Multi task detection method for operating status of belt conveyor based on DR-YOLOM by Yongan LI, Tengjie CHEN, Hongwei WANG, Zhihao ZHANG

“…The results show that compared to mainstream single detection algorithms, DR-YOLOM multi task detection algorithm has better comprehensive detection ability, and this algorithm can ensure high target recognition accuracy, segmentation accuracy, and appropriate inference speed with a small number of parameters. …”
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Determinants of depressive symptoms in multinational middle-aged and older adults by Can Lu, Shenwei Wan, Zhiyong Liu

“…Abstract This study harnesses machine learning to dissect the complex socioeconomic determinants of depression risk among older adults across five international cohorts (HRS, ELSA, SHARE, CHARLS, MHAS). Evaluating six predictive algorithms, XGBoost demonstrated superior performance in four cohorts (AUC 0.7677–0.8771), while LightGBM excelled in ELSA (AUC 0.9011). …”
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Control strategy of robotic manipulator based on multi-task reinforcement learning by Tao Wang, Ziming Ruan, Yuyan Wang, Chong Chen

“…To tackle this issue, instead of uniform parameter sharing, we propose an adjudicate reconfiguration network model, which we integrate into the Soft Actor-Critic (SAC) algorithm to address the optimization problems brought about by parameter sharing in multi-task reinforcement learning algorithms. …”
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Fog Service Placement Optimization: A Survey of State-of-the-Art Strategies and Techniques by Hemant Kumar Apat, Veena Goswami, Bibhudatta Sahoo, Rabindra K. Barik, Manob Jyoti Saikia

“…To process these data, it requires substantial computing and storage resources for smooth implementation and execution. While centralized cloud computing offers scalability, flexibility, and resource sharing, it faces significant limitations in IoT-based applications, especially in terms of latency, bandwidth, security, and cost. …”
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Detection of water surface targets based on improved Deformable DETR by Pengjiu WANG, Junbin Gong, Wei LUO, Xiao HUANG, Junjie GUO

“…The algorithm is designed to significantly enhance the inference and training speed of the model while improving detection accuracy, thus achieving more efficient and robust detection of water surface targets. …”
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A novel approach for music genre identification using ZFNet, ELM, and modified electric eel foraging optimizer by Shuang Zhang, Zhiyong Sun, Hasan Jafari

“…The present research suggests a new method for music genre identification via integrating deep learning models with a metaheuristic algorithm. The proposed model uses a pre-trained Zeiler and Fergus Network (ZFNet) to extract high-level features from audio signals, while an Extreme Learning Machines (ELM) is utilized for efficient classification. …”
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Low latency Montgomery multiplier for cryptographic applications by khalid javeed, Muhammad Huzaifa, Safiullah Khan, Atif Raza Jafri

“…The proposed Montgomery multiplier is based on school-book multiplier, Karatsuba-Ofman algorithm and fast adders techniques. The Karatsuba-Ofman algorithm and school-book multiplier recommends cutting down the operands into smaller chunks while adders facilitate fast addition for large size operands. …”
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Injustice épistémique et démocratie délibérative à l’ère du numérique : l’envers du mouvement #BalanceTaStartUp by Coline Sénac

“…I point out that anonymity, while allowing victims to share their experiences, becomes a weapon for online trolls and detractors, seeking to discredit these testimonies. …”
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Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens by Cheng Han, Shiying Lu, Pan Hu, Jiang Chang, Deying Zou, Feng Li, Yansong Li, Qiang Lu, Honglin Ren

“…Integrating these genes into an ML model based on the support vector machine (SVM) algorithm allows us to predict the zoonotic potential of various Brucella strains with high accuracy. …”
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Implementation and Performance of Lightweight Authentication Encryption ASCON on IoT Devices by Gabriela Cagua, Valerie Gauthier-Umana, Carlos Lozano-Garzon

“…The Internet of Things (IoT) is growing rapidly, enabling interconnected devices to communicate and share data seamlessly. While this expansion drives significant advancements across industries, it also introduces critical challenges in ensuring secure device communication. …”
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HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks by Luca Marchetti, Rosario Lombardo, Corrado Priami

“…HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA). HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. …”
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Data augmentation scheme for federated learning with non-IID data by Lingtao TANG, Di WANG, Shengyun LIU

