Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis by Youness Chawki, Khalid Elasnaoui, Mohamed Ouhda

“…During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify the virus using X-ray and CT scans. …”
Get full text

Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution i... by Alejandro Coca-Castro, Maycol A. Zaraza-Aguilera, Yilsey T. Benavides-Miranda, Yeimy M. Montilla-Montilla, Heidy B. Posada-Fandiño, Angie L. Avendaño-Gomez, Hernando A. Hernández-Hamon, Sonia C. Garzón-Martinez, Carlos A. Franco-Prieto

“…The results are relevant for institutions tracking the dynamics of building areas from cloud computing platfo future assessments of classifiers in likewise platforms in other contexts.…”
Get full text

Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning. by Shan Gao, Amit K Chakraborty, Russell Greiner, Mark A Lewis, Hao Wang

“…The framework is further evaluated on four empirical datasets: COVID-19 incidence data from Singapore, 18 other countries, and Edmonton, Canada, as well as SARS data from Hong Kong, with two classifiers exhibiting consistently high accuracy. Our results highlight detectable statistical features distinguishing outbreak and non-outbreak sequences well before potential occurrence, in both synthetic and real-world datasets presented in this study.…”
Get full text

Detection of honey bees (<em>Apis mellifera</em>) in hypertemporal LiDAR point cloud time series to extract bee activity zones and times by J. S. Meyer, R. Tabernig, R. Tabernig, B. Höfle, B. Höfle

“…By training a random forest classifier based on local neighbourhood features, the classified points can then be clustered in single and distinct objects of bees/hornets. …”
Get full text

S³DR-Det: A Rotating Target Detection Model for High Aspect Ratio Shipwreck Targets in Side-Scan Sonar Images by Quanhong Ma, Shaohua Jin, Gang Bian, Yang Cui, Guoqing Liu, Yihan Wang

“…In this paper, to address the discrepancies in the above three aspects, we propose the Side-scan Sonar Dynamic Rotating Target Detector (S³DR-Det), which is a model with a dynamic rotational convolution (DRC) module designed to effectively gather rotating targets’ high-quality features during the model’s feature extraction phase, a feature decoupling module (FDM) designed to distinguish between the various features needed for regression and classification in the detection phase, and a dynamic label assignment strategy based on spatial matching prior information (S-A) specific to rotating targets in the training phase, which can more reasonably and accurately classify positive and negative samples. …”
Get full text

Android Malware Category and Family Identification Using Parallel Machine Learning by Ahmed Hashem El Fiky, Mohamed Ashraf Madkour, Ayman El Shenawy

“…Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  …”
Get full text

DAI-TIRS: An AI-Powered Threat Intelligence and Response System for Securing the Metaverse by Mohini Sharma, Raghav Sandhane, Jaydeep Rajeshkumar Katariya

“…DAI-TIRS is the integration of machine learning-based anomaly detection, dynamic honeypots, and predictive threat modeling that detect, classify, and mitigate AI-driven threats in real-time. …”
Get full text

High‐accuracy dynamic gesture recognition: A universal and self‐adaptive deep‐learning‐assisted system leveraging high‐performance ionogels‐based strain sensors by Yuqiong Sun, Jinrong Huang, Yan Cheng, Jing Zhang, Yi Shi, Lijia Pan

“…However, achieving rapid detection of subtle motions and timely processing of dynamic signals remain a challenge for sensors. …”
Get full text

Trajectory analysis using data mining techniques by Gary Reyes

“…The proposed approach integrates real-time data flow processing with a two-level clustering strategy to detect and analyze vehicular density patterns. The first level performs dynamic clustering of GPS locations, forming microclusters that represent spatially homogeneous traffic zones. …”
Get full text

A new approach combining CNN, RNN, and an improved Otsu threshold method for detecting hand gestures in people with thumb finger size problems and hand tremors by Malik Kareem Kadhim, Chen Soong Der, Chen Chai Phing

“…This research presents a comprehensive approach to identifying dynamic hand gestures, which is particularly beneficial for individuals with finger disabilities. …”
Get full text

Unraveling urban multi-modal travel patterns and anomalies: a data-driven approach by Ali Shateri Benam, Angelo Furno, Nour-Eddin El Faouzi

“…With a multi-modal approach, our study aims to uncover daily demand patterns, detect and classify anomalies, and explore their connections to the socioeconomic and spatial characteristics of urban zones. …”
Get full text

Uncovering the impact of outliers on clusters’ evolution in temporal data-sets: an empirical analysis by Muhammad Atif, Muhammad Farooq, Muhammad Shafiq, Tmader Alballa, Somayah Abdualziz Alhabeeb, Hamiden Abd El-Wahed Khalifa

Get full text

Enhanced visibility graph for EEG classification by Asma Belhadi, Pedro G. Lind, Pedro G. Lind, Pedro G. Lind, Youcef Djenouri, Youcef Djenouri, Anis Yazidi, Anis Yazidi

Get full text

A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…MethodsWe devised a hybrid deep learning–based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. …”
Get full text

A Hybrid Security Framework for Train-to-Ground (T2G) Communication Using DOA-Optimized BPNN Detection, Bayesian Risk Scoring, and RL-Based Response by Chaoyuan Sun, Weijiao Zhang, Peng Sun, Hui Wang, Chunhui Yang

“…First, feature selection is performed on the TON_IoT dataset to develop a Dream Optimization Algorithm (DOA)-optimized backpropagation neural network (DOA-BPNN) model for efficient anomaly detection. A Bayesian risk scoring module then quantifies detection outcomes and classifies risk levels, improving threat detection accuracy. …”
Get full text

In Silico Detection of Virulence Genes in Whole-Genome Sequences of Extra-Intestinal Pathogenic <i>Escherichia coli</i> (ExPEC) Documented in Countries of the Andean Community by Nabila Aldaz, Karen Loaiza, César Marcelo Larrea-Álvarez, Miroslava Anna Šefcová, Marco Larrea-Álvarez

Get full text

Dynamique de l'occupation du sol et trajectoire du couvert végétal autour de trois sites miniers du Sud Mali entre 1988 et 2019 by Souleymane Sidi Traore, Sidi Dembele, Djénéba Dembele, Nouhoum Diakite, Cheick Hamalla Diakite

“…The objective of this research is to determine the dynamics of land use and the change in plant cover in three mining sites. …”
Get full text

Towards a Universal Security Framework for Darknet Suppression: Conceptual Foundations and Future Prospects by Cheng HUANG, Jianwei DING, Jiapeng ZHAO, Zhouguo CHEN, Jinqiao SHI

“…The proposed real-time lightweight traffic detection method enhances law enforcement’s ability to identify and classify darknet activities. …”
Get full text

A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications by Dipraj Debnath, Fernando Vanegas, Juan Sandino, Ahmad Faizul Hawary, Felipe Gonzalez

“…This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. …”
Get full text

UAV-based estimation of post-sowing rice plant density using RGB imagery and deep learning across multiple altitudes by Trong Hieu Luu, Thanh Tam Nguyen, Quang Hieu Ngo, Huu Cuong Nguyen, Phan Nguyen Ky Phuc

“…The robust rice plant density estimation process incorporates two key innovations: first, a dynamic system of 12 adaptive segmentation thresholding blocks that effectively detects rice seed presence across diverse and variable background conditions. …”
Get full text