A Fault Identification Method for Electric Submersible Pumps Based on DAE-SVM by Peihao Yang, Jiarui Chen, Hairong Zhang, Sheng Li

“…Firstly, the ESP production data were processed and fault-related features were screened using the random forest (RF) algorithm. …”
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Prototyping a graph-based approach for efficient vehicle tracking in a cluster controlled CCTV surveillance systems by Apurva NARAYAN, Charumathi PARIVALLAL, Olirva MAHESWARAN, Sarath CHANDRAN KALATHILPARAMBIL RAMACHANDRAN

“…Utilizing algorithms like YOLO for object detection, the system ensures an accurate and efficient monitoring across multiple surveillance nodes. …”
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AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study by Mihaela Hnatiuc, Domnica Alpetri, Sorin-Robertino Sintea, Bogdan Hnatiuc, Gabriel Margarit Raicu, Mirel Paun, Ionica Dina

“…This study explores the application of the Artificial Intelligence of Things (AIoT) in viticulture for the early detection of grapevine diseases. By integrating Internet of Things (IoT) sensors with machine learning algorithms, the system is designed to detect potential grapevine pathogens in real time. …”
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Analisis Kinerja Model Deteksi Objek Yolo, Ssd, dan Faster R-Cnn pada Citra Penglihatan Malam untuk Pengenalan Tindak Kejahatan by Hendriyawan Achmad, Arif Pramudwiatmoko, Muhammad Satrio Gumilang, Bahrul Al Karim, Hadi Wiyono

“…In an effort to address this pressing concern, the present research advocates for the deployment of advanced security technologies, specifically emphasizing the utilization of night vision surveillance cameras in conjunction with machine learning algorithms to proficiently identify klitih-related offenses, particularly during nocturnal hours. …”
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Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives by Kainat Iqbal, Bayan Alabdullah, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Jeongmin Park

“…Our evaluation demonstrates SVM as the best-performing machine learning algorithm. In particular, SVM with Mel Spectrogram as input data, achieved an accuracy of around 97%. …”
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CircuitHD: Brain-Inspired Hyperdimensional Computing for Circuit Recognition by Paul R. Genssler, Lilas Alrahis, Ozgur Sinanoglu, Hussam Amrouch

“…This work is the first to propose HDC for optimized circuit encoding and learning. One advantage of HDC over conventional deep learning algorithms is that it does not require extensive training to encode a gate-level netlist into a hypervector. …”
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From Genomics to AI: Revolutionizing Precision Medicine in Oncology by Giulia Calvino, Juliette Farro, Stefania Zampatti, Cristina Peconi, Domenica Megalizzi, Giulia Trastulli, Sarah Andreucci, Raffaella Cascella, Claudia Strafella, Carlo Caltagirone, Federico Grifalchi, Emiliano Giardina

“…However, challenges remain regarding data standardization, interpretability, bias in AI algorithms, and ethical considerations. …”
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Classification of patients with lithium-treated bipolar disorder based on gene expression: Dirichlet Bayesian network model by Nader Salari, Sahar Souri Pilangorgi, Afshin Almasi, Soodeh Shahsavari, Andrew J. Fournier

“…Essential variables were selected using partial least squares regression to analyze and classify the data. The plaid algorithm was employed to identify identical patterns and biclusters within the gene expression data. …”
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Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. …”
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Radiomics in the differential diagnosis of glioblastoma under the primary neurooncoimaging conditions by Nikita E. Maslov, Daria A. Valenkova, Alexander M. Sinitсa, Gennadiy E. Trufanov, Vladimir M. Moiseenko, Alexander Yu. Efimtsev, Vera V. Chernobrivtseva

“…We conducted a retrospective comparative statistical analysis of radiomic features extracted from 46 conventional brain MR images of the patients with adult-type diffuse gliomas and identified IDH mutation status using the Random Forest algorithm of machine learning in combination with various preprocessing methods of the source imaging data and a semi-automated LevelTracing tool used for segmentation of the regions of interest (ROI). …”
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Artificial intelligence in hospital infection prevention: an integrative review by Rabie Adel El Arab, Zainab Almoosa, May Alkhunaizi, May Alkhunaizi, Fuad H. Abuadas, Joel Somerville, Joel Somerville

