Early Heart Attack Detection Using Hybrid Deep Learning Techniques by Niga Amanj Hussain, Aree Ali Mohammed

“…Given the significant risk that heart disease, particularly heart attacks, poses to individuals’ lives, it is crucial to develop effective techniques for early detection. Advanced machine learning and deep learning algorithms have the ability to predict heart attacks by analyzing a patient’s medical history and overall health. …”
Get full text

Assessment of different genotyping markers and algorithms for distinguishing Plasmodium falciparum recrudescence from reinfection in Uganda by Alex Mwesigwa, Monica Golumbeanu, Sam Jones, Sara L. Cantoreggi, Benson Musinguzi, Joaniter I. Nankabirwa, Everd Maniple Bikaitwoha, Joan N Kalyango, Charles Karamagi, Mateusz Plucinski, Samuel L. Nsobya, Christian Nsanzabana, Pauline Byakika-Kibwika

“…The 3/3 match-counting algorithm classified significantly fewer recrudescences than both the ≥ 2/3 and Bayesian algorithms at probability cutoffs of ≥ 0.7 and ≥ 0.8 (P < 0.05). …”
Get full text

Analysis of the Possibility of Using Selected Artificial Intelligence Algorithms for the Assessment of the Microstructure of Vermicular Cast Iron by U. Janiszewska, Ł. Marcjan, S. Gajoch, K. Jaśkowiec, A. Bitka, M. Małysza, D. Wilk-Kołodziejczyk

“…This paper presents an analysis of artificial intelligence algorithms in the context of their applicability to the automatic analysis of microstructure images. …”
Get full text

Assessment of different genotyping markers and algorithms for distinguishing Plasmodium falciparum recrudescence from reinfection in Uganda. by Mwesigwa, Alex, Golumbeanu, Monica, Jones, Sam, Cantoreggi, L. Sara, Musinguzi, Benson, Nankabirwa, I. Joaniter, Bikaitwoha, Everd Maniple, Kalyango, N. Joan, Karamagi, Charles, Plucinski, Mateusz, Nsobya, L. Samuel, Nsanzabana, Christian, Byakika-Kibwika, Pauline

“…The 3/3 match-counting algorithm classified significantly fewer recrudescences than both the ≥ 2/3 and Bayesian algorithms at probability cutoffs of ≥ 0.7 and ≥ 0.8 (P < 0.05). …”
Get full text

Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…Results indicate that all three algorithms achieved high accuracy and F1 scores, reaching 0.985. …”
Get full text

Early detection of plant leaf diseases using stacking hybrid learning. by Abdullah Sheneamer

“…Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. …”
Get full text

Improving dependability with low power fault detection model for skinny-hash. by Sonal Arvind Barge, Gerardine Immaculate Mary

“…Two fault detection approaches are proposed to detect faults in cryptographic algorithms running on IoT devices. …”
Get full text

Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment by Asha Varma Songa, Ganesh Redy Karri

“…It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN).  The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. …”
Get full text

Combining Physical and Network Data for Attack Detection in Water Distribution Networks by Côme Frappé - - Vialatoux, Pierre Parrend

“…Machine learning detection algorithms, which are showing encouraging results in cybersecurity applications, are leveraging the increasing number of datasets published in the water distribution community for better attack detection. …”
Get full text

RFID-embedded mattress for sleep disorder detection for athletes in sports psychology by Metin Pekgor, Aydolu Algin, Turhan Toros

“…A multi-layered mattress design integrates advanced RFID technology with machine learning algorithms—Gaussian process regression (GPR) and linear regression (LR)—to classify postures and detect movement anomalies. …”
Get full text

Adversarial liveness detector: Leveraging adversarial perturbations in fingerprint liveness detection by Antonio Galli, Michela Gravina, Stefano Marrone, Domenico Mattiello, Carlo Sansone

“…As a consequence, FAS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognise live fingerprints from fake replicas. …”
Get full text

Cost-effectiveness of follow-up algorithms for chronic thromboembolic pulmonary hypertension in pulmonary embolism survivors by Dieuwke Luijten, Wilbert B. van den Hout, Gudula J.A.M. Boon, Stefano Barco, Harm Jan Bogaard, Marion Delcroix, Karl-Friedrich Kreitner, Matthias Held, Menno V. Huisman, Luis Jara-Palomares, Stavros V. Konstantinides, Lucia J.M. Kroft, Albert T.A. Mairuhu, Lilian J. Meijboom, Thijs E. van Mens, Maarten K. Ninaber, Esther J. Nossent, Piotr Pruszczyk, Luca Valerio, Anton Vonk Noordegraaf, Frederikus A. Klok

“…Conclusion Subjecting all PE survivors to any of the currently established dedicated follow-up algorithms to detect CTEPH is cost-effective and preferred above not performing a dedicated follow-up, evaluated against the Dutch acceptability threshold of EUR 50 000 per QALY. …”
Get full text

RQPool: A Novel Multi-Branch Graph-Level Anomaly Detection by Aaron Alex Philip, Ziad Kobti

“…Moreover, existing Graph Neural Network (GNN) algorithms focus primarily on spatial domain features while neglecting spectral properties. …”
Get full text

Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors by Michael Reich, Njira Lugogo, Laurie D Snyder, Megan L Neely, Guilherme Safioti, Randall Brown, Michael DePietro, Roy Pleasants, Thomas Li, Lena Granovsky

“…Purpose By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict impending exacerbations of chronic obstructive pulmonary disease (COPD) for individual patients. …”
Get full text

An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form by Zaher Salah, Hamza Abu Owida, Esraa Abu Elsoud, Esraa Alhenawi, Suhaila Abuowaida, Nawaf Alshdaifat

“…This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and email length, to improve detection accuracy. …”
Get full text

Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

Get full text

Using triaxial accelerometry to detect hunts and kills by African wild dogs by James Redcliffe, Scott Creel, Scott Creel, Ben Goodheart, Ben Goodheart, Johnathan Reyes de Merkle, Johnathan Reyes de Merkle, Stephani S. Matsushima, Michelo Mungolo, Ruth Kabwe, Emmanuel Kaseketi, Will Donald, Adrian Kaluka, Clive Chifunte, Matthew S. Becker, Matthew S. Becker, Rory Wilson

Get full text

Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques by Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai, Yu-Jung Lin

“…Traditional sentiment analysis struggles with sarcasm detection and class imbalance. To address this, we introduce Synthetic Ensemble Oversampling methods (SEO) that effectively leverage the strengths of various oversampling algorithms. …”
Get full text

A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
Get full text

Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation by Kayode S. Adewole, Andreas Jacobsson, Paul Davidsson

“…The results show that XGBoost outperformed the other ensemble algorithms on two publicly available datasets for intrusion detection. …”
Get full text