Classification Method Research Articles

Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2

BackgroundFungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.ResultsHere we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.ConclusionsHiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient’s health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

A new classification for emergency critically ill patients and analysis of their adverse events during intrahospital transport: A cluster analysis.

Critical patients may experience various adverse events during transportation within hospitals. Therefore, quickly evaluating and classifying patients before transporting them from the emergency department and focusing on managing high-risk patients are critical. At present, no unified classification method exists; all the current approaches are subjective. To ensure transportation safety, we conducted a cluster analysis of critically ill patients transferred from the emergency department to the intensive care unit. Single-centre cohort study. This study was conducted at a comprehensive first-class teaching hospital in Beijing. Convenience sampling and continuous enrolment were employed. We collected data from 1 January 2019, to 31 December 2021. All patients were transferred from the emergency department to the intensive care unit, and cluster analysis was conducted using five variables. A total of 584 patients were grouped into three clusters. Cluster 1 (high systolic blood pressure group) included 208 (35.6%) patients. Cluster 2 (high heart rate and low blood oxygen group) included 55 (9.4%) patients. Cluster 3 (normal group) included the remaining 321 (55%) patients. The oxygen saturation levels of all the patients were lower after transport, and the proportion of adverse events (61.8%) was the highest in Cluster 2 (p < .05). This study utilized data on five important vital signs from a cluster analysis to explore possible patient classifications and provide a reference for ensuring transportation safety. Before transferring patients, we should classify them and implement targeted care. Changes in blood oxygen levels in all patients should be considered, with a focus on the occurrence of adverse events during transportation among patients with high heart rates and low blood oxygen levels.

Comprehensive Classification of the Capitellar Injury Concurrent with Radial Head Fracture.

Capitellar injury (CI) includes capitellar cartilage injury (CCI) and capitellar fracture (CF). A comprehensive classification of CI concurrent with radial head fracture (RHF) that can guide surgical strategy is lacking in the literature. Therefore, this study aimed to introduce a comprehensive classification of CI concurrent with RHF and investigate its value. A total of 35 patients with CI concurrent with RHF confirmed by surgical exploration were retrospectively analyzed, includingmales in 19 cases and females in 16 cases. RHF was classified according to the Mason classification, and CI was classified into six types, including 3 types of CCI and CF, each based on the site and degrees of injuries (comprehensive classification method proposed in this study). The classification results were analyzed. Two radiologists were selected to independently classify the CI, and the inter- and intra-observer agreements were analyzed with kappa statistics. Mason Type I, II, III, and IV RHF accounted for 14.3%, 48.6%, 37.1%, and 0% of cases, respectively. Type I, II, III, IV, V, and VI CIs accounted for 22.9%, 34.3%, 25.7%, 11.4%, 2.9%, and 2.9% of cases, respectively. Therewas no obvious relationship between the CI and RHF types (p > 0.05). All Type I CIs underwent removal, 9 Type II CIs underwent microfracture repair, and 3 Type II CIs underwent removal. All Type III CIs underwent fixation, one Type IV CI underwent removal, and 3 Type IV CIs underwent fixation, one Type V CI underwent fixation, and one Type VI CI underwent arthroplasty. The inter- and intra-observer kappa coefficients were 0.830 ~ 0.905 and 0.805 ~ 0.892, respectively. At 12 months postoperatively, the elbow function evaluated by MEPS was 91, with an excellent and good rate of 97%. Different types of CI differ not only in pathology but also in treatment methods. The CI comprehensive classification put forth in this paper for the first time reflects different types of pathology well, with high consistency and repeatability, and can guide the selection of surgical methods, leading to satisfactory postoperative results.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.

Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly similar breeds due to the challenges in qualifying the phenotypic character. Machine learning algorithms show unique advantages in breed classification using genomic information. To evaluate the classification methods for Chinese cattle breeds, this study utilized genomic SNP data from 213 individuals across seven Chinese local breeds and compared the classification accuracies of three feature selection methods (FST value sorting and screening, mRMR, and Relief-F) and three machine learning algorithms (Random Forest, Support Vector Machine, and Naive Bayes). Results showed that: 1) using the FST method to screen more than 1500 SNPs, or using the mRMR algorithm to screen more than 1000 SNPs, the SVM classification algorithm can achieve more than 99.47% classification accuracy; 2) the most effective algorithm was SVM, followed by NB, while the best SNP selection method was FST and mRMR, followed by Relief-F; 3) species misclassification often occurs between breeds with high similarity. This study demonstrates that machine learning classification models combined with genomic data are effective methods for the classification of local cattle breeds, providing a technical basis for the rapid and accurate classification of cattle breeds in China.

