Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models by Hajar Fatorachian, Kulwant Pawar

“…This study evaluates the application of machine learning techniques—ARIMA (Auto-Regressive Integrated Moving Average) and Multiple Linear Regression (MLR)—to forecast demand trends and analyze key drivers in a mid-sized cold chain operation. …”
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Image Augmentation Using Both Background Extraction and the SAHI Approach in the Context of Vision-Based Insect Localization and Counting by Ioannis Saradopoulos, Ilyas Potamitis, Iraklis Rigakis, Antonios Konstantaras, Ioannis S. Barbounakis

“…Traditional insect monitoring methods are limited in scope, but advancements in AI and machine learning enable automated, non-invasive monitoring with camera traps. …”
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Zero-Shot Traffic Identification with Attribute and Graph-Based Representations for Edge Computing by Zikui Lu, Zixi Chang, Mingshu He, Luona Song

“…Methods based on machine learning and deep learning have achieved remarkable results, but they heavily rely on the distribution of training data, which makes them ineffective in handling unseen samples. …”
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How digital transformation can influence workflows, teaching practices and curricula in (bio)process science and engineering—An interview series with stakeholders by J. F. Buyel

“…Abstract A massive digital transformation is underway in biotechnology and process engineering fueled by recent advances in machine learning and so‐called artificial intelligence, especially in the large language model field (e.g., ChatGPT). …”
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Multi-objective design of multi-material truss lattices utilizing graph neural networks by Ramón Frey, Michael R. Tucker, Mohamadreza Afrasiabi, Markus Bambach

“…Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems. …”
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Deep learning–based resource allocation for secure transmission in a non-orthogonal multiple access network by Miao Zhang, Yao Zhang, Qian Cen, Shixun Wu

“…Machine learning techniques, especially deep learning algorithms have been widely utilized to deal with different kinds of research problems in wireless communications. …”
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Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self-Tuning Least Squares Support Vector Machine by Doddy Prayogo, Yudas Tadeus Teddy Susanto

“…The prediction accuracy of the ST-LSSVM was then compared to other machine learning methods, namely, LS-SVM and BPNN, and was benchmarked with the previous results by neural network (NN) from Goh using coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). …”
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A Vortex Identification Method Based on Extreme Learning Machine by Jun Wang, Lei Guo, Yueqing Wang, Liang Deng, Fang Wang, Tong Li

“…Global vortex identification methods are of high computational complexity and time-consuming. Machine learning methods are related to the size and shape of the flow field, which are weak in versatility and scalability. …”
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Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm by Weiwei Xiao

“…This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. …”
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Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review by Aqsha Nur, Defin Yumnanisha, Sydney Tjandra, Adang Bachtiar, Dante Saksono Harbuwono

“…AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. …”
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Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data by Arnaud Ferré, Mouhamadou Ba, Robert Bossy

“…An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Mohammad Aljanabi, Mohd Arfian Ismail, Vitaly Mezhuyev

“…ITLBO with supervised machine learning (ML) technique was used for feature subset selection (FSS). …”
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Automatic speech recognition predicts contemporaneous earthquake fault displacement by Christopher W. Johnson, Kun Wang, Paul A. Johnson

“…Abstract Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. …”
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The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. …”
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Language experience influences performance on the NIH Toolbox Cognition Battery: A cluster analysis by Ashley Chung-Fat-Yim, Sayuri Hayakawa, Viorica Marian

“…The present study addresses this limitation by using a machine learning algorithm, known as cluster analysis, to identify naturally occurring subgroups of participants with similar language profiles. …”
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Design of Intelligent Recognition English Translation Model Based on Deep Learning by Yuexiang Ruan

“…Additionally, a new and popular branch of machine learning is deep learning which has achieved excellent results in research fields such as natural language processing. …”
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Analysis of Acoustic Signals of Footsteps from the Piezoelectric Sensor by Bilge Çiğdem Çiftçi, Gamze Kaya, Mustafa Kurt

“…The source of the acoustic signal can be determined by matching it with the existing database using machine learning algorithms like face recognition systems for future goals.…”
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Radiative forcing from the 2020 shipping fuel regulation is large but hard to detect by Jianhao Zhang, Yao-Sheng Chen, Edward Gryspeerdt, Takanobu Yamaguchi, Graham Feingold

“…The strict sulfur regulation on shipping fuel implemented in 2020 (IMO2020) presents an opportunity to assess the potential impacts of such emission regulations and the detectability of deliberate aerosol perturbations for climate intervention. Here we employ machine learning to capture cloud natural variability and estimate a radiative forcing of +0.074 ±0.005 W m−2 related to IMO2020 associated with changes in shortwave cloud radiative effect over three low-cloud regions where shipping routes prevail. …”
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Advancements in the Application of Convolutional Neural Networks in Ultrasound Imaging for Breast Cancer Diagnosis and Treatment by An Zichen, Li Fan

“…Deep learning (DL), as one of the most powerful machine learning techniques in the field of artificial intelligence (AI), has the ability to automatically select features from raw data, achieving remarkable advancements in breast US imaging. …”
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A Novel Chinese Entity Relationship Extraction Method Based on the Bidirectional Maximum Entropy Markov Model by Chengyao Lv, Deng Pan, Yaxiong Li, Jianxin Li, Zong Wang

“…Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. …”
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