traits of value in exploiting genetic resources. In this study, the Ilex macrocarpa mitogenome was sequenced and assembled to understand the genetic diversity and phylogenetic relationship. The mitogenome has a cyclic molecular structure and is 539,461 bp long with a GC content of 45.53%. It contained 39 protein-coding proteins, 19 tRNA genes, and 3 rRNA genes. The 146 simple sequence repeats and 591 dispersed repeat sequences were identified in the mitogenome. Codon usage analysis revealed a preference for codons ending in A/T. A total of 517 C to U RNA editing sites were predicted, with nad4 and ccmB being edited most frequently (39 times). Phylogenetic analysis via mitochondrial protein-coding genes showed that the Aquifoliales order contains sister plants to Lamiales, Solanales, and Gentianales. In addition, the conflicts between chloroplast and mitochondrial phylogeny were also observed. This study provides a theoretical framework for understanding the evolution, classification, and identification of the Ilex genus, which lays an important foundation for future molecular breeding investigations.

An illustrated guide to the lady beetles (Coleoptera: Coccinellidae) of the Indian Subcontinent. Part II. Tribe Chilocorini.

An illustrated guide to the lady beetles of the tribe Chilocorini (Coleoptera: Coccinellidae: Coccinellinae) of the Indian region is presented. Brief diagnostic accounts of six genera and 31 species are given with supporting illustrations of the diagnostic characters, including genitalia, wherever possible. Details of distribution, host insects / plant associations, and seasonal occurrences are provided for the first time based on extensive collections, label data and published literature. Chilocorus keralensis sp. n. is described from the Western Ghats, South India. Brumoides lineatus sensu Kapur 1967, recorded from the Andaman Islands, is found to be distinct from B. lineatus Weise 1885, and described and illustrated as B. andamanensis sp. n. Phaenochilus indicus Miyatake, 1970 and P. flaviceps Miyatake, 1970 are transferred to Chilocorus Leach (comb. n.) following the latest phylogenetic generic classification of Chilocorini. Three species, Chilocorus albomarginalis (Li & Wang), Chilocorus nigricaeruleus Li & Wang and Chilocorus yunlongensis Cao & Xiao, all originally described from China, are added to the Indian fauna of Chilocorini based on material examined from the north-eastern Indian states of Assam, Meghalaya, and Manipur, respectively. For nine species, illustrations of the life stages are included to facilitate their field identification. A key to the genera and an updated and revised checklist of the Chilocorini of the Indian subcontinent are also provided.

Michaël Ferrier Inter-Media: Writing the World Between Image, Music and Text

Informed by his practice of music, drawing, photography and calligraphy, and depicting various experiences of border crossing between cultures, languages and media, Michaël Ferrier’s eclectic writings are characterised by an aesthetic hybridity that defies generic classifications. In line with his critique of the primacy of the visual in contemporary societies, his versatile prose frequently uses synaesthesia and refers to numerous art forms such as jazz, painting or cinema, thereby appealing to the reader’s five senses. Ceaselessly experimenting in his literary work with the flexible form of the novel, between fact and fiction, Ferrier published in 2019 his first photo-text, Scrabble, which is analysed in depth in the second part of this article. In this autobiographical narrative focusing on his childhood in Chad in the 1970s, the numerous black and white illustrations introduce a complex game of presence and absence that mirrors the way memory functions. Haunted by the traumatic advent of civil war that brutally halted Ferrier’s stay in Africa, this multi-media work circles around an ominous image of the author playing Scrabble with his brother as the conflict is about to break out; evoked at the beginning and the end of the text, this decisive image however remains elusive—a “ghost image.”

A classification of genus 0 modular curves with rational points

Let E E be a non-CM elliptic curve defined over Q \mathbb {Q} . Fix an algebraic closure Q ¯ {\overline {\mathbb Q}} of Q \mathbb {Q} . We get a Galois representation \[ ρ E : G a l ( Q ¯ / Q ) → G L 2 ( Z ^ ) \rho _E \colon {Gal}({\overline {\mathbb Q}}/\mathbb {Q})\to GL_2({\widehat {\mathbb {Z}}}) \] associated to E E by choosing a system of compatible bases for the N N -torsion subgroups of E ( Q ¯ ) . E({\overline {\mathbb Q}}). Associated to an open subgroup G G of G L 2 ( Z ^ ) GL_2({\widehat {\mathbb {Z}}}) satisfying − I ∈ G -I \in G and det ( G ) = Z ^ × \det (G)={\widehat {\mathbb {Z}}}^{\times } , we have the modular curve ( X G , π G ) (X_G,\pi _G) over Q \mathbb {Q} which loosely parametrises elliptic curves E E such that the image of ρ E \rho _E is conjugate to a subgroup of G t . G^t. In this article we give a complete classification of all such genus 0 0 modular curves that have a rational point. This classification is given in finitely many families. Moreover, for each such modular curve morphism π G : X G → X G L 2 ( Z ^ ) \pi _G \colon X_G \to X_{GL_2({\widehat {\mathbb {Z}}})} can be explicitly computed.

“[L]e divorce terrible entre le cœur et la chair:” Joseph Kessel’s Belle de Jour (1928)

The publication in 2020 of Joseph Kessel’s writings in Gallimard’s distinguished Pléiade series prompted Gaby Levin to publish an article titled “France Rediscovers Joseph Kessel, the Jewish Writer of Belle de Jour.” It is curious that out of all of Kessel’s many publications Levin should single out Belle de Jour given that it has garnered virtually no scholarly attention, in contrast to Luis Buñuel’s 1967 film adaptation starring Catherine Deneuve. The novel tells the story of Séverine Sérizy, a wealthy young married woman who, in sync with her husband emotionally and intellectually but not sexually, takes up afternoon work as a prostitute. In the preface, Kessel explains, in reaction to accusations of “licence inutile, voire de pornographie,” that his intent was simply to “montrer le divorce terrible entre le cœur et la chair, entre un vrai, immense et tendre amour et l’exigence implacable des sens.” This study focuses at the micro-textual level on how Kessel works out his promised chair/cœur binary, one which not only figures into, but structures, a text that by all appearances follows the blueprint for a classic roman de formation, a generic classification that Kessel ultimately problematizes.

Klasifikasi Citra Genus panthera Menggunakan Pendekatan Deep learning Berbasis Convolutional Neural network (CNN)

This research aims to develop an image classification method for the panthera genus using a deep learning approach based on Convolutional Neural network (CNN). The panthera genus includes large species such as tigers, lions, leopards, and jaguars, which share similarities in appearance but also differences in fur patterns, body size, and habitat. Image classification of the panthera genus is important in various applications, including wildlife conservation and biological research. In this study, image datasets of tigers, lions, and leopards were collected from various sources to a total of 6,290 images. The proposed method involves image pre-processing, such as resizing, converting and normalization, and the use of a Convolutional Neural network (CNN) model to perform classification. The CNN model is implemented and trained using training data to recognize specific visual patterns in the images of each species. The results of this study show that the CNN-based deep learning approach can achieve high accuracy in the classification of panthera genus images of 85.21%. This method can correctly distinguish between tiger, lion, and leopard images based on unique visual features. In addition, the deep learning approach also offers advantages in efficiency and scalability to cope with the large number of images in the dataset. This research makes an important contribution to the development of wildlife image classification methods using a CNN-based deep learning approach.