Search Results - Contraceptive autoencoder

A deep multiple self-supervised clustering model based on autoencoder networks.

Authors: Zhu, Ling Liu, Zijin Liu, Guangyu

Source: Scientific Reports; 5/26/2025, Vol. 15 Issue 1, p1-14, 14p

Subject Terms: CLUSTERING algorithms, ARTIFICIAL intelligence, AUTOENCODERS, IMAGE processing, COMPUTER workstation clusters

Enhanced instance selection for large-scale data using integrated clustering and autoencoder techniques.

Authors: Nazari, Mohammad Saadatfar, Hamid

Source: International Journal of Data Science & Analytics; Nov2025, Vol. 20 Issue 6, p5585-5602, 18p

Cervical Cytological and Colposcopic abnormalities in Cases Using Combined Oral Contraceptive Pills.

Authors: Ali Hussein, Sara Hossam El Said Nooh, Amal Abd El Aziz El Rahman Esmail, Amr Ahmed Abd et al.

Source: Zagazig University Medical Journal; Jan2025, Vol. 31 Issue 1, p484-492, 9p

Subject Terms: PAP test, ORAL contraceptives, CONTRACEPTION, CERVICAL intraepithelial neoplasia, CERVICAL cancer

Hybrid Greylag Goose deep learning with layered sparse network for women nutrition recommendation during menstrual cycle.

Authors: Logapriya, E. Rajendran, Surendran Zakariah, Mohammad

Source: Scientific Reports; 2/18/2025, Vol. 15 Issue 1, p1-22, 22p

Subject Terms: OPTIMIZATION algorithms, DEEP learning, AUTOENCODERS, NUTRITIONAL requirements, FEATURE extraction, MENSTRUATION, MENSTRUAL cycle

Explainable AI based cervical cancer prediction using FSAE feature engineering and H2O AutoML.

Authors: Karthikeyan, Panneerselvam Malaserene, I. Deepakraj, E.

Source: Scientific Reports; 11/18/2025, Vol. 15 Issue 1, p1-23, 23p

Subject Terms: CERVICAL cancer, MACHINE learning, PUBLIC health, PREDICTION models, CAUSAL models, ARTIFICIAL intelligence, FEATURE selection

Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP.

Authors: Liaw, Lawrence Chuin Ming Tan, Shing Chiang Goh, Pey Yun et al.

Source: Neural Computing & Applications; Aug2024, Vol. 36 Issue 22, p13895-13913, 19p

Subject Terms: TUMOR classification, SUPERVISED learning, CERVICAL cancer, MACHINE learning, EARLY diagnosis, DEEP learning

Machine learning based genome cancer dataset approaches: Review.

Authors: Singh, Sonam Jawahar Agrawal, Pooja

Source: AIP Conference Proceedings; 2024, Vol. 3005, p1-12, 12p

Subject Terms: DIGITAL technology, DISEASE risk factors, MACHINE learning, DISRUPTIVE innovations, NUCLEOTIDE sequencing

Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity.

Authors: SHUAI ZHOU CHI LIU DAYONG YE et al.

Source: ACM Computing Surveys; Aug2023, Vol. 55 Issue 8, p1-39, 39p

Subject Terms: ARTIFICIAL neural networks, DEEP learning, INTERNET security, INTRAUTERINE contraceptives

Effectiveness of data resampling and ensemble learning in multiclass imbalance learning.

Authors: Fachrie, Muhammad Musdholifah, Aina Pulungan, Reza

Source: Artificial Intelligence Review; Dec2025, Vol. 58 Issue 12, p1-60, 60p

Subject Terms: RESAMPLING (Statistics), ENSEMBLE learning, CLASSIFICATION, COMPARATIVE studies, EVIDENCE gaps

Systematic Analysis of Latent Fingerprint Patterns through Fractionally Optimized CNN Model for Interpretable Multi-Output Identification.

Authors: Sabir, Mubeen Khan, Zeshan Aslam Waqar, Muhammad et al.