A deep learning drug screening framework for integrating local-global characteristics: A novel attempt for limited data

At the beginning of the "Disease X" outbreak, drug discovery and development are often challenged by insufficient and unbalanced data. To address this problem and maximize the information value of limited data, we propose a drug screening model, LGCNN, based on convolutional neural network (CNN), which enables rapid drug screening by integrating features of drug molecular structures and drug-target interactions at both local and global (LG) levels. Experimental results show that LGCNN exhibits better performance compared to other state-of-the-art classification methods under limited data. In addition, LGCNN was applied to anti-SARS-CoV-2 drug screening to realize therapeutic drug mining against COVID-19. LGCNN transcends the limitations of traditional models for predicting interactions between single drug targets and shows new advantages in predicting multi-target drug-target interactions. Notably, the cross-coronavirus generalizability of the model is also implied by the analysis of targets, drugs, and mechanisms in the prediction results. In conclusion, LGCNN provides new ideas and methods for rapid drug screening in emergency situations where data are scarce.

Extracting waterline and inverting tidal flats topography based on Sentinel-2 remote sensing images: A case study of the northern part of the North Jiangsu radial sand ridges

Muddy tidal flats with landforms vary widely over space and time. The evolution of tidal flats topography has had a significant impact on the region. Therefore, there is a need to develop methods for rapidly and accurately characterizing tidal flats topography. This study applied thresholding segmentation (TS), machine learning, and two image classification methods to Sentinel-2 remote sensing images to extract the instantaneous waterline of the northern part of the North Jiangsu Radial sand Ridges (NJRSR). Tidal level data at specific time intervals were obtained using the Delft3D and TPXO9 models, after which tidal flats topography inversion was conducted. As a result, varied among the different classification methods, with the K-Nearest Neighbors (KNN) algorithm producing the optimal waterline extraction. Furthermore, the accuracy of tidal level simulation was improved by employing Delft3D software and constructing a scaled-down model of the study area. Comparisons with Delft3D model simulations of tidal levels with observations at three tide gauge stations yielded accuracies of 17.5 cm, 18.5 cm and 17.9 cm, this result showed that the establishment of the tidal level model can enhance the accuracy of tidal flats Digital Elevation Model (DEM) inversion. In fact, the combined application of the KNN algorithm and Delft3D model within retrieval of tidal level provided the most accurate topographic inversion, with an average error of 0.297 m, which was reduced to 0.292 m after median filtering, and the results indicated a trend of southward erosion in the tidal flats in the study area, with a turning point roughly located in the southern part of Doulong Port. The results of this study can help improve the future monitoring of dynamic changes in intertidal wetland topography and the conservation and development of tidal flats areas.

Multi-scale superpixel-based nearest subspace classifier for mucilage detection from hyperspectral PRISMA data

Hyperspectral imaging has a critical role in observing the Earth's surface, providing images with rich spectral information. As a result, it has become an essential tool for monitoring and addressing environmental issues such as pollution, water balance, and climate change. Among these challenges, marine pollution poses a significant ecological threat, as evidenced by the recent mucilage incident in Turkey in April 2021. Mucilage, a dense mucus-like substance resulting from the overgrowth of marine plankton and microorganisms, detrimentally affects marine ecosystems when its levels surge. Traditional mucilage detection involves labour-intensive sampling and laboratory analysis, rendering it impractical for vast marine environments. Remote sensing techniques offer a promising alternative for automated mucilage detection from satellite imagery. In this study, we propose a multi-scale superpixel-based classification approach for mucilage detection, using the PRISMA satellite's hyperspectral mucilage datasets captured over the Sea of Marmara. Our method performs classification by analyzing the closeness between the subspace spanned by the samples in the superpixel and the subspace spanned by the within-class training samples. We formulate the solution to this approach via a Canonical Correlation Analysis framework and show that our proposed method outperforms its competitors, especially when the training data is limited. Our comprehensive experiments include assessing the transferability of classification methods on datasets collected from different locations and at different times. We compared the performance of our proposed method with state-of-the-art spectral-spatial classification methods, including deep learning-based random patches network (RPNet), structural-kernel collaborative representation (SKCR), and window based nearest subspace classifier (WBNSC). The results show that the proposed method outperforms existing techniques in most cases. The datasets and source code are publicly available on GitHub at https://github.com/kgtoker/MSBNSC, enabling more comprehensive access and collaboration in environmental monitoring efforts.

