Efficient text-to-video retrieval via multi-modal multi-tagger derived pre-screening by Yingjia Xu, Mengxia Wu, Zixin Guo, Min Cao, Mang Ye, Jorma Laaksonen

“…We predict discrete semantic tags for video and text with our proposed multi-modal multi-tagger module, and then leverage an inverted index for space-efficient and fast tag matching to filter out irrelevant videos. …”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…Structured workloads typically exhibit predictable patterns of data access and usage, making them susceptible to displaying anomalies that may indicate unauthorized access, data manipulation, or other security breaches. …”
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Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments by Chaoyang CHEN, Yute TANG, Yi HUANG, Zhiqun LIU

“…Moreover, dynamic obstacle trajectories were modeled, and the Singer model, combined with the Kalman filter algorithm, was used to precisely predict obstacle states, thereby enhancing the dynamic obstacle avoidance capabilities of AUVs. …”
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Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks by Haifeng Jiang, Yanjing Sun, Renke Sun, Hongli Xu

“…We adopt the social welfare function to predict inequality of residual energy of neighbors after selecting different next hop nodes. …”
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Artificial intelligence model in the cognitive and learning activities of university subjects by N. Abishev, R. Ramazanov, M. Abaideldanova, K. Chesnokova, A. Baizhumayeva

“…The authors design this model using algorithms–sets of rules that enable programs to make decisions, recognize patterns, and generate predictions based on input data relevant to the learning and cognitive processes of university subjects. …”
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A Novel Method for ECG-Free Heart Sound Segmentation in Patients with Severe Aortic Valve Disease by Elza Abdessater, Paniz Balali, Jimmy Pawlowski, Jérémy Rabineau, Cyril Tordeur, Vitalie Faoro, Philippe van de Borne, Amin Hossein

“…The proposed approach outperformed the existing methods, achieving a higher sensitivity and positive predictive value for S2, especially in the presence of severe heart murmurs. …”
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Application of artificial neural networks in the drilling processes: Can equivalent circulation density be estimated prior to drilling? by Husam H. Alkinani, Abo Taleb T. Al-Hameedi, Shari Dunn-Norman, David Lian

“…The goal of this work was to predict ECD prior to drilling by using artificial neural network (ANN). …”
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Estimating canopy height in tropical forests: Integrating airborne LiDAR and multi-spectral optical data with machine learning by Brianna J. Pickstone, Hugh A. Graham, Andrew M. Cunliffe

“…This study aims to compare the performance of three machine learning algorithms (Multiple Linear Regression (MLR), Random Forest (RF), and Convolutional Neural Networks (CNN)) when using PlanetScope and Sentinel-2 imagery to improve the accuracy of height predictions. …”
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Implementation of a neural network model in the Statistica 12 for mudflow frequency forecasting by B. A. Ashabokov, A. A. Tashilova, L. A. Kesheva, N. V. Teunova

“…It follows from the linear trend equation that, on average, over the entire period, including the predicted one, the number of mudflows tends to grow slightly by 0.3/10 years. …”
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Unsupervised machine learning identifies biomarkers of disease progression in post-kala-azar dermal leishmaniasis in Sudan. by Ana Torres, Brima Musa Younis, Samuel Tesema, Jose Carlos Solana, Javier Moreno, Antonio J Martín-Galiano, Ahmed Mudawi Musa, Fabiana Alves, Eugenia Carrillo

“…Today, basic knowledge of this neglected disease and how to predict its progression remain largely unknown.<h4>Methods and findings</h4>This study addresses the use of several biochemical, haematological and immunological variables, independently or through unsupervised machine learning (ML), to predict PKDL progression risk. …”
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PDP1 related ferroptosis risk signature indicates distinct immune microenvironment and prognosis of breast cancer patients by Yufeng Wang, Huifen Dang, Gongjian Zhu, Yingxia Tian

“…The model remained significant in multivariate Cox regression analysis, indicating that it could independently predict the survival of BC patients. ACSL1, BNIP3, and EMC2 were downregulated after knockdown of PDP1.ConclusionRiskScore model constructed by PDP1-ferroptosis-related genes ACSL1, BNIP3, and EMC2 is able to help predict the prognosis of BC patients.…”
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Analysis of reservoir rock permeability changes due to solid precipitation during waterflooding using artificial neural network by Azizollah Khormali, Soroush Ahmadi, Aleksandr Nikolaevich Aleksandrov

“…A comparison of the predicted values of rock permeability under scaling conditions with experimental data showed that the proposed ANN model makes it possible to predict formation damage due to scaling with high accuracy. …”
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Komparasi Algoritma Support Vector Machines dengan Algoritma Artificial Neural Network untuk Memprediksi Nilai Persetujuan Kredit Modal Kerja yang Diberikan Bank Umum by Abu Sopian, Agus Wiyatno, Albert Riyandi

“…In this research will conducted working capital credit value approval prediction will be provided by commercial bank using support vector machine algorithm that is compared with artificial neutral network algorithm. …”
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On energy-balanced backpressure routing mechanisms for stochastic energy harvesting wireless sensor networks by Zheng Liu, Xinyu Yang, Peng Zhao, Wei Yu

“…Existing research efforts focus on the energy management based on the assumption that the energy harvesting process is predictable. Unfortunately, such an assumption is not practicable in real-world energy harvesting systems. …”
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AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks by Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer, Michael Levin

“…Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. …”
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Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability. by Ritam Dutta, Lalit Kumar Sharma, Bheem Dutt Joshi, Vineet Kumar, Amira Sharief, Saurav Bhattacharjee, Mukesh Thakur, Dhriti Banerjee, Rajappa Babu

“…The Random Forest approach outperformed other algorithms in supervised classification techniques. …”
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The Role of Artificial Intelligence in Aviation Construction Projects in the United Arab Emirates: Insights from Construction Professionals by Mariam Abdalla Alketbi, Fikri Dweiri, Doraid Dalalah

“…The majority agreed that AI has the potential to revolutionize project management processes, improving decision-making, and efficiency. AI tools can predict delays, optimize workflows, and enhance safety through real-time data analytics and machine learning algorithms, reducing risks and human error. …”
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Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach by Imad Khan, M. Waleed Ahmed Khan

“…Backpropagation neural networks, one of the supervised learning algorithms, is commonly used to train data networks by optimizing the error between actual and predicted values. …”
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Enhance differential privacy mechanisms for clinical data analysis using CNNs and reinforcement learning by Rakesh Batchala, Priyank Jain, Manasi Gyanchandani, Sanyam Shukla, Rajesh Wadhvani

“…The primary emphasis is on predicting and optimizing ventilation and sedation strategies for patients in Intensive Care Units. …”
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Real-Time Typical Urodynamic Signal Recognition System Using Deep Learning by Xin Liu, Ping Zhong, Di Chen, Limin Liao

“…This resulted in a total of 2,655 images to train, validate and test the DL algorithm to predict the urdynamic signals. Results Yolov5l had the best detection performance and the highest comprehensive index score (F1, 0.81; mean average precision, 0.83). …”
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