Multi-Source, Fault-Tolerant, and Robust Navigation Method for Tightly Coupled GNSS/5G/IMU System by Zhongliang Deng, Zhichao Zhang, Zhenke Ding, Bingxun Liu

“…Compared with standard Kalman filtering (EKF) and advanced multi-rate Kalman filtering (MRAKF), the proposed algorithm achieved 28.3% and 53.1% improvements in its <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mi>σ</mi></mrow></semantics></math></inline-formula> error, respectively, significantly enhancing the accuracy and reliability of the multi-source fusion navigation system.…”
Get full text

ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments by Fei Gao, Yang Tian, Yongliang Wu, Yunxia Zhang

“…In the neck network, the Shuffle Attention (SA) module is employed before each upsampling step to improve upsampling quality. …”
Get full text

Innovative Tailored Semantic Embedding and Machine Learning for Precise Prediction of Drug-Drug Interaction Seriousness by Ayman Mohamed Mostafa, Alaa S. Alaerjan, Hisham Allahem, Bader Aldughayfiq, Meshrif Alruily, Alshaimaa A. Tantawy, Mohamed Ezz

“…We improved the performance by increasing the BioWordVec Indication Substance embedding specificity, a new creation constructed through transfer learning methodologies employed on the BioWordVec model. This approach employs not only the names of the drugs but also the indications for the drugs and the active substances, forming a highly semantic network capable of capturing multiple relations between drugs. …”
Get full text

Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening by Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

“…This innovative approach to segmentation is formally introduced through two algorithms detailed in this paper. The weakly supervised segmentation framework achieved a Dice Similarity Coefficient (DSC) of 62.05% and an Intersection over Union (IoU) of 61.89%. …”
Get full text

Daily time-use compositions of physical behaviours and its association with evaluative and experienced wellbeing: a multilevel compositional analysis by Anantha Narayanan, Scott Duncan, Conal Smith, Flora Le, Lisa Mackay, Julia McPhee, Basile Chaix, Tom Stewart

“…Time-use data were processed using UK Biobank machine learning algorithms. We employed Bayesian multilevel compositional analysis to investigate how time-use behaviours, and reallocating time between behaviours, were associated with both life satisfaction and momentary affective states. …”
Get full text

Modeling and validation of wearable sensor-based gait parameters in Parkinson’s disease patients with cognitive impairment by Guo Hong, Guo Hong, Fengju Mao, Fengju Mao, Mingming Zhang, Fei Zhang, Fei Zhang, Xiangcheng Wang, Kang Ren, Kang Ren, Zhonglue Chen, Zhonglue Chen, Xiaoguang Luo, Xiaoguang Luo

“…The logistic regression model demonstrated superior predictive performance (test set AUC: 0.957), outperforming other machine learning algorithms. SHAP analysis revealed that Step Length, UPDRS-III score, Duration of PD, and Peak angular velocity during steering were the most influential predictors in the logistic regression model. …”
Get full text

Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…Hence, quality management is employed to reduce defects using quality improvement (QI) methods for achieving sustainability in manufacturing companies. …”
Get full text

Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…Drones powered with facial recognition were also being used to broadcast warnings to the citizens not to step out of their homes, and chide them for not wearing face masks. …”
Get full text

Development of an artificial intelligence system for the forecasting of infectious diseases by A. A. Kuzin, R. I. Glushakov, S. A. Parfenov, K. V. Sapozhnikov, A. A. Lazarev

“…Employing machine learning algorithms, AI systems can rapidly analyze a large amount of data, extract specific disease patterns, and screen for the most efficient AI instruments in relation to specific tasks, thus contributing to prevention, diagnostics, and treatment of infectious diseases in the context of personalized medicine. …”
Get full text

Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…Data pre-processing, specifically min-max normalization, is crucial for improving the accuracy of distance-based algorithms like KNN. Decision tree-based algorithms like RF and CatBoost are less sensitive to data normalization. …”
Get full text

Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. …”
Get full text

Autonomous platform for solution processing of electronic polymers by Chengshi Wang, Yeon-Ju Kim, Aikaterini Vriza, Rohit Batra, Arun Baskaran, Naisong Shan, Nan Li, Pierre Darancet, Logan Ward, Yuzi Liu, Maria K. Y. Chan, Subramanian K.R.S. Sankaranarayanan, H. Christopher Fry, C. Suzanne Miller, Henry Chan, Jie Xu

“…In particular, the automated workflow and algorithms effectively explore the search space, mitigate biases, employ statistical methods to ensure data repeatability, and concurrently optimize multiple objectives with precision. …”
Get full text

Research on the robustness of the open-world test-time training model by Shu Pi, Xin Wang, Jiatian Pi

“…Capitalizing on the fact that model gradients dynamically change during testing, our method employs a single-step query-based approach to dynamically generate and update adversarial perturbations. …”
Get full text

Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety by Olly Kravchenko, Julia Bäckman, David Mataix-Cols, James J. Crowley, Matthew Halvorsen, Patrick F. Sullivan, John Wallert, Christian Rück

“…Conclusions The findings suggest that a model incorporating a broad array of multimodal data offered a modest improvement in explanatory power compared to one using a limited set of easily accessible measures. Employing machine learning algorithms capable of capturing complex non-linear associations and interactions is a viable next step to improve prediction of post-ICBT symptom severity. …”
Get full text

Deep learning-based time series prediction in multispectral and hyperspectral imaging for cancer detection by Lijun Hao, Changmin Wang, Jinshan Che, Mingming Sun, Yuhong Wang

“…Traditional approaches for cancer detection rely on handcrafted features and conventional machine learning algorithms, which struggle with high-dimensional spectral data, noise interference, and domain adaptation challenges. …”
Get full text

Digital Academic Leadership in Higher Education Institutions: A Bibliometric Review Based on CiteSpace by Olaniyi Joshua Olabiyi, Carl Jansen van Vuuren, Marieta Du Plessis, Yujie Xue, Chang Zhu

“…This was the result of a multi-step refinement process using CiteSpace’s default thresholds and clustering algorithms to detect the most influential nodes based on centrality, citation burst, and network clustering. …”
Get full text

Unsupervised Clustering Successfully Predicts Prognosis in NSCLC Brain Metastasis Cohorts by Emre Uysal, Gorkem Durak, Ayse Kotek Sedef, Ulas Bagci, Tanju Berber, Necla Gurdal, Berna Akkus Yildirim

“…<b>Background/Objectives</b>: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. …”
Get full text

Real-world assessment of healthcare provided by the National Health Service: The network of regional Beaver research platforms by Federico Rea, Pietro Pugni, Dario Pescini, Luigi Palmieri, Simona Giampaoli, Flavia Carle, Giovanni Corrao

“…The use of standardized and validated algorithms enables to obtain regional estimates that, being obtained by employing regional platforms containing data extracted with standardized procedure, may be compared and possibly summarized by using common meta-analytic techniques. …”
Get full text

A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. …”
Get full text

SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

“…Individual tree segmentation (ITS) from terrestrial laser scanning (TLS) point clouds is foundational for deriving detailed forest structural parameters, crucial for precision forestry, biomass calculation, and carbon accounting. Conventional ITS algorithms often struggle in complex forest stands due to reliance on heuristic rules and manual feature engineering. …”
Get full text