COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach by Zuhaira M. Zain, Nazik M. Alturki

“…Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. …”
Get full text

Development and evaluation of a low-cost database solution for the Community Paramedicine at Clinic (CP@clinic) database. by Ricardo Angeles, Krzysztof Adamczyk, Francine Marzanek, Melissa Pirrie, Mikayla Plishka, Gina Agarwal

“…This low-cost, user-friendly and secure database captures initial and follow-up data, incorporates algorithms that guide the paramedics, and calculates risk factor scores for the participants. …”
Get full text

Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters by Seunghun Baek

“…By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. …”
Get full text

Virtual Nurse for Detecting Suicide Risk Behaviors in Adolescents by Yohanes Tarigan

“…The Virtual Nurse uses machine learning algorithms to analyze user behavior, speech patterns, and interactions to identify signs of suicide risk. …”
Get full text

An efficient post-processing adaptive filtering technique to rectifying the flickering effects. by Anudeep Gandam, Jagroop Singh Sidhu, Sahil Verma, N Z Jhanjhi, Anand Nayyar, Mohamed Abouhawwash, Yunyoung Nam

“…Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. …”
Get full text

Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria. by Juho Rousu, Daniel D Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes

“…Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. …”
Get full text

FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

“…The study benchmarks four machine-learning algorithms— Random Forest, XGBoost, CatBoost and Long Short-Term Memory (LSTM) networks—for forecasting stock market liquidity in Germany’s DAX equity market. …”
Get full text

Edge vs. Cloud: Empirical Insights into Data-Driven Condition Monitoring by Chikumbutso Christopher Walani, Wesley Doorsamy

“…The tested induction machine fault diagnosis models are developed using popular algorithms, namely support vector machines, k-nearest neighbours, and decision trees. …”
Get full text

Quantum Computing and the Law: Navigating the Legal Implications of a Quantum Leap by Kasim Balarabe

“…The unique characteristics of quantum algorithms and hardware pose significant challenges for the existing patent system, necessitating a clear and consistent framework for protecting quantum innovations while fostering collaboration. …”
Get full text

Unraveling the Drivers of ESG Performance in Chinese Firms: An Explainable Machine-Learning Approach by Hyojin Kim, Myounggu Lee

“…Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. …”
Get full text

Three-dimensional reconstruction cloud studio based on semi-supervised generative adversarial networks by Chong YU

“…Because of the intrinsic complexity in computation,three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications.The existing methods for 3D reconstruction often produce holes,distortions and obscure parts in the reconstructed 3D models.While the 3D reconstruction algorithms based on machine learning can only reconstruct voxelized 3D models for simple isolated objects,they are not adequate for real usage.From 2014,the generative adversarial network (GAN) is widely used in generating unreal dataset and semi-supervised learning.So the focus of this paper is to achieve high quality 3D reconstruction performance by adopting GAN principle.A novel semi-supervised 3D reconstruction framework,namely SS-GAN-3D was proposed,which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge.This new model only takes 2D observation images as the weak supervision,and doesn’t rely on prior knowledge of shape models or any referenced observations.Finally,through qualitative and quantitative experiments and analysis,this new method shows compelling advantages over the current state-of-the-art methods on Tanks ＆amp; Temples and ETH3D reconstruction benchmark datasets.Based on SS-GAN-3D,the 3D reconstruction studio solution was proposed.…”
Get full text

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar by Pengxiang Li, Mingzhou Bai, Xin Li, Chenyang Liu

“…The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.…”
Get full text

Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery. by Basel Abu-Jamous, Rui Fa, David J Roberts, Asoke K Nandi

“…Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. …”
Get full text

Deriving and applying core competencies for intellectual property-based startup entrepreneurs: a data-driven approach to technology evaluation by Wongyu Lee, Jaeho Shin, Donghee Yoo

“…Despite IP-based startups’ role in fostering innovation and job creation, current research on these startups primarily emphasizes business feasibility, marketability, and technical viability while overlooking managerial competencies. Technology evaluations often prioritize secondary factors and neglect core managerial skills that are crucial for success. …”
Get full text

Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. …”
Get full text

Spectrum Allocation Using Integer Linear Programming and Kerr Optical Frequency Combs by Sergio Muñoz-Tapasco, Andrés F. Calvo-Salcedo, Jose A. Jaramillo-Villegas

“…Spectrum allocation methods, such as the Routing, Modulation Level, and Spectrum Assignment (RMLSA) approach, play a crucial role in executing this strategy efficiently. While current algorithms have improved allocation efficiency, further development is necessary to optimize network performance. …”
Get full text

Adaptive information-constrained mapping for feature compression in edge AI and federated systems by Viacheslav Kovtun

“…Efficient projection-gradient optimisation algorithms have been developed, suitable for implementation in constrained computational environments. …”
Get full text

Sewer Cleaning Robot: A Visually Assisted Cleaning Robot for Sewers by Bo Xiong, Lei Zhang, Zhaoyang Cai

“…This paper provides technical reserves for replacing human labor that use vision algorithms to assist in cleaning tasks within sewers.…”
Get full text

A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things. by Umhara Rasool Khan, Javaid A Sheikh, Aqib Junaid, Shazia Ashraf, Altaf A Balkhi

“…A detailed comparison of the five regression-based ML algorithms is presented, and it is observed that the ML models help in efficient use of resources while designing an antenna for bio-medical applications.…”
Get full text

Intelligent Beam-Hopping-Based Grant-Free Random Access in Secure IoT-Oriented Satellite Networks by Zhongliang Deng, Yicheng Liao

“…This technique utilizes orthogonal resource allocation algorithms to facilitate efficient resource sharing, effectively tackling the irregular and dynamic traffic. …”
Get full text