Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

“…Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. …”
Get full text

A hybrid attack detection strategy for cybersecurity using moth elephant herding optimisation‐based stacked autoencoder by Abdullah Shawan Alotaibi

“…Finally, the attack detection is performed using the stacked autoencoder classifier, which is trained using the proposed MEHO algorithm. The MEHO algorithm is developed by integrating the Moth search (MS) algorithm and the Elephant Herding Optimisation (EHO). …”
Get full text

Construction of a Model for the Cross-Domain Opinion Word Extraction by N. V. Loukachevitch, I. I. Chetviorkin

“…The extraction model was trained in the movie domain and then applied to four other domains. …”
Get full text

The incremental value of tuberculosis detecting African giant pouched rats over Smear microscopy and Xpert MTB/RIF for Tanzanian TB detection. by Tefera B Agizew, Joseph Soka, Stephen Mwimanzi, Cynthia D Fast, Gilbert Mwesiga, Nashon Edward, Marygiven Stephen, Rehema Kondo, Christophe Cox, Negussie Beyene

“…Sputum-smear microscopy (smear) is being replaced by Xpert MTB/RIF (Xpert)-based diagnostic algorithms in many countries. We evaluated the incremental values of rat-based case detection over smear and Xpert.…”
Get full text

ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN by Tawfikur Rahman, Rasel Ahommed, Nibedita Deb, Utpal Kanti Das, Md. Moniruzzaman, Md. Alamgir Bhuiyan, Farzana Sultana, Md. Kamruzzaman Kausar

“…The integrated CWT and DCNN enable simultaneous classification of multiple ECG abnormalities alongside normal signals.Material and Methods: This analytical observational research employed CWT to generate spectrograms from 1D ECG signals, as input to a DCNN trained on diverse datasets. The model is evaluated using performance metrics, such as precision, specificity, recall, overall accuracy, and F1-score.Results: The proposed algorithm demonstrates remarkable performance metrics with a precision of 100% for normal signals, an average specificity of 100%, an average recall of 97.65%, an average overall accuracy of 98.67%, and an average F1-score of 98.81%. …”
Get full text

Generalizing location-centric variations to enhance contactless human activity recognition by Fawad Khan, Syed Yaseen Shah, Jawad Ahmad, Alanoud Al Mazroa, Adnan Zahid, Muhammed Ilyas, Qammer Hussain Abbasi, Syed Aziz Shah

“…To address this challenge, in this study, we present a novel federated learning (FL) algorithm designed to train a robust global model from local datasets in different localizations. …”
Get full text

Automated Vertebral Bone Quality Determination from T1-Weighted Lumbar Spine MRI Data Using a Hybrid Convolutional Neural Network–Transformer Neural Network by Kristian Stojšić, Dina Miletić Rigo, Slaven Jurković

“…The performance of the trained model was evaluated using the dice similarity coefficient (DSC), accuracy, precision, recall and intersection-over-union (IoU) metrics. …”
Get full text

A Novel Approach of Optimal Signal Streaming Analysis Implicated Supervised Feedforward Neural Networks by Farhan Ali, He Yigang

“…The dataset consists of artificially generated radio waves to train signals through neural networks (NNs) and machine learning algorithms to detect errors properly. …”
Get full text

Driver drowsiness shield (DDSH): a real-time driver drowsiness detection system by Archita Bhanja, Dibyajyoti Parhi, Dipankar Gajendra, Kreetish Sinha, Arup Kumar Sahoo

“…Using a balanced dataset, the model was trained to distinguish between open and closed eyes accurately. …”
Get full text

Migraine triggers, phases, and classification using machine learning models by Anusha Reddy, Ajit Reddy

“…Models based on these algorithms are then trained using the dataset, which includes a compilation of the types of migraine experienced by various patients. …”
Get full text

Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT Reconstruction by Jayroop Ramesh, Donthi Sankalpa, Rohan Mitra, Salam Dhou

