Artificial intelligence in gastric cancer diagnosis, treatment and prognostic prediction: current application and future perspective by PENG Dongge, WAN Ziye, LU Ning

“…Gastric cancer remains one of the most prevalent and lethal malignancies worldwide, characterized by an insidious onset, challenges in early detection, and a poor prognosis in advanced stages. …”
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Automatic construction of global cloud sample database based on Landsat imagery by Tao He, Guihua Huang, Lei Zhang, Daiqiang Wu, Yichuan Ma

“…This study provides global cloud samples with different cloud characteristics and covering different land covers for cloud detection models, which increases generalization ability of models and improves the accuracy and efficiency of processing massive medium to fine resolution satellite data in the big data era.…”
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Overview and Comparison of Deep Neural Networks for Wildlife Recognition Using Infrared Images by Peter Sykora, Patrik Kamencay, Roberta Hlavata, Robert Hudec

“…To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. …”
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Workload Forecasting Methods in Cloud Environments: An Overview by Samah Aziz, Manar Kashmoola

“…We explore more sophisticated approaches like algorithms for deep learning (DL) and machine learning (ML) in addition to more conventional approaches like analysis of time series and models of regression. …”
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Failure Management Overview in Optical Networks by Sergio Cruzes

“…The key ML techniques discussed include network kriging (NK) for performance estimation and failure localization, support vector machine (SVM) for classification tasks, convolutional neural networks (CNNs) for signal analysis and soft failure identification, and generative adversarial networks (GANs) for synthetic data generation and soft failure detection. It also explores the application of artificial neural networks (ANNs), autoencoders (AEs), Gaussian process (GP), long short-term memory (LSTM), and gated recurrent units (GRUs) in optical networks. …”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. …”
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Hybrid AE and Bi-LSTM-Aided Sparse Multipath Channel Estimation in OFDM Systems by Vijayakumar Kondepogu, Budhaditya Bhattacharyya

“…Additionally, a hybrid deep learning HA-Bi-LSTM model is developed by combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Auto-Encoder (AE) to enhance data communication performance. …”
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A SAR-based flood mapping approach: application of SAR-SIFT registration and modified DeepLabV3 segmentation in flood hazard assessment by Zhuoyu Zhang, Jiaqi Xiong, Xiang Li, Yu Li, Junrong Liu

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Decoding pixels: A modular software prototype for cognitive image-based diagnostics of PV plants by Tsanakas John Ioannis A., Marechal Philippe

“…In this paper, we introduce a software prototype, evolved from an innovative diagnostics framework researched and developed by CEA-INES over the last years, which integrates aIRT imagery with deep learning-based algorithms and physical/electrical modeling. …”
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The EyCon Dataset: A Visual Corpus of Early Conflict Photography by Marina Giardinetti, Daniel Foliard, Julien Schuh, Mohamed-Salim Aissi

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Localization of Capsule Endoscope in Alimentary Tract by Computer-Aided Analysis of Endoscopic Images by Ruiyao Zhang, Boyuan Peng, Yiyang Liu, Xinkai Liu, Jie Huang, Kohei Suzuki, Yuki Nakajima, Daiki Nemoto, Kazutomo Togashi, Xin Zhu

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Accurately assessing congenital heart disease using artificial intelligence by Khalil Khan, Farhan Ullah, Ikram Syed, Hashim Ali

“…These ML-based models can help healthcare professionals identify high-risk infants and ensure timely and appropriate care. In addition, ML algorithms excel at detecting and analyzing complex patterns that can be overlooked by human clinicians, thereby enhancing diagnostic accuracy. …”
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A predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis by Behnaz Motamedi, Balázs Villányi

“…Effective disease management necessitates the accurate and timely prediction of lung cancer and diabetes. Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. …”
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Small Target Ewe Behavior Recognition Based on ELFN-YOLO by Jianglin Wu, Shufeng Li, Baoqin Wen, Jing Nie, Na Liu, Honglei Cen, Jingbin Li, Shuangyin Liu

“…The obtained results indicate that the proposed approach outperforms existing methods in scenarios involving multi-scale detection of small objects. The proposed method is of significant importance for strengthening animal welfare and ewe management, and it provides valuable data support for subsequent tracking algorithms to monitor the activity status of ewes.…”
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Automated Cough Analysis with Convolutional Recurrent Neural Network by Yiping Wang, Mustafaa Wahab, Tianqi Hong, Kyle Molinari, Gail M. Gauvreau, Ruth P. Cusack, Zhen Gao, Imran Satia, Qiyin Fang

“…In this study, we developed a machine learning model for the detection and classification of cough sounds. …”
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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…When a defect is detected, it can be repaired (detect-repair). Moreover, the gathered data from defect detection can be used in two ways: to prevent defect occurrence in the future (detect-prevent) and to design algorithms for predicting when a defect may occur in the future, hence, to prevent defects before they arise (predict-prevent). …”
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Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review by Anastasios Anastasiadis, Antonios Koudonas, Georgios Langas, Stavros Tsiakaras, Dimitrios Memmos, Ioannis Mykoniatis, Evangelos N. Symeonidis, Dimitrios Tsiptsios, Eliophotos Savvides, Ioannis Vakalopoulos, Georgios Dimitriadis, Jean de la Rosette

“…Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. …”
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Recurrent academic path recommendation model for engineering students using MBTI indicators and optimization enabled recurrent neural network by Anupama V, Sudheep Elayidom M

“…At last, an adaptive recommendation of the engineering department is performed using DRNN, which is trained based on the Magnetic Invasive Weed Optimization (MIWO) algorithm. On the other hand, MBTI personality type categorization is done, wherein the correlation of courses with MBTI outcome is detected using MIWO-based DRNN. …”
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An Energy-Efficient Battery Monitoring and Logging System for Agricultural Robotics with CAN Bus Integration by Soosaar Guido, Lillerand Tormi

“…A key innovation is its adaptive data acquisition algorithm, which adjusts polling frequency based on battery activity and temperature thresholds, significantly reducing power consumption without compromising responsiveness. …”
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Hybrid-CID: Securing IoT with Mongoose Optimization by S. Merlin Sheeba, R. S. Shaji

“…After preprocessing, the Hybrid-CID framework develops a hybrid optimization algorithm to identify the intrusions from the traffic data which ensures data privacy by maintaining the reliability and integrity of IoT deployments. …”
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