Infrared Small Target Detection Based on Double Gray-Values Descend Angle Contrast Measure by Song Guan, Dali Zhou, Xiaodong Wang, Pengji Zhou

“…Infrared small target detection remains a subject of significant theoretical research value and practical application potential. …”
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PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES by Emmanuel J. Ebong, Samuel N. John, Dominic S. Nyitamen, Samuel F. Kolawole

“…The large dataset was scaled down using Min Max Scaler before the Machine Learning (ML) classification stage. Four (4) ML algorithms namely, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF) were used to classify the models. …”
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Parvovirus B19 Infection due to over Immunosuppression in Kidney Transplant Recipients: Case Reports and Literature Review by Abdulrahman Altheaby, Malak Alotaibi, Nuha Alajlan, Ala Alshareef, Mohammed Alruwaymi, Ghaleb Aboalsamh, Mohammed F. Shaheen, Mohammed Alzunitan, Ziad Arabi

“…The limited data available regarding consensus on screening algorithms and indications in donors and recipients prior to kidney transplantation makes diagnosis and management challenging. …”
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Assessing Lightweight Folding UAV Reliability Through a Photogrammetric Case Study: Extracting Urban Village’s Buildings Using Object-Based Image Analysis (OBIA) Method by Junyu Kuang, Yingbiao Chen, Zhenxiang Ling, Xianxin Meng, Wentao Chen, Zihao Zheng

“…Nevertheless, limited research currently explores data collected by such compact UAVs, and whether they can balance a small form factor with high data quality remains uncertain. …”
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AlgAlert: A two-level approach for algae bloom prediction using deep learning by Areej Alsini, Amina Saeed, Dawood Amin

“…Consequently, governmental agencies and research institutions invest significant effort into monitoring water quality and developing management strategies for aquatic systems. …”
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YOLO-HF: Early Detection of Home Fires Using YOLO by Bo Peng, Tae-Kook Kim

“…This gap causes object detection algorithms to produce high false positive and false negative rates in research on the early detection of such fires. …”
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Data-Driven Revolution in Academic Support for Mathematics Underachievers through Random Forest Individual and Hybrid Model by Asadi Srinivasulu, Vanithamani Palanisamy

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A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network by Owen He, Daoxing Chen, Yimei Li

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Predictive model for initial response to first-line treatment in children with infantile epileptic spasms syndrome by Wenrong Ge, Lin Wan, Zong Wang, Lijun Fu, Guang Yang

“…Methods Using a dataset from our previously published research, we constructed and tested a predictive model for the initial response to first-line treatment in children with IESS. …”
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Data-driven predictive models for sustainable smart buildings by Prabhu Rajaram, Gnana Swathika O․V․

“…Green AI is crucial for enhancing the environmental sustainability of AI systems in intelligent buildings. While some research is being undertaken on the role of AI in green and smart architecture, our comprehension of its usability and advantages is fragmented. …”
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Binarized Neural Networks for Resource-Efficient Spike Sorting by Luca M. Meyer, Majid Zamani, Andreas Demosthenous

“…Deep learning-based spike sorting algorithms are based on power-intensive dot products, which poses challenges for on-chip processing with resource-constrained devices. …”
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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…Among the evaluated algorithms, MLP consistently achieves the highest predictive accuracy across both temporal and spatial scenarios. …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…It examines the integration of advanced sensors, remote sensing, and machine learning algorithms in precision agriculture, assessing their roles in optimizing irrigation and nutrient management. …”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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Design and validation of a novel multiple sites signal acquisition and analysis system based on pressure stimulation for human cardiovascular information by Gaiqin Liu, Yuan Li, Longcong Chen, Juan Jiang, Jie Tian, Panpan Feng

“…Furthermore, the results suggest that the system can facilitate in-depth research into the relationships between collected signals and CVDs, provide rich raw data for cardiovascular health assessment and disease prediction models based on machine learning algorithms, and offer a new non-invasive method for early diagnosis, evaluation, and prediction of CVDs.…”
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Audio signal transmission method in network-based audio analytics system by Антон Порошенко, Андрій Коваленко

“…To achieve these goals, the following methods are used: mathematical signal processing, data compression algorithms, optimization of network protocols, and the use of high-speed network connections. …”
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Advancing Alzheimer’s disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis by Md Nurul Ahad Tawhid, Siuly Siuly, Enamul Kabir, Yan Li

“…This study will establish a pioneering direction for future researchers and technology experts in the field of neurodiseases.…”
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Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematolo... by Vinod Kumar, Chander prabha, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

“…A multi-model machine learning approach compares nine algorithms: KNN, AdaBoost (AB), logistic regression (LR), random forest (RF), SVM, naive Bayes (NB), decision tree (DT), gradient boosting (GB), and stochastic gradient descent (SGD). …”
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Neural Network Optimization of Mechanical Properties of ABS-like Photopolymer Utilizing Stereolithography (SLA) 3D Printing by Abdulkader Ali Abdulkader Kadauw

“…This approach uses machine learning algorithms to analyze and predict the relationships between various printing parameters and the resulting mechanical properties, thereby allowing the engineering of better materials specifically designed for targeted applications. …”
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