Classification methods for hyperspectral remote sensing images with weak texture features

In order to provide method support for remote sensing image classification of weak texture region and improve the classification effect, a remote sensing image classification model for weak texture region was constructed. Firstly, the Gray Level Co-occurrence Matrix (GLCM) was used to extract texture features, then the correlation between texture features was reduced by dimensionality reduction method, and finally the spectral features were fused to classify remote sensing images. In the experimental part, neural network classifier was used to classify images based on texture feature, spectral feature and spectral features combined with texture features, and the results ware compared with object-oriented classification method. The analysis of texture extraction in the study area shows that the texture feature image obtained in the 5 × 5 window has higher resolution than that in the 7 × 7 window, and more robust texture features can be obtained in the range of 4∼8 pixel pairs. When using neural network classifier for classification, compared with images based on spectral features and texture features, the overall classification accuracy (OA) of the proposed classification model is increased by 16.72% and 11.16%, respectively, and the Kappa coefficient is increased by 0.2824 and 0.1943, respectively. Compared with the object-oriented classification method, the overall classification accuracy of the proposed classification scheme is increased by 1.15%, and the Kappa coefficient is increased by 0.0191. The constructed model has good classification effect and high classification accuracy for remote sensing images of weak texture region, and can provide scientific reference for remote sensing image classification of weak texture region.

Using experience classification for training non-Markovian tasks

Unlike standard Reinforcement Learning (RL) model, many real-world tasks are non-Markovian, which requires long-term memory and dependency. Hence solving a non-Markovian task is more difficult and challenging than solving a Markovian one. In this paper, we propose a novel RL approach for training non-Markovian tasks expressed in temporal logic LTLf (Linear Temporal Logic over Finite Traces). To this end, an encoding of linear complexity from LTLf into MDPs (Markov Decision Processes) is introduced in order to take advantage of advanced RL algorithms. We further propose an experience classification method based on the automaton structure (theoretically equivalent to LTLf specifications). An automatic reward shaping technique and a prioritized experience replay mechanism are developed to cooperate with the classification method to improve the performance of RL algorithms. We provide empirical evaluations on two widely used benchmark problems, Waterworld and Cartpole, both augmented with complex non-Markovian tasks. The evaluations are conducted with respect to the metrics of training speed, policy quality, convergence rates, computational efficiency and scalability etc. The experimental results show that our approach achieves superior performance over other relevant studies, specially with an average improvement of 133% in convergence rate and a reduction of 11% in training time.

Brain tumour detection via EfficientDet and classification with DynaQ-GNN-LSTM

The early detection and accurate staging of brain tumors are critical for effective treatment strategies and improving patient outcomes. Existing methods for brain tumor classification often struggle with limitations such as suboptimal precision, accuracy, and recall rates, alongside significant delays in processing. The current methodologies in brain tumor classification frequently encounter issues such as inadequate feature extraction capabilities and limited accuracy in segmentation, which impede their effectiveness. To address these challenges, the proposed model integrates Fuzzy C-Means for segmentation, leveraging its ability to enhance the accuracy in distinguishing tumor regions. Bounding boxes surrounding identified tumour regions are produced by the method by efficiently utilising calculated region attributes. The use of Vision Transformers for feature extraction marks a significant advancement, offering a more nuanced analysis of the intricate patterns within brain imaging data samples. These features are then classified using a Dyna Q Graph LSTM (DynaQ-GNN-LSTM), a cutting-edge approach that combines the strengths of deep learning, reinforcement learning, and graph neural networks. The superiority of the proposed model is evident through its performance on multiple datasets. It demonstrates an 8.3% increase in precision, 8.5% increase in accuracy, 4.9% increase in recall and 4.5% increase in specificity, alongside 2.9% reduction in delay compared to existing methods. In conclusion, the proposed method offers an efficient solution to the challenges faced in brain tumor classification. The study's findings underscore the transformative impact of integrating cutting-edge technologies in medical diagnostics, paving the way for more accurate, and timely health interventions for clinical scenarios.

