Les machines y voient-elles quelque chose ? by Denis Bonnay

Get full text

An AIoT-Based Automated Farming Irrigation System for Farmers in Limpopo Province by Relebogile Langa, Michael Nthabiseng Moeti, Thabiso Maubane

“…However, artificial irrigation is wasteful as it is regulated and monitored through human intervention, leading to water scarcity which is one of the obstacles that threatens the agricultural sector in the province of Limpopo. A machine learning precipitation prediction algorithm optimizes water usage. …”
Get full text

Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
Get full text

Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors by Apeksha Gawande, Swati Sherekar, Ranjit Gawande

“…IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. …”
Get full text

Precision Weed Control Using Unmanned Aerial Vehicles and Robots: Assessing Feasibility, Bottlenecks, and Recommendations for Scaling by Shanmugam Vijayakumar, Palanisamy Shanmugapriya, Pasoubady Saravanane, Thanakkan Ramesh, Varunseelan Murugaiyan, Selvaraj Ilakkiya

Get full text

High accuracy inverse design of reconfigurable metasurfaces with transmission-reflection-integrated achromatic functionalities by Jiang Xiao-Qiang, Fan Wen-Hui, Chen Xu, Zhao Lv-Rong, Qin Chong, Yan Hui, Wu Qi, Ju Pei

“…Meanwhile, transfer learning is also employed to reduce the large quantity of training data required by the DNN. …”
Get full text

Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
Get full text

Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition by Jiachen Ge, Jianping Cai, Gaolei Zhang, Deyu Li, Lianyuan Tao

“…Using 23 prognostic genes, we developed and validated a prognostic signature through 101 machine learning algorithms and their combinations, with ridge regression demonstrating optimal performance. …”
Get full text

AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis by Cong Feng, Ahmed Kateb Jumaah Al-Nussairi, Mustafa Habeeb Chyad, Narinderjit Singh Sawaran Singh, Jianyong Yu, Amirfarhad Farhadi

“…The framework integrates machine learning (ML) algorithms for predictive temperature management, blockchain technology for secure data handling, and edge computing for real-time data processing, resulting in a highly efficient and secure system. …”
Get full text

Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…The growing availability of wearable devices capable of real-time HRV data collection opens up opportunities for early detection and health status classification using machine learning. …”
Get full text

Passive forensic based on spatio-temporal localization of video object removal tampering by Linqiang CHEN, Quanxin YANG, Lifeng YUAN, Ye YAO, Zhen ZHANG, Guohua WU

“…To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.…”
Get full text

Non-convex optimization with using positive-negative moment estimation and its application for skin cancer recognition with a neural network by P.A. Lyakhov, U.A. Lyakhova, R.I. Abdulkadirov

“…The introduction of the developed algorithms into the learning process of various architectures of multimodal neural network systems for analyzing heterogeneous data has made it possible to increase the accuracy of recognizing pigmented skin lesions by 2.33 – 5.69 percentage points, compared to the original optimization methods. …”
Get full text

An Enhanced Bio-inspired GWO–DE Technique for Efficient Feature Selection in the EEG-RSVP Paradigm by S. Abinayaa, S. S. Sridhar

Get full text

Research status and prospect of UWB radar echo signal processing technology for mine rescue by Xuezhao ZHENG, Chaoyang SUN, Yuan HUANG, Wen DING, Yang MA, Qiang LI, Haowei MOU

“…The extraction and fusion of multi-modal life signals and the optimization of data processing process are realized by using deep learning related technologies. …”
Get full text

Cost-Effectiveness Analysis of a Machine Learning–Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at... by Charlotte Havreng-Théry, Arnaud Fouchard, Fabrice Denis, Jacques-Henri Veyron, Joël Belmin

“…After each home visit, the home care aides completed a questionnaire on participants’ functional status using a smartphone app, and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of an emergency visit within 7 to 14 days. …”
Get full text

Classification of Cavendish Banana Quality using Convolutional Neural Network by Ajeng Ayu Suryani, Ummi Athiyah, Yohani Setiya Rafika Nur, Warto

“…We perform classification using one of the deep learning algorithms, namely Convolutional Neural Network. …”
Get full text

Application of Structure Tensor-Based Image Fusion Method in Marine Exploration by Xiaoyi MA, Yihong CHEN, Fei WANG, Shuo XIE

“…To obtain high-quality color fusion images with a prominent performance in detecting targets and obtaining comprehensive information, the deep learning-based image fusion method using structure tensors was optimized based on the characteristics of marine targets. …”
Get full text

Online clustering with interpretable drift adaptation to mixed features by Flavio Corradini, Vincenzo Nucci, Marco Piangerelli, Barbara Re

Get full text

Artificial Intelligence in Healthcare: Innovation and Impact in Medical Practice by Amelia-Maria ISAC, Corina VERNIC

“…Studies were selected following PRISMA guidelines, ensuring the inclusion of peer-reviewed research with statistical validation. Data were extracted from two key studies published between 2016 and 2021: (1) “Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs” (Gulshan et al., 2016) and (2) “Deep Learning for Alzheimer’s Disease: A Systematic Review” (Wang et al., 2021). …”
Get full text

Astronomaly Protege: Discovery through Human-machine Collaboration by Michelle Lochner, Lawrence Rudnick

Get full text