Source: Computer Modeling in Engineering & Sciences (CMES); 2025, Vol. 145 Issue 1, p807-855, 49p

Subject Terms: BIOMETRIC identification, CONVOLUTIONAL neural networks, HUMAN fingerprints, NONLINEAR functions, FEATURE extraction, COMPUTER performance, GENDER, ARTIFICIAL intelligence

Patterns of neural activity in prelimbic cortex neurons correlate with attentional behavior in the rodent continuous performance test.

Authors: Miranda-Barrientos, Jorge Adiraju, Suhaas Rehg, Jason J. et al.

Source: Translational Psychiatry; 11/7/2025, Vol. 15 Issue 1, p1-12, 12p

Instrumental Variables in Causal Inference and Machine Learning: A Survey.

Authors: Wu, Anpeng Kuang, Kun Xiong, Ruoxuan et al.

Source: ACM Computing Surveys; Nov2025, Vol. 57 Issue 11, p1-36, 36p

Subject Terms: ARTIFICIAL neural networks, PATTERN recognition systems, MACHINE learning, DATA mining, DEEP reinforcement learning, INSTRUMENTAL variables (Statistics), LATENT variables, REINFORCEMENT learning

Techniques and challenges for nuclei segmentation in cervical smear images: a review.

Authors: Rasheed, Assad Shirazi, Syed Hamad Khan, Pordil et al.

Source: Artificial Intelligence Review; Oct2025, Vol. 58 Issue 10, p1-53, 53p

Subject Terms: ARTIFICIAL neural networks, IMAGE segmentation, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, IMAGE processing, DEEP learning

The Liver Meeting: 2025 Abstracts.

Source: Hepatology (02709139); 2025Supplement, Vol. 82, pS1-S2308, 2308p

Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.

Authors: Garg, Pankaj Krishna, Madhu Kulkarni, Prakash et al.

Source: Cancers; Sep2025, Vol. 17 Issue 17, p2799, 22p

Subject Terms: RANDOM forest algorithms, PREDICTION models, RECURRENT neural networks, CERVIX uteri tumors, BREAST tumors, OVARIAN tumors, ARTIFICIAL intelligence, COMPUTED tomography, EARLY detection of cancer, CONVOLUTIONAL neural networks, MAGNETIC resonance imaging, FEMALE reproductive organ tumors, SUPPORT vector machines, MESSENGER RNA, DEEP learning, ELECTRONIC health records, MACHINE learning, INDIVIDUALIZED medicine, DISEASE progression

An Outlier Detection Algorithm Based on Multimodal Granular Distances.

Authors: Huang, Tiancai Zhang, Shiwang Luo, Hao et al.

Source: Mathematics (2227-7390); Sep2025, Vol. 13 Issue 17, p2812, 16p

Subject Terms: OUTLIER detection, GRANULAR computing, MACHINE learning, DATA mining, DATA structures, SCIENTIFIC method, GRANULATION

ECTRIMS 2025 ePoster.

Source: Multiple Sclerosis Journal; 2025 Suppl 3, Vol. 31, p763-1333, 571p

Subject Terms: CONFERENCES & conventions, POSTERS

Evaluation of the efficacy of marine natural products against PARP-1/2 proteins in high-grade serous ovarian cancer: insights into MD and SMD simulations.

Authors: Priyankha, Sridhar Prakash, Muthuramalingam

Source: Journal of Biomolecular Structure & Dynamics; Aug(late) 2025, Vol. 43 Issue 13, p7031-7045, 15p

Advances in breast cancer diagnosis: a comprehensive review of imaging, biosensors, and emerging wearable technologies.

Authors: Khan, Abdul Qadeer Touseeq, Manahil Rehman, Sara et al.

Source: Frontiers in Oncology; 2025, p1-35, 35p

Subject Terms: WEARABLE technology, CANCER diagnosis, BREAST cancer, ARTIFICIAL intelligence, EARLY detection of cancer

IN TOO DEEP: NAVIGATING AN UNPREDICTABLE ALGORITHM.

Authors: Ranieri, Anthony J.

Source: Touro Law Review; 2025, Vol. 40 Issue 3, p815-849, 35p

Subject Terms: DEEPFAKES, MALWARE, INFORMATION technology, GOVERNMENT regulation, FREEDOM of speech, FEDERAL legislation

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