Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. by Maggy T Sikulu-Lord, Michael D Edstein, Brendon Goh, Anton R Lord, Jye A Travis, Floyd E Dowell, Geoffrey W Birrell, Marina Chavchich

“…<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). …”
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Enhanced Intrusion Detection in In-Vehicle Networks Using Advanced Feature Fusion and Stacking-Enriched Learning by Ali Altalbe

“…This work implements and validates the FFS-IDS using real-time car hacking data sets and achieves better performance than individual decision tree classifiers and popular ensemble learning methods such as Random Forest, LightGBM, AdaBoost, and ExtraTree algorithms. …”
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A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing by Muhammad Asif, Mohammad Abrar, Faizan Ullah, Abdu Salam, Farhan Amin, Isabel de la Torre, Mónica Gracia Villar, Helena Garay, Gyu Sang Choi

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Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning by Mustapha Mouzai, Mohamed Amine Riahla, Amor Keziou, Hacène Fouchal

“…As a main contribution, we propose a more interpretable approach to exploit the dataset by extracting individual mission sequences, handling non-stationary features, and converting the GPS raw data into a simplified structured format. Then, we design tree-based machine learning algorithms, namely decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), for the purpose of classifying signal types and to recognize spoofing attacks. …”
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Comparative Model Efficiency Analysis Based on Dissimilar Algorithms for Image Learning and Correction as a Means of Fault-Finding by Joe Benganga, Tshepo Kukuni, Ben Kotze, Lepekola Lenkoe

“…Furthermore, the results demonstrate higher data losses in the data transfer learning due to data recycling, suggesting that the model is prone to image feature losses when the model threshold is set at 75%. …”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…The detection methods are then categorized into three main groups: traditional image processing, machine learning, and deep learning, with their principles, case studies, limitations, and future development directions analyzed. …”
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Machine Learning-Based Intrusion Detection Systems for the Internet of Drones: A Systematic Literature Review by Mostafa Ogab, Sofiane Zaidi, Abdelhabib Bourouis, Carlos T. Calafate

“…In this paper, we present a systematic literature review to examine the current research area of intrusion detection systems for IoD, focusing on the effectiveness of implemented machine learning models, employed datasets, existing challenges and limitations, as well as emerging trends and future research directions. …”
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Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm by Faris Kateb, Mahmoud Ragab, Felwa Abukhodair, Omar Ahmed Abdulkader, Louai A. Maghrabi, Sami Saeed Binyamin, Mohammed Khaled Al-Hanawi

“…To achieve this, the ESHCS-DLJSO approach utilizes a min-max normalization technique to transform the input data into a more suitable format. The bacterial foraging optimization algorithm (BFOA) method is used for feature extraction. …”
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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

“…This study underscores the potential of integrating spectroscopic data with advanced machinelearning techniques for meat classification and fraud detection.…”
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Research on corrosion defect identification and risk assessment of well control equipment based on a machine learning algorithm by He Zhang, Jiangna Cao, Haibo Liang, Gang Cheng

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Unobtrusive Sleep Posture Detection Using a Smart Bed Mattress with Optimally Distributed Triaxial Accelerometer Array and Parallel Convolutional Spatiotemporal Network by Zhuofu Liu, Gaohan Li, Chuanyi Wang, Vincenzo Cascioli, Peter W. McCarthy

“…To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. …”
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Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu, Yong He

“…Meanwhile, optimized feature selection algorithms identified effective wavelengths (EWs) and vegetation indices (VIs) for yield estimation. …”
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Research on dimension measurement algorithm for parcel boxes in high-speed sorting system by Ning Dai, Jingchao Chen, Xudong Hu, Yanhong Yuan

“…In this paper, we propose a 3D localization algorithm for rectangular packaging boxes based on deep learning, and design a lightweight parcel box detection model, the Efficient Object detection Network (EODNet). …”
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Prediction of Anthocyanin Content in Purple-Leaf Lettuce Based on Spectral Features and Optimized Extreme Learning Machine Algorithm by Chunhui Liu, Haiye Yu, Yucheng Liu, Lei Zhang, Dawei Li, Junhe Zhang, Xiaokai Li, Yuanyuan Sui

“…Finally, dung beetle optimization (DBO), subtraction-average-based optimization (SABO), and the whale optimization algorithm (WOA) optimized the extreme learning machine (ELM) for modeling. …”
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An annotated image dataset of urban insects for the development of computer vision and deep learning models with detection tasksFigshare by Min Hui Lim, Hiang Hao Chan, Song-Quan Ong

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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…Optimization, machine learning, and deep learning algorithms are applied in this study. …”
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Mapping forest-agroforest frontiers in the Peruvian Amazon with deep learning and PlanetScope satellite data by Wanting Yang, Daniel Ortiz-Gonzalo, Xiaoye Tong, Dimitri Gominski, Rasmus Fensholt

“…To achieve this, we combine deep learning and remote sensing data, including 3-m PlanetScope satellite imagery, a Digital Elevation Model (DEM), and temporal data from the Landtrendr change detection algorithm. …”
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A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach by Paola Argiento, Anna D'Agostino, Rossana Castaldo, Monica Franzese, Matteo Mazzola, Ekkehard Grünig, Lavinia Saldamarco, Valeria Valente, Alessandra Schiavo, Erica Maffei, Davide Lepre, Antonio Cittadini, Eduardo Bossone, Michele D'Alto, Luna Gargani, Alberto Maria Marra

“…Aim of the current study was to develop a Machine Learning (ML) algorithm based on the analysis of anamnestic data to predict the presence of an invasively measured PH. …”
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A comprehensive Malabar Spinach dataset for diseases classificationMendeley Data by Mushfiqur Rahman, Md Al Mamun

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Study on User Fraud Identification of PV Expansion Based on a Bottom-Up Approach of a DELM Algorithm Improved by SSA for a Power Distribution Network by Wang Jinpeng, Wei Haojie, Dou Shunyao, Jeremy-Gillbanks, Zhao Xin

“…Finally, the proposed approach is validated both in simulation and using measurement data from real networks. The algorithm&#x2019;s performance in detecting fraudulent behavior in outdated electromagnetic meters is evaluated and verified. …”
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