“…To solve the problem that the model accuracy remains low when the data are not independent and identically distributed (non-IID) across different clients in federated learning, a privacy-preserving data augmentation scheme was proposed.Firstly, a data augmentation framework for federated learning scenarios was designed.All clients generated synthetic samples locally and shared them with each other, which eased the problem of client drift caused by the difference of clients’ data distributions.Secondly, based on generative adversarial network and differential privacy, a private sample generation algorithm was proposed.It helped clients to generate informative samples while preserving the privacy of clients’ local data.Finally, a differentially private label selection algorithm was proposed to ensure the labels of synthetic samples will not leak information.Simulation results demonstrate that under multiple non-IID data partition strategies, the proposed scheme can consistently improve the model accuracy and make the model converge faster.Compared with the benchmark approaches, the proposed scheme can achieve at least 25% accuracy improvement when each client has only one class of samples.…”
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Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…This algorithm integrates the RepViT (Representation Learning with Visual Tokens) network to reduce computational complexity while capturing the global context of images. …”
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Transceiver Design for an FDA-MIMO Radar and MIMO Communication Spectral Coexistence System by Qihang XU, Lan LAN, Guisheng LIAO, Kewei WANG, Tongxing ZHENG

“…In addition, the communication transmission codebook is approximated using an inequality theorem, and the radar transmission waveform is optimized using Taylor expansion and relaxation algorithms. Simulation results reveal that this joint design method can effectively improve the SINR of the radar system while ensuring communication throughput, thereby considerably enhancing the performance of the FDA-MIMO radar and MIMO communication spectral coexistence system under mainlobe jamming conditions.…”
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Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review by Seyed Abolfazl Aghili, Amin Haji Mohammad Rezaei, Mohammadsoroush Tafazzoli, Mostafa Khanzadi, Morteza Rahbar

“…Heating, Ventilation, and Air Conditioning (HVAC) systems contribute a considerable share of total global energy consumption and carbon dioxide emissions, putting them at the heart of the issues of decarbonization and removing barriers to achieving net-zero emissions and sustainable development goals. …”
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Hybrid Optimization Machine Learning Framework for Enhancing Trust and Security in Cloud Network by Himani Saini, Gopal Singh, Amrinder Kaur, Sunil Saini, Niyaz Ahmad Wani, Vikram Chopra, Zahid Akhtar, Shahid Ahmad Bhat

“…For resource allocation, the framework employs the Time-aware modified best fit decreasing (T-MBFD) algorithm, which adapts to fluctuating workloads. Key input parameters for T-MBFD include available resources, job size, and time constraints, while output parameters focus on optimized resource distribution and minimizing wastage. …”
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Information Bottleneck-Based Domain Adaptation for Hybrid Deep Learning in Scalable Network Slicing by Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle

“…We propose pre-training a variational information bottleneck (VIB)-based Quality of Service (QoS) estimator, using slice-specific inputs shared across all source domain slices. Each target domain slice can deploy this estimator to predict its QoS and optimize slice resource allocation using the IDLA algorithm. …”
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A Comparison of Generalized Hyperbolic Distribution Models for Equity Returns by Virginie Konlack Socgnia, Diane Wilcox

“…To better understand the dependence structure of the stocks, we fit the MGHD and subclasses to both the stock returns and the two leading principal components derived from the price data. While the MGHD could fit both data subsets, we observed that the multivariate normality of the stock return residuals, computed by removing shared components, suggests that the departure from normality can be explained by the structure in the common factors.…”
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Leather Defect Detection Based on Improved YOLOv8 Model by Zirui Peng, Chen Zhang, Wei Wei

“…Addressing the low accuracy and slow detection speed experienced by algorithms based on deep learning for a leather defect detection task, a lightweight and improved leather defect detection algorithm, dubbed YOLOv8-AGE, has been proposed based on YOLOv8n. …”
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Assessing tafenoquine implementation in Brazil: a qualitative evaluation of perceptions of healthcare providers and Plasmodium vivax patients (QualiTRuST Study) by Alicia P. C. Santos, Marcelo A. M. Brito, Ana P. S. Oliveira, Rafaela N. Dávila, Hiran S. S. Gama, Evellyn A. R. T. Silva, Hélio A. Amazonas Jr., Patrícia C. S. Balieiro, Rosilene Rufatto, Penny Grewal Daumerie, Cássio R. L. Peterka, Dhélio Batista Pereira, Marcus V. G. Lacerda, Felipe L. G. Murta

“…Prescription insecurities among HCPs after initial training prompted requests for additional training. While some patients initially doubted single-dose efficacy, confidence grew with experience. …”
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