A Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems by Kazushi Adachi, Samir Khan, Shinichi Nakasuka, Kohji Tominaga, Seiji Tsutsumi, Yu Daimon, Taiichi Nagata

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Learning to Learn Sequential Network Attacks Using Hidden Markov Models by Timothy Chadza, Konstantinos G. Kyriakopoulos, Sangarapillai Lambotharan

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Modulation Assessment With IQD Correction for Two-Way Sequential AAF Relaying Transmissions by Hala Mostafa, Mohamed Marey, Khaled Mohamad Almustafa, Juliano Katrib

“…In this paper, we create an original maximum-likelihood (ML) solution to the MA challenge in the circumstance of these discrepancies for amplify-and-forward (AAF) two-way sequential relaying transmissions (TWRT). …”
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A multimodal approach for ADHD with coexisting ASD detection for children by Jungpil Shin, Sota Konnai, Md. Maniruzzaman, Yoichi Tomioka, Yong Seok Hwang, Akiko Megumi, Akira Yasumura

“…The potentiality of these features was evaluated using Sequential Forward Floating Selection (SFFS)-based algorithm and support vector machine (SVM) was employed to evaluate the performance of ZL and PL tasks. …”
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Video inter-frame tampering detection algorithm fusing multiple features by Hui XIAO, Bin WENG, Tianqiang HUANG, Han PU, Zehui HUANG

“…Traditional passive forensics of video inter-frame tampering often relies on single feature.Each of these features is usually suitable for certain types of videos,while has low detection accuracy for other videos.To combine the advantages of these features,a video inter-frame tampering detection algorithm that could fuse multi-features was proposed.The algorithm firstly classified the input video into one group based on its space information and time information values.Then it calculated the VQA features that represented the video inter-frame continuity.These features were sorted by the SVM-RFE feature recursive elimination algorithm.Finally,the sorted features were filtered and fused by the sequential forward selection algorithm and Adaboost binary classifier.Experimental results show that the proposed algorithm could achieve higher tampering detection accuracy.…”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…ObjectivePedestrian detection is a crucial task in computer vision, especially in applications like autonomous driving, robot navigation, and intelligent surveillance. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework by Muhammad Ali Aslam, Khairullah Khan, Wahab Khan, Sajid Ullah Khan, Abdullah Albanyan, Shabbab Ali Algamdi

“…The BiLSTM network sequentially processes the input, leveraging both forward and backward contextual information to encode the complex syntactic and semantic patterns inherent in Urdu text. …”
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Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning by Jungpil Shin, Abu Saleh Musa Miah, Koki Hirooka, Md. Al Mehedi Hasan, Md. Maniruzzaman

“…To further optimize the feature set, we applied the Sequential Forward Floating Selection method to select the most relevant features, reducing dimensionality and computational complexity. …”
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Towards precision diagnosis: a novel hybrid DC-CAD model for lung disease detection leveraging multi-scale capsule networks and temporal dynamics by Esther Stacy E. B. Aggrey, Qin Zhen, Seth Larweh Kodjiku, Linda Delali Fiasam, Collins Sey, Chiagoziem C. Ukwuoma, Evans Aidoo, Emmanuel Osei-Mensah

“…Abstract The early detection of lung diseases, including cancer, is essential for improving patient outcomes. …”
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CNN–Transformer Hybrid Architecture for Underwater Sonar Image Segmentation by Juan Lei, Huigang Wang, Zelin Lei, Jiayuan Li, Shaowei Rong

“…The salient object detection (SOD) of forward-looking sonar images plays a crucial role in underwater detection and rescue tasks. …”
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An IMU-Based Machine Learning System for Container Collision Position Identification by Xin Zhang, Zihan Song, Do-Myung Park, Byung-Kwon Park

“…This study leverages data from an Inertial Measurement Unit sensor and evaluates combinations of machine learning models and feature selection methods to identify the optimal approach for collision position detection. Five machine learning models (decision tree, k-nearest neighbors, support vector machine, random forest, and extreme gradient boosting) and five feature selection methods (Pearson’s correlation coefficient, mutual information, sequential forward selection, sequential backward selection, and extra trees) were assessed using three performance metrics: accuracy, execution time, and CPU utilization. …”
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Self-propelled Subsurface Radar Detector of Low-Depth Objects by I. Yu. Malevich, A. S. Lopatchenko, T. V. Shukevich

“…The device is made on a four-wheeled automated platform with an adjustable console on which the antenna unit moves. With the forward movement of the platform, a sequential radar survey of the upper soil layer is performed, the results of which in the form of a surface projection of the normalized power of depth portraits are displayed on the monitor screen. …”
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DAT: Deep Learning-Based Acceleration-Aware Trajectory Forecasting by Ali Asghar Sharifi, Ali Zoljodi, Masoud Daneshtalab

“…DAT is an end-to-end model that processes sequential sensor data to detect objects and forecasts their future trajectories at each time step. …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy. …”
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Music audio emotion regression using the fusion of convolutional neural networks and bidirectional long short-term memory models by Yi Qiu, Yu Lin, Yun Lin

“…The model uses CNNs to detect temporal patterns and BiLSTMs to interpret sequences in both forward and backward directions, enhancing its ability to capture the complex structure of musical data. …”
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Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs. by Josie Hughes, Xi Huo, Lindsey Falk, Amy Hurford, Kunquan Lan, Bryan Coburn, Andrew Morris, Jianhong Wu

“…The clinical significance of small changes in outcomes such as infection prevalence and death may exceed more easily detectable changes in drug use and resistance. Integrating harms and benefits into ranked outcomes for each patient may provide a way forward in the analysis of these tradeoffs. …”
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Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…A cost-effective RGB camera, integrated with a microcontroller, captured images from the top of the seedlings over a two-week period, from which sequential forward floating selection (SFFS) and correlation matrices were used to streamline feature extraction. …”
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iDILI-MT: identifying drug-induced liver injury compounds with a multi-head Transformer by Wanrong Zheng, Fobao Lai

“…We present iDILI-MT (identifying drug-induced liver injury compounds with a multi-head Transformer), a self-contained computational framework that integrates a feed-forward network for sequential feature extraction, a multi-head Transformer encoder for contextual representation learning, and a squeeze-and-excitation attention module for channel-wise feature recalibration. …”
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Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study by Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, Narges Armanfard, Howard Bergman, Roland Grad, Machelle Wilchesky, Vladimir Khanassov, Isabelle Vedel, Samira Abbasgholizadeh Rahimi

“…The most accurate classification model, which combined sequential forward selection for feature selection, principal component analysis for dimensionality reduction, and a decision tree for classification, achieved an accuracy of 87.5%, sensitivity of 90%, and precision of 88.3%, effectively distinguishing individuals with and those without depression. …”
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