Unlocking telecommuting patterns before, during, and after the COVID-19 pandemic: An explainable AI-driven study by Adedolapo Ogungbire, Suman Kumar Mitra

“…Shapely Additive Explanations, a model-agnostic approach, explains the best-performing model for each period. …”
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Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm by Hongyan Wang

“…In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated.…”
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Comparative Analysis of Machine Learning Based Approaches for Face Detection and Recognition by Ratnesh Kumar Shukla, Arvind Kumar Tiwari

“…This article discusses a device focused on images that enables users to recognise and detect many face-related features using the webcam. In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. …”
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ATBShellFinder: A Bytecode-Level Webshell Detector Based on Adversarial Training by Yuqin Xie, Yuan Zhang, Daofeng Li, Guoren Xiong

“…These adversarial embeddings are then combined with normal embeddings for model training, significantly improving the robustness and generalization of the model even with limited webshell samples. …”
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VCAFusion: A Framework for Infrared and Low Light Visible Image Fusion Based on Visual Characteristics Adjustment by Jiawen Li, Zhengzhong Huang, Jiapin Peng, Xiaochuan Zhang, Rongzhu Zhang

“…To further improve fusion quality, we develop an edge-preserving multi-scale decomposition model and a saliency-preserving strategy, ensuring seamless integration of perceptual features. …”
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Addressing Hybrid Confounder Issue for Causal Session-Based Recommendation by Quan Li, Xinhua Xu, Jinjun Liu, Guangmin Li

“…Next, user preference for the next item is estimated from the observation sequence, environmental feature and item popularity. Finally, the objective function is constructed by cross entropy and KL scatter, and the model parameters are learned. …”
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Development of an eHealth Intervention Including Self-Management for Reducing Sedentary Time in the Transition to Retirement: Participatory Design Study by Lisa Hultman, Caroline Eklund, Petra von Heideken Wågert, Anne Söderlund, Maria Lindén, Magnus L Elfström

“…Participants expressed that the eHealth intervention would need to first provide the user with knowledge regarding sedentary time, then offer features for measuring sedentary time and for setting goals, and lastly provide support in finding joyful activities to perform in order to avoid being sedentary. …”
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A Comparative Study of Statistical and Machine Learning Methods for Solar Irradiance Forecasting Using the Folsom PLC Dataset by Oscar Trull, Juan Carlos García-Díaz, Angel Peiró-Signes

“…Furthermore, pure time series approach models yield lower performance. However, a hybrid approach in which physical models are integrated with machine learning demonstrates significantly improved accuracy. …”
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Metallographic Spheroidization Rate Classification by Using Deep Learning by Lin Chiu‐Chin, Pei‐Hsuan Chiang, Ke‐Hao Chen, Yu‐Jen Pan, Chung‐Hsien Chen, Wang Yi‐Shun, Jen‐Chun Lee

“…This represents an improvement of 1.42% over the baseline model. Additionally, the YOLOv8‐DFFN model surpasses the performance of the original YOLOv8 algorithm. …”
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Leveraging LSTM and ensemble classifiers for enhanced food waste classification by Khalaf Alsalem

“…The study shows that ensemble models, particularly gradient boosting and SVM, perform well for waste classification tasks. …”
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Combining miRNA concentrations and optimized machine-learning techniques: An effort for the tomato storage quality assessment in the agriculture 4.0 framework by Seyed Mohammad Samadi, Keyvan Asefpour Vakilian, Seyed Mohamad Javidan

“…Also, feature selection results showed that miRNA1917, miRNA172, and miRNA156, as inputs to the optimized RF model could predict the storage temperature, storage period, and mechanical loading on the fruits with R2 values of 0.94, 0.93, and 0.93, respectively. …”
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Underwater Visual Multi-Target Tracking Algorithm Integrating Re-parameterization and Attention Mechanism by Junyi LI, Mingle HE, Chang LIU, Yong XU

“…This algorithm effectively enhanced the network’s multi-scale feature extraction capability and improved the detection accuracy of the model by integrating a structurally re-parameterized multi-scale feature extraction convolutional structure(DBB-RFAConv) and the AMSCE-attention mechanism. …”
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Identification and Analysis of Senescence-Related Genes in Head and Neck Squamous Cell Carcinoma by a Comprehensive Bioinformatics Approach by Lin Deng, Jinglin Mi, Xiaolan Ruan, Guozhen Zhang, Yufei Pan, Rensheng Wang

“…We further improved the prognostic model and survival prediction by combining CSRS.Score with clinicopathological features using a decision tree model, which had high predictive accuracy and survival prediction. …”
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New Approach for Mapping Land Cover from Archive Grayscale Satellite Imagery by Mohamed Rabii Simou, Mohamed Maanan, Safia Loulad, Mehdi Maanan, Hassan Rhinane

“…The best-performing segmentation model was then used to segment the colorized declassified images into built-up areas. …”
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Role of regional and global datasets in the simulation of intense tropical cyclones over Bay of Bengal region in a convection‐permitting scale by Thatiparthi Koteshwaramma, Kuvar Satya Singh

“…Model predictions highlight that the regional dataset IMDAA performs better in predicting rainfall magnitude compared with the global dataset due to the added assimilation of numerous local observations. …”
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Growth Differentiation Factor 15 Predicts Cardiovascular Events in Peripheral Artery Disease by Ben Li, Farah Shaikh, Houssam Younes, Batool Abuhalimeh, Abdelrahman Zamzam, Rawand Abdin, Mohammad Qadura

“…Secondary model performance metrics included precision, recall, positive likelihood ratio (LR+), and negative likelihood ratio (LR-). …”
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AS-YOLO: Enhanced YOLO Using Ghost Bottleneck and Global Attention Mechanism for Apple Stem Segmentation by Na Rae Baek, Yeongwook Lee, Dong-hee Noh, Hea-Min Lee, Se Woon Cho

“…The proposed model reduces the number of parameters and enhances the computational efficiency using the ghost bottleneck while improving feature extraction capabilities using the global attention mechanism. …”
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Fatigue Load Prediction of Wind Turbine Drive Train Based on CNN-BiLSTM by Xiaodong WANG, Qing LI, Deyi FU, Yingming LIU, Ruojin WANG

“…We then compare the model's predicted data with actual data, employing relevant evaluation metrics to assess the predictive performance of the model, thereby validating its effectiveness. …”
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Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier by Ali Saeed Almuflih, Ilyos Abdullayev, Sergey Bakhvalov, Rustem Shichiyakh, Bibhuti Bhusan Dash, K. B. V. Brahma Rao, Kritika Bansal

“…The experimental validation of the BSODL-ZDADC technique is verified by utilizing the ToN-IoT dataset. The performance validation of the BSODL-ZDADC technique portrayed a superior accuracy value of 98.28% over other models.…”
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Fire and Smoke Detection in Complex Environments by Furkat Safarov, Shakhnoza Muksimova, Misirov Kamoliddin, Young Im Cho

“…We evaluate the performance of the proposed model using a comprehensive Fire and Smoke Detection Dataset, which includes diverse real-world scenarios. …”
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