A Comparative Analysis of Deep Learning Models for Prediction of Microsatellite Instability in Colorectal Cancer by Ziynet Pamuk, Hüseyin Erikçi

“…Colorectal cancer remains one of the most prevalent and fatal malignancies worldwide, underscoring the necessity for early and precise diagnostic approaches to enhance patient prognoses. …”
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Application of Transformer Models for Classification of Chest X-rays by Enoel Arrokho Ernandes

“…Chest X-raying is the most well-known and widespread clinical method of diagnosing pneumonia. …”
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An interpretable wheat yield estimation model using an attention mechanism-based deep learning framework with multiple remotely sensed variables by Mingqi Li, Pengxin Wang, Kevin Tansey, Yue Zhang, Fengwei Guo, Junming Liu, Hongmei Li

“…The attention weights indicated that the most significant variable influencing wheat yield was FPAR, followed by LAI and VTCI. …”
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Bi-directional Pre-trained Network for Single-station Seismic Waveform Analysis by Yuqi CAI, Ziye YU, Weitao WANG, Yanru AN, Lu LI

“…The application of machine learning, particularly deep learning methods, is becoming increasingly widespread in seismology, achieving near-human accuracy in tasks such as phase detection and event classification. However, most neural network models in seismology currently focus on single tasks. …”
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Breath Detection from a Microphone Using Machine Learning by Tomasz Sankowski, Piotr Sulewski, Jan Walczak, Aleksandra Bruska

“…Spectrogram Classification Using Convolutional Neural Networks: This approach involves classifying spectrograms of half-second or quarter-second audio segments using a convolutional neural network adapted for image classification tasks. 3. …”
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A Framework for Breast Cancer Classification with Deep Features and Modified Grey Wolf Optimization by Fathimathul Rajeena P.P, Sara Tehsin

“…Breast cancer is the most common disease in women, with 287,800 new cases and 43,200 deaths in 2022 across United States. …”
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Analysis of Students’ Role Perceptions and their Tendencies in Classroom Education Based on Visual Inspection by Lanlan Jiang

“…A multi example learning student engagement assessment method based on a one-dimensional convolutional neural network is proposed. Based on the conceptual composition of student engagement, head posture, eye gaze, and eye-opening and closing states and the most used facial movement units are used as visual features. …”
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Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data by Bas Peters, Eldad Haber, Keegan Lensink

“…While reversibility saves the major amount of memory used in deep networks by the data, the convolutional kernels can take up most memory if fully invertible networks contain multiple invertible pooling/coarsening layers. …”
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Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN) by C. Selvan, Basha H. Anwar, Soumyalatha Naveen, Shaik Thasleem Bhanu

“…In situations where ambulance resources are limited, a support vector machine (SVM) evaluates patient data to optimize the distribution of available ambulances, ensuring that the most critical patients receive timely medical attention. …”
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Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak, Mustapha Mabrouki

“…The intent is to identify the most favorable model that balances high accuracy with low computational cost.…”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. …”
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Odor prediction of whiskies based on their molecular composition by Satnam Singh, Doris Schicker, Helen Haug, Tilman Sauerwald, Andreas T. Grasskamp

“…Moreover, we use OWSum and a Convolutional Neural Network (CNN) architecture to classify the five most relevant odor attributes of each sample and predict their sensory scores with promising accuracies (up to F1: 0.71, MCC: 0.68, ROCAUC: 0.78). …”
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”Discrimination Against Children” in the Light of Landmark Decisions of the European Court of Human Rights and the Us Supreme Court by Peri Uran Murphy

“…One of the most significant and convoluted issues regarding the international protection of human rights is the right to non-discrimination. …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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Multiclass Incremental Learning for Fault Diagnosis in Induction Motors Using Fine-Tuning with a Memory of Exemplars and Nearest Centroid Classifier by Magdiel Jiménez-Guarneros, Jonas Grande-Barreto, Jose de Jesus Rangel-Magdaleno

“…Early detection of fault events through electromechanical systems operation is one of the most attractive and critical data challenges in modern industry. …”
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Automatic Mushroom Species Classification Model for Foodborne Disease Prevention Based on Vision Transformer by Boyuan Wang

“…Mushroom poisoning is one of the most serious food safety issues worldwide. Motivated by this problem, this study uses an open-source mushroom dataset and employs several data augmentation approaches to decrease the probability of model overfitting. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…Moreover, the research conducts a comparative evaluation of various machine learning models to identify the most effective algorithms for different climatic zones and crop types. …”
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A WiFi RSSI ranking fingerprint positioning system and its application to indoor activities of daily living recognition by Zixiang Ma, Bang Wu, Stefan Poslad

“…WiFi received signal strength indicator seem to be the basis of the most widely used method for indoor positioning systems driven by the growth of deployed WiFi access points, especially within urban areas. …”
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Handwritten Geez Digit Recognition Using Deep Learning by Mukerem Ali Nur, Mesfin Abebe, Rajesh Sharma Rajendran

“…Amharic language is the second most spoken language in the Semitic family after Arabic. …”
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