Safe dynamic sparse training of modified RBF networks for joint feature selection and classification

Developing an efficient embedded feature selection method for both binary and multiclass classification problems is a fundamental topic to be further studied. In this paper, the conventional radial basis function (RBF) network is modified for joint feature selection and classification. By utilizing the anisotropic Gaussian basis function, an individual weight parameter is assigned to each feature for feature weighting. The L1-regularized loss function is designed by combining the softmax cross entropy loss with L1 regularization terms imposing on feature and output weights. A specialized active-set limited-memory projected Quasi-Newton (SAL-PQN) algorithm is proposed to minimize the L1-regularized loss function, which can optimize all the adjustable parameters and zero out the redundant feature and output weights simultaneously. Furthermore, a safe dynamic pruning strategy is integrated into the SAL-PQN algorithm for dynamic sparse training of the modified RBF network, which continuously excludes the dynamically zeroed-out feature weights from the training process. It is theoretically assured of yielding the same gradient updates as SAL-PQN, thus the relevant computation of the zeroed-out feature weights in the forward and backward propagations can be safely eliminated. The experimental results demonstrate the effectiveness and efficiency of the proposed methods for joint feature selection and classification, and illustrate the practical utility of the inherent feature weighting capability.

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification

State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervised domain adaptation) or unlabeled samples (unsupervised domain adaptation). Active learning is a method to select informative samples to obtain maximum performance from minimum annotations. Selecting informative target domain samples can improve model performance and robustness, and reduce data demands. This paper proposes a novel pipeline called ALFREDO (Active Learning with FeatuRe disEntangelement and DOmain adaptation) that performs active learning under domain shift. We propose a novel feature disentanglement approach to decompose image features into domain specific and task specific components. Domain specific components refer to those features that provide source specific information, e.g., scanners, vendors or hospitals. Task specific components are discriminative features for classification, segmentation or other tasks. Thereafter we define multiple novel cost functions that identify informative samples under domain shift. We test our proposed method for medical image classification using one histopathology dataset and two chest X-ray datasets. Experiments show our method achieves state-of-the-art results compared to other domain adaptation methods, as well as state of the art active domain adaptation methods.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39% in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

Online network traffic classification based on external attention and convolution by IP packet header

Network traffic classification is an important part of network monitoring and network management. Three traditional methods for network traffic classification are flow-based, session-based, and packet-based, while flow-based and session-based methods cannot meet the real-time requirements and existing packet-based methods will violate user’s privacy. To solve the above problems, we propose a network traffic classification method only by the IP packet header, which satisfies the requirements of both the user’s privacy protection and online classification performances. Through statistical analyses, we find that IP packet header information is effective on the network traffic classification tasks and this conclusion is also demonstrated by experiments. Furthermore, we propose a novel external attention and convolution mixed (ECM) model for online network traffic classification. This model adopts both low-computational complexity external attention and convolution to respectively extract the byte-level and packet-level characteristics for traffic classification. Therefore, it can achieve high classification accuracy and low time consumption. The experiments show that ECM can reach over 96% classification accuracy on four datasets and the classification time is 0.36ms per packet which can meet the real-time requirements. The code is available at https://github.com/CNZZQ1030/ECM-for-Network-Traffic-Classification.

The High-Altitude Volcanic Caves of Mount Etna (Eastern Sicily): A Relevant Refuge for Some Ptero-Bryophyte Communities

A phytosociological analysis of the plant communities in high-altitude volcanic caves of Mount Etna (Sicily), based on literature data and unpublished relevés, is presented. A total of 147 phytosociological relevés were processed and analyzed using classification and ordination methods. Classification of the relevés, supported by ordination, showed two main vegetation groups: the first one includes communities of the Pohlion crudae alliance, and the other includes the vegetation from the Pohlio crudae–Asplenion septentrionalis alliance. Furthermore, two new communities, Pohlio crudae–Cystopteridetum dickieanae subass. amphidietosum mougeotii and Pohlio crudae–Cystopteridetum dickieanae subass. polystichetosum lonchitis, are proposed for Etna. The high-altitude caves can be considered a refuge for these ptero-bryophytic rare communities.

