A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

“…The study presents a mix of machine learning methods for automatic diagnosis of malaria by using the feature extraction capability of Convolutional Neural Networks (CNNs) along with the efficient classification performance of traditional machine learning classifiers. …”
Get full text

TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. …”
Get full text

Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning by Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Roa’a Khaled, Andrea Buccoliero, Syed Baqir Hussain Shah, Angelo Di Terlizzi, Giacomo Di Benedetto, Marco Agostino Deriu

“…Multiple pretrained CNN models were evaluated, with Xception emerging as the optimal choice for its balance of computational efficiency and performance. …”
Get full text

Optimizing Hyperspectral Desertification Monitoring Through Metaheuristic-Enhanced Wavelet Packet Noise Reduction and Feature Band Selection by Weichao Liu, Jiapeng Xiao, Rongyuan Liu, Yan Liu, Yunzhu Tao, Tian Zhang, Fuping Gan, Ping Zhou, Yuanbiao Dong, Qiang Zhou

“…The Genetic Algorithm (GA) was also found to be effective in extracting feature bands relevant to land desertification, which enhances the classification accuracy of the model. …”
Get full text

A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder by Yuzhen Zheng, Duan Zeng, Ying Tian, Siyuan Li, Shen He, Huafang Li, Huafang Li, Huafang Li

“…Weighted gene co-expression network analysis (WGCNA) was performed to construct metabolite networks and identify modules and metabolites associated with depressive features. …”
Get full text

Modified generative adversarial network and Pseudo- Zernike matrix features extraction for human-computer interactive gesture recognition by Jing Yu, Lu Zhao

“…Firstly, the improved InceptionV2 and InceptionV2-trans structures are added to the encoder and decoder respectively to enhance the feature reduction capability of the model. Secondly, conditional batch normalization is performed in each component network to improve over-fitting, and Mish activation function is used instead of ReLU function to improve network performance. …”
Get full text

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies. by Mario Pannunzi, Thomas Nowotny

Get full text

‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion by Manash Sarma, Subarna Chatterjee

“…We have conducted a multimodal analysis and stage classification by integrating the ADNI gene expression and clinical datasets using ‘Feature-Level Fusion’. Result In the case of ADNI study participants, we obtained best multi-classification performance with ‘ROC AUC’ scores of 0. 76, 0.76, 0.71 for the CN, MCI, and Dementia stages, respectively. …”
Get full text

Machine Learning based estimation of Chlorophyll and Flavonoid content in Bitter Leaf using Color and GLCM Texture Features by Retno Damayanti, Yusuf Hendrawan, Sandra Sandra

“…Future work will focus on expanding the dataset and incorporating multi-modal imaging to further refine model performance and advance non-invasive plant biochemical analysis. …”
Get full text

Study on Skid Resistance of Asphalt Pavements Under Macroscopic and Microscopic Texture Features: A Review of the State of the Art by Wei Chen, Zhengchao Zhang, Jincheng Wei, Xiaomeng Zhang, Wenjuan Wu, Yuxuan Sun, Guangyong Wang

Get full text

Preventing water pollution using importance-performance analysis and terrain analysis by L. Sulistyowati, N. Andareswari, F. Afrianto, A. Rais, M.F. Hafa, D. Darwiyati, A.L. Ginting

“…The terrain analysis data were derived from the surface elevation data in the form of a digital elevation model.FINDINGS: According to the importance-performance analysis community assessment, urban trash management was one of the crucial yet low-rated features. …”
Get full text

Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features by Daulappa Guranna BHALKE, Betsy RAJESH, Dattatraya Shankar BORMANE

“…This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). …”
Get full text

Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk by Xiaqing Xie, Hangjiang Guo, Yueming Lu, Tianle Zhang

“…Experimental results on real datasets show that the Adap-UIL model outperforms the benchmark models, especially in the P@5 and P@10 metrics by 5 percentage points, and it captures key features more efficiently and effectively.…”
Get full text

BLTTNet: feature fusion based on BiLSTM-Transfomer-TCN for prediction of remaining useful life of aircraft engines by Yixu Yang, Xiaoying Su, Chaoyong Wang, Hongxi Liu, Kunhao Fu, Tao Xie, Zishuo Zhang

“…We utilize the efficient implicit feature extraction capability of BiLSTM to represent high-dimensional features. …”
Get full text

Integrating Viewing Direction and Image Features for Robust Multi-View Multi-Object 3D Pedestrian Tracking by R. Ali, M. Mehltretter, C. Heipke

“…Recently, there has been growing interest in the development of 3D multi-view, multi-object detection and tracking models (MV-MOD and MV-MOT), resulting in significant methodological advances. …”
Get full text

Combined inflammation-related biomarkers and clinicopathological features for the prognosis of stage II/III colorectal cancer by machine learning by Lei Liang, Ning Xu, Lanfei Ding, Xin Li, Chengxun Jiang, Jianhua Zhang, Jun Yang

Get full text

SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. …”
Get full text

Mapping the invasive Spartina alterniflora in sub-meter level with improved phenological spectral features and deep learning method by Bingfeng Zhou, Meng Xu, Jinyan Tian, Yue Huang, Jie Song, Lin Zhu, Xiumin Zhu, Xinyuan Qu, Liyan Zhang, Xiaojuan Li, Huili Gong

“…The results indicated that the developed sub-meter spectral features and the improved DeepLabv3+ model could enhance classification performance. …”
Get full text

Face to Face: Anthropometry-Based Interactive Face Shape Modeling Using Model Priors by Yu Zhang, Edmond C. Prakash

Get full text

A Dual-Perspective Self-Supervised IoT Intrusion Detection Method Based on Topology Reconstruction and Feature Perturbation by Ruisheng Li, Huimin Shen, Qilong Zhang

“…As a critical technology for securing IoT, intrusion detection systems aim to identify potential threats by analyzing network traffic features. Yet, traditional models struggle to capture the complex topological structures in IoT environments, and their training often relies heavily on large amounts of labeled data, making them unsuitable for IoT settings where massive data is continually generated. …”
Get full text