“…Comparative data suggest that while both machine learning and deep learning approaches perform well, some deep learning models may offer slight advantages in complex data environments. …”
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APLOSE: A web-based annotation platform for underwater passive acoustic monitoring by Gabriel Dubus, Maëlle Torterotot, Julie Béesau, Mathieu Dupont, Anatole Gros-Martial, Mathilde Michel, Elodie Morin, Paul Nguyen Hong Duc, Pierre-Yves Raumer, Olivier Adam, Flore Samaran, Dorian Cazau

“…Emerging detection and classification algorithms based on deep learning models require manageable large-scale manual annotations of ground truth data. …”
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Revolutionising English language education:empowering teachers with BERT-LSTM-driven pedagogical tools by Sheik Hameed Nagoor Gani, Vijayakumar Selvaraj, Sahidul Islam Md, Sugadev Thalapathy, Mohamed Jalaludeen Abdulkadhar, Kanmani Kalimuthu

“…The proposed approach leverages big data and advanced machine learning algorithms by incorporating millions of student exercise attempts from a dataset. …”
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Aircraft Position Estimation Using Deep Convolutional Neural Networks for Low SNR (Signal-to-Noise Ratio) Values by Przemyslaw Mazurek, Wojciech Chlewicki

“…The proposed solution uses the processed image values for the tracking process with the raw data using the Track-Before-Detect method.…”
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Quantitatively defined stromal B cell aggregates are associated with response to checkpoint inhibitors in unresectable melanoma by James W. Smithy, Xiyu Peng, Fiona D. Ehrich, Andrea P. Moy, Mohammad Yosofvand, Colleen Maher, Nathaniel Aleynick, Rami Vanguri, Mingqiang Zhuang, Jasme Lee, MaryLena Bleile, Yanyun Li, Michael A. Postow, Katherine S. Panageas, Travis J. Hollmann, Margaret K. Callahan, Ronglai Shen

“…In pre-treatment melanoma samples from 50 patients treated with immune checkpoint inhibitors (ICIs), a higher stromal B cell percentage is associated with the clinical benefit of ICI therapy. The automatic detection of B cell aggregates with DBSCAN, a novel application of a computer-aided machine learning algorithm, demonstrates the potential for enhanced accuracy compared to pathologist assessment of lymphoid aggregates. …”
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Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network. II. Application to Next-generation Wide-field Surveys by Sangjun Cha, M. James Jee, Sungwook E. Hong, Sangnam Park, Dongsu Bak, Taehwan Kim

“…Rubin Observatory, we generated training data sets of mock shear catalogs with a source density of 33 arcmin ^−2 from cosmological simulation ray-tracing data. …”
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Influence of Aerobic Exercise Load Intensity on Children’s Mental Health by Sihong Zhao

“…This technique includes several commonly used and improved video analysis algorithms. The use of moving target and tracking technology and algorithms can completely extract moving targets, eliminate the phenomenon of void and nothingness, and improve data acquisition and analysis capabilities. …”
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Improved Key Microbial Biomarker Discovery Using Ensemble Statistical Methods by Walter Pirovano, Yashjit Gangopadhyay, Mirna Lilian Baak, Christiaan Arie de Leeuw, Radhika Bongoni, Eline Suzanne Klaassens

“…While differential abundance analysis and machine-learning techniques are widely used to perform such analyses, selecting the best method is not trivial due to the complexity and specific characteristics of both the data and the algorithms. …”
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Impact of bridging the gap between Artificial Intelligence and nanomedicine in healthcare by Divyam Mishra, Bhavishya Chaturvedi, Vishal Soni, Dhairya Valecha, Megha Goel, Jamilur R. Ansari

“…We will also assess the long-term implications of lipid nanoparticles in drug delivery applications. Machine Learning algorithms are employed to create data-driven adaptive nanomaterials and paradigms, further advancing the field. …”
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Leveraging Advanced Technologies for Real-Time Plant Disease Monitoring by Mishra Archana, Afzal Md

“…The basic datasets are plant images and environmental data, and they have already shown the ability to provide useful information when analyzed with ML Algorithms such as CNN and some ensemble methods. …”
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