Multivariate technique for the Prediction and Classification of Brain Tumor using deep shallow network

Brain tumors, a major cause of death, carry a substantial socio-economic burden. It is essential to distinguish between various types of tumors, such as gliomas, meningiomas, and pituitary tumors, using MRI data. This assists radiologists and eliminates the need for risky histology biopsies. The objective of the proposed work is to create an automated system capable of distinguishing tumor types. This system integrates a deep, shallow network with the ResNet18 model for feature extraction and machine learning methods for classification on images from the Kaggle dataset. The standalone ResNet18 model produced an accuracy of 91.42% and to enhance the accuracy, features extracted from the pool5 layer of the ResNet18 architecture were examined by chi-square algorithm. The optimal deep features extracted using the chi-square algorithm were integrated with 17 machine learning classifier techniques comprising variants of SVM, KNN, and ensemble techniques. The proposed method predicted the classes, yielding an accuracy of 99.9% for cubic SVM among the variants of SVM, 94% for cosine KNN among the variants of KNN, and 99.8% for the Adaboost algorithm only, with a maximum of the top 10 features justified using local interpretable model-agnostic (LIME) and gradient-weighted class activation mapping (Grad-CAM) visualization techniques.

Multivariate technique for the Prediction and Classification of Brain Tumor using deep shallow network

Brain tumors, a major cause of death, carry a substantial socio-economic burden. It is essential to distinguish between various types of tumors, such as gliomas, meningiomas, and pituitary tumors, using MRI data. This assists radiologists and eliminates the need for risky histology biopsies. The objective of the proposed work is to create an automated system capable of distinguishing tumor types. This system integrates a deep, shallow network with the ResNet18 model for feature extraction and machine learning methods for classification on images from the Kaggle dataset. The standalone ResNet18 model produced an accuracy of 91.42% and to enhance the accuracy, features extracted from the pool5 layer of the ResNet18 architecture were examined by chi-square algorithm. The optimal deep features extracted using the chi-square algorithm were integrated with 17 machine learning classifier techniques comprising variants of SVM, KNN, and ensemble techniques. The proposed method predicted the classes, yielding an accuracy of 99.9% for cubic SVM among the variants of SVM, 94% for cosine KNN among the variants of KNN, and 99.8% for the Adaboost algorithm only, with a maximum of the top 10 features justified using local interpretable model-agnostic (LIME) and gradient-weighted class activation mapping (Grad-CAM) visualization techniques.

Wildlife habitat suitability analysis and mapping the former dhidhessa wildlife sanctuary using GIS-based analytical hierarchal process and weighted linear combination methods

Management of wildlife populations and the creation of conservation programs depend on the evaluation of wildlife habitats. Habitat suitability mapping is a technique typically used to map appropriate environmental factors and assess species existence in different areas. This study aims to map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, Ethiopia, using GIS-based Analytical Hierarchal Process and Weighted Linear Combination Methods. This study used both primary and secondary data sources. Datasets used to collect data include Digital Elevation Model (DEM), Landsat 9 (OLI/TIRS) and population data. Beside, large mammalian species occurrence data obtained from field survey was used. To map wildlife habitat suitability sites in Former Dhidhessa wildlife sanctuary, environmental factors such as proximity of road network, distance to surface water, land use land cover types, slope, population density and topography were used with the integration of species occurrence data recorded from the study area. These environmental factors scaled to common ranges, and assigned appropriate weights. The quantile classification method was utilized to classify suitability index into five zones (unsuitable, less suitable, moderately suitable, suitable, and highly suitable) to produce the map. Accordingly, the model revealed that 18.9 % of the study area is highly suitable, 19.5 % is suitable, 19.9 % is moderately suitable, 19.5 % is less suitable, and 22.2 % is unsuitable for wildlife. About 58.3 % of the study area is currently identified as suitable for wildlife whereas 41.7 % is unsuitable. This showed that the former Dhidhessa wildlife sanctuary is still having large suitable habitats that can support wide ranges of wildlife. Hence, based on the developed preliminary habitat suitability indices and maps, the federal and local governments shall reevaluate the status of former Dhidhessa wildlife sanctuary and develop future conservation and management plans to enhance the conservation of wildlife and their habitats in the area.