“…Digital phantom and clinical datasets are used to evaluate the performance of the models. <italic>Results:</italic> The results show that the Real-Time Intermediate Flow Estimation (RIFE) algorithm outperforms the others in terms of the Structural Similarity Index Method (SSIM): 0.986 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.010, Peak Signal to Noise Ratio (PSNR): 44.13 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 2.76, and Mean Square Error (MSE): 18.86 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 206.90 across all datasets. …”
Get full text

Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy by Georgina Company-Se, Virginia Pajares, Albert Rafecas-Codern, Pere J. Riu, Javier Rosell-Ferrer, Ramon Bragós, Lexa Nescolarde

“…All the frequencies used to train and test the algorithms obtained high significant differences between neoplasm and the other types of tissues (P < 0.001). …”
Get full text

CO2 Gas Layer Recognition Method Based on Long Short-Term Memory Network by HE Lina, WU Wensheng, WANG Xiannan, ZHANG Wei, ZHANG Chuanju, SONG Xiaoyu

“…Due to the development of deep CO2 gas layer in Enping sag, the Pearl River Mouth basin, traditional logging methods cannot accurately evaluate reservoir fluids. CO2 gas layer recognition model based on Long Short-Term Memory Network (LSTM) is constructed, and CO2 sensitive logging parameters are optimized through m × 2 regularized cross validation to train the model. …”
Get full text

A Preliminary Study on 2D Convolutional Neural Network-Based Discontinuous Rail Position Classification for Detection on Rail Breaks Using Distributed Acoustic Sensing Data by Hye-Yeun Chun, Jungtai Kim, Dongkue Kim, Ilmu Byun, Kyeongjun Ko

“…In this research, as a preliminary study on rail break detection system, a deep learning-based discontinuous rail position classification method, which is using vibration data obtained from distributed acoustic sensing (DAS) system during train operation, is proposed. To analyze the vibration data, a preprocessing algorithm for determining train occupancy is applied first. …”
Get full text

Hand segmentation pipeline from depth map: an integrated approach of histogram threshold selection and shallow CNN classification by Zhengze Xu, Wenjun Zhang

“…We found that MINIMUM, MEAN and MEDIAN are effective ways to separate objects and the threshold in the valley between two maxima similar to MINIMUM algorithm with a minimum error. Then, each segmentation proposal is evaluated by a 3-layers shallow convolutional neural network (CNN) which is trained as a binary classification function to predict whether it is a partition of hand. …”
Get full text

Unique temperature change patterns in calves eyes and muzzles: a non-invasive approach using infrared thermography and object detection by Sueun Kim, Norio Yamagishi, Shingo Ishikawa, Shinobu Tsuchiaka

“…A mobile thermal imaging camera was paired with the Mask R-CNN algorithm (object detection) trained on annotated datasets to detect eye and muzzle regions accurately. …”
Get full text

Forecasting COVID-19 cases based on mobility by Mehmet Şahin

“…In this context, various machine learning algorithms are implemented to obtain accurate predictions. …”
Get full text

A novel approach for music genre identification using ZFNet, ELM, and modified electric eel foraging optimizer by Shuang Zhang, Zhiyong Sun, Hasan Jafari

“…The present research suggests a new method for music genre identification via integrating deep learning models with a metaheuristic algorithm. The proposed model uses a pre-trained Zeiler and Fergus Network (ZFNet) to extract high-level features from audio signals, while an Extreme Learning Machines (ELM) is utilized for efficient classification. …”
Get full text

Enhancing Power Converter Reliability Through a Logistic Regression-Based Non-Invasive Fault Diagnosis Technique by Acácio M. R. Amaral

“…To train and evaluate the LR model, two different datasets were created using various electrical quantities that can be measured non-invasively. …”
Get full text

MACRPO: Multi-agent cooperative recurrent policy optimization by Eshagh Kargar, Ville Kyrki

“…The use of this control parameter is suitable for environments in which the agents are unable to fully cooperate with each other. We evaluate our algorithm on three challenging multi-agent environments with continuous and discrete action spaces, Deepdrive-Zero, Multi-Walker, and Particle environment. …”
Get full text