Research status and progress on key technologies of intelligent orchard by CUI Hongwei, LU Xiaoxuan, YANG Yaqing, LU Xinyi, MA Hao, JI Jiangtao, JIN Xin, LI Xiuzhen, ZHAO Zimeng, ZENG Ningning

“…Using LiDAR, vision-based navigation, and obstacle avoidance algorithms, machinery can complete tasks safely and efficiently. …”
Get full text

Regional Logistics Network Design in Mitigating Truck Flow-Caused Congestion Problems by Mi Gan, Xinyuan Li, Fadong Zhang, Zhenggang He

“…We model the RLN design problem as a minimal cost problem and design double-layer Lagrangian relaxation heuristics algorithms to solve the model problems. Through experiments with data from the six-node problem and Sioux-Falls network, the effectiveness of the models and algorithms is verified. …”
Get full text

Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting by Jeonghoe Lee, Bingjiang Xia

“…In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.…”
Get full text

Comparative Analysis of Hybrid Model Performance Using Stacking and Blending Techniques for Student Drop Out Prediction In MOOC by Muhammad Ricky Perdana Putra, Ema Utami

“…The algorithms used in the base model are KNN, Decision Tree, and Naïve Bayes, while the meta-model uses XGBoost. …”
Get full text

Evaluation of Knowledge Management Maturity Level in Iranian Audiovisual Archives Based on the APQC Model by Sepideh Ciruskabiri, Atefeh Sharif, Saeed Rezaei Sharifabadi, Mohammad Hassanzadeh

“…Although a KM program is extremely important for the preservation and optimized utilization of such archives, its systematic implementation in Iran has not yet been comprehensively examined. …”
Get full text

Advancing Stability in Robot Manipulators: A Review of Recent Progress and Parameters by Shabnom Mustary, Mohammod Abul Kashem, Jannatul Mawya Sony, Nayem Hossain, Mohammad Asaduzzaman Chowdhury

“…ABSTRACT In this review, we present an overview of the current state‐of‐the‐art in robotics while emphasizing stability and control aspects with pragmatic consideration over robotic manipulators. …”
Get full text

Integrating artificial intelligence in nanomembrane systems for advanced water desalination by Anbarasu Krishnan, Thanigaivel Sundaram, Beemkumar Nagappan, Yuvarajan Devarajan, Bhumika

“…The increasing global demand for clean drinking water calls for innovative approaches to optimize desalination processes, making them more sustainable and efficient. …”
Get full text

Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation by Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth, Benkeser David

“…However, few of these methods are interpretable, while those that do emphasize interpretability often suffer in terms of performance. …”
Get full text

Design and Implementation of an Ultra-Low-Power Hazardous Gas Monitoring System by Hongyu Liu, Yuchen Wang, Jiankang Yu, Shuqing Wang, Huijuan Chen

“…In addition, the system optimizes data acquisition and processing algorithms to segment gases of different concentrations. …”
Get full text

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches by Cvetkovski Goga, Petkovska Lidija

“…In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. …”
Get full text

Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…This work compares five supervised machine learning algorithms, including artificial neural networks, support vector regression, kernel ridge regression, random forest, and Lasso regression. …”
Get full text

Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems by Benedetta Fasciolo, Nicolò Grasso, Giulia Bruno, Paolo Chiabert

“…Abstract As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. …”
Get full text

Repeatability and agreement of three biometers measuring corneal keratometry and astigmatism in eyes with cataract and high myopia by Haisheng Zhou, Tong Bao, Shuchan Li, Yunlei Pang, Qinglei Shao, Lifeng Wang, Lihua Yin, Jinli Wang, Na Hu, Yuanyuan Qi, Bozhou Zhang

“…Further research is needed to refine measurement algorithms and optimize clinical accuracy.…”
Get full text

Impact of Public Space in Primary and Secondary Schools Based on Natural Visibility Ratio by Feng Liu, Hao Zhou, Jiangtao Xie, Yue Tang, Shuyu Liu

“…In recent years, the safety of primary and secondary schools has garnered considerable attention from policymakers and architects, necessitating rational design methods to develop effective strategies that optimize the campus environment. This study utilizes algorithmic simulations, spatial analysis, and statistical methods to examine the relationships between the layouts of buildings, public space morphology, and campus safety. …”
Get full text

Channel and Power Allocation for Multi-Cell NOMA Using Multi-Agent Deep Reinforcement Learning and Unsupervised Learning by Ming Sun, Yihe Zhong, Xiaoou He, Jie Zhang

“…To address these challenges, this paper proposes a framework of MDRL-UL that integrates the multi-agent deep reinforcement learning with the unsupervised learning to allocate the channel and power resources in a near-optimal manner. In the framework, a multi-agent deep reinforcement learning neural network (MDRLNN) is proposed for channel allocation, while an attention-based unsupervised learning neural network (ULNN) is proposed for power allocation. …”
Get full text

A new temporal locality-based workload prediction approach for SaaS services in a cloud environment by Wiem Matoussi, Tarek Hamrouni

“…The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. …”
Get full text

Biosynthesis, applications, and mathematical modeling dynamics of lactic acid bacteria exopolysaccharides: A review by Ankit Barot, Kalyan Das, Yogesh Patel, et al.

“…These models improve the economic viability of LAB-EPS production by optimizing output and reducing substrate use. White-box models, utilize comprehensive system knowledge, forecast performance, and optimize processes such as EPS biosynthesis while employing algorithms like fuzzy patterns, rule-based systems, and decision trees to increase productivity and identify bottlenecks. …”
Get full text

GPU Assisted Brute Force Cryptanalysis of GPRS, GSM, RFID, and TETRA by Cihangir Tezcan, Gregor Leander

“…In this work we provide optimized implementations of several widely used algorithms on GPUs, leading to interesting insights on the cost of brute force attacks on several real-word applications. …”
Get full text

Ultra-High Spatial Resolution Clinical Positron Emission Tomography (PET) Systems by Myungheon Chin, Muhammad Nasir Ullah, Derek Innes, Craig S. Levin

“…A key strategy involves organ-specific, or loco-regional, scanner configurations that optimize photon detection efficiency (PDE) while balancing the trade-off between spatial resolution and image signal-to-noise ratio (SNR). …”
Get full text

Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…This study utilizes a range of machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, K-nearest neighbour, Support Vector Regression, LightGBM and CatBoost, to analyse their effectiveness in predicting pavement performance. …”
Get full text