A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter by Serpil Aslan, Muhammed Yildirim

“…The model employs the FastText word embedding technique to convert tweets into vector representations. These vectorized inputs are then processed by a hybrid model integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with a global attention mechanism. …”
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Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies by Ayad M. Dalloo, Amjad J. Humaidi

“…Efficiency, low-power consumption, and real-time processing in embedded machine learning implementations are critical, particularly for models deployed in environments with large-scale data processing and resource-constrained environments. …”
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Autonomous Maneuvering Decision-Making Algorithm for Unmanned Aerial Vehicles Based on Node Clustering and Deep Deterministic Policy Gradient by Xianyong Jing, Fuzhong Cong, Jichuan Huang, Chunyan Tian, Zikang Su

“…The results show that the NC_DDPG algorithm significantly accelerates the autonomous learning and decision-making process under both balanced and disadvantageous conditions, taking only about 77% of the time required by Vector DDPG. …”
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Seismic Attribute Extraction and Application Based on the Gabor Wavelet Transform by Ran Xiong, Xuri Huang, Liang Guo, Xuan Zou, Haonan Tian

“…Finally, the validity of the extracted seismic attributes is verified by a field data. In this process, the seismic amplitude, spectrum data and Gabor attributes are used as sample data for the support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost) models and deep residual shrinkage network (DRSN) for comparison. …”
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Heart disease prediction using ECG-based lightweight system in IoT based on meta-heuristic approach by Amin Abbaszadeh, Mahdi Bazargani

“…Initially, pre-processing is accomplished using the ECG signals to eliminate noise and improve signal smoothness. …”
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Cloud-Driven Data Analytics for Growing Plants Indoor by Nezha Kharraz, István Szabó

“…Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. …”
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Terahertz Metasurfaces for Polarization Manipulation and Detection: Principles and Emerging Applications by Yongliang Liu, Yifei Xu, Bo Yu, Wenwei Liu, Zhengren Zhang, Hua Cheng, Shuqi Chen

“…Abstract Terahertz frequencies locating between microwave and infrared regions can record and process optical information for next‐generation information technology such as 6G communication. …”
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Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography by Xiaodong Sheng, Tao Fan, Xiaoqi Jin, Jing Jin, Zhixian Chen, Guanqun Zheng, Min Lu, Zongcheng Zhu

“…The experimental results show that the method can accurately extract the blood vessel center line, direction vector and other information in the coronary angiography image without manual intervention, and can be used in the computer-assisted diagnosis and treatment process of clinical cardiovascular disease.…”
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Machine learning-based assessment of flood susceptibility in the Eastern Mediterranean: a case study of Baniyas River basin by Hazem Ghassan Abdo, Sahar Mohammed Richi, Taorui Zeng, Saeed Alqadhi, Pankaj Prasad, Thong Nguyen-Huy, Maged Muteb Alharbi, Javed Mallick

“…In this analysis, the performance of four ensemble machine learning algorithms (ML), that is support vector machine (SVM), random forest (RF), artificial neural network (ANN) and extreme gradient boost (XGBoost), was compared and tested in mapping flood susceptibility in the Eastern Mediterranean region. …”
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Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield by Rahul Kumar, Ashok Kumar, Karamjit Bhatia, Kottakkaran Sooppy Nisar, Siddharth Singh Chouhan, Priti Maratha, Anoop Kumar Tiwari

“…” These models include Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. …”
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Robot-Based Diver Safety Support Scenario and Concept Design for NADIA by Hyungjoo Kang, Gun Rae Cho, Hansol Jin, Min-Gyu Kim, Ji-Hong Li, Seongho Jin, Jungsoo Lee, Jeongtack Min

“…To implement these functions, design elements such as modularization and vector arrangement of propulsion units were derived. …”
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Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH by Yanfeng Peng, Junhang Chen, Yanfei Liu, Junsheng Cheng, Yu Yang, He Kuanfang, Guangbin Wang, Yi Liu

“…ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. …”
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Flexible Baseband-Unit Aggregation Enabled by Reconfigurable Multi-IF Over WDM Fronthaul by Haiyun Xin, Hao He, Kuo Zhang, Syed Baqar Hussain, Weisheng Hu

“…For 4G+ deployment as specified in 3GPP protocol, 100-MHz orthogonal frequency-division multiplexing signal with 64 quadratic-amplitude modulation format after transmission shows that 3.5% error vector magnitude can be achieved.…”
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Construction of Analogy Indicator System and Machine-Learning-Based Optimization of Analogy Methods for Oilfield Development Projects by Muzhen Zhang, Zhanxiang Lei, Chengyun Yan, Baoquan Zeng, Fei Huang, Tailai Qu, Bin Wang, Li Fu

“…Single-indicator and whole-asset analogy experiments are then performed with five standard machine-learning algorithms—support vector machine (SVM), random forest (RF), backpropagation neural network (BP), k-nearest neighbor (KNN), and decision tree (DT). …”
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Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors by Sunnia Ikram, Imran Sarwar Bajwa, Amna Ikram, Isabel de la Torre Diez, Carlos Eduardo Uc Rios, Angel Kuc Castilla

“…The system is equipped with ultrasonic sensor, PIR sensor and a buzzer, with data processing managed by an Arduino Uno microcontroller. …”
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Dynamic Classifier Auditing by Unsupervised Anomaly Detection Methods: An Application in Packaging Industry Predictive Maintenance by Fernando Mateo, Joan Vila-Francés, Emilio Soria-Olivas, Marcelino Martínez-Sober, Juan Gómez-Sanchis, Antonio José Serrano-López

“…Three anomaly detection methods were evaluated: One-Class Support Vector Machine (OCSVM), Minimum Covariance Determinant (MCD), and a majority (hard) voting ensemble of the two. …”
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Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models by Zhenqian Huang, Zhen Huang, Pengtao An, Jun Liu, Chen Gao, Juncai Huang

“…Predicting the deformation of surrounding rock is an important task to ensure the safety of mountain tunnel construction.This study, set against the backdrop of an actual under-construction tunnel, reconstructed the missing surrounding rock monitoring data using a Particle Swarm Optimization-based Least Squares Support Vector Regression model (PSO-LSSVR), and subsequently predicted the tunnel surrounding rock deformation using the constructed Gaussian Process Regression model (PSO-GPR).The research results indicate that the average relative error of the PSO-LSSVR reconstruction model is 1.21 %, lower than the 4.82 % of the LSSVR reconstruction model and the 4.69 % of the BP reconstruction model. …”
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Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs by Yue Zhang, Sheng Quan Xie, Chaoyang Shi, Jun Li, Zhi-Qiang Zhang

“…Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. …”
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Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security by Maoran Xiao, Qi Zhou, Zhen Zhang, Junjie Yin

“…This paper explores the use of deep learning for real-time intrusion detection in power grids with a primary focus on safeguarding the integrity and security of Data Processing Units (DPUs). An evaluation of various machine learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Trees, and Random Forests, is conducted to detect various types of intrusions, including Fault, Injection, Masquerade, Normal, and Replay. …”
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Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach by David Carrascal, Paula Bartolomé, Elisa Rojas, Diego Lopez-Pajares, Nicolas Manso, Javier Diaz-Fuentes

“…A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. …”
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