Advanced Technologies for Smart Fertilizer Management in Agriculture: A Review by Jian-Jun Liu, Hao Wu, Imran Riaz

“…Precision fertilization has emerged as a critical component of modern smart agriculture, providing a transformative approach to optimizing nutrient application, enhancing crop productivity, and minimizing environmental harm while improving resource use efficiency. …”
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Enhancing Autonomous Driving Perception: A Practical Approach to Event-Based Object Detection in CARLA and ROS by Jingxiang Feng, Peiran Zhao, Haoran Zheng, Jessada Konpang, Adisorn Sirikham, Phuri Kalnaowakul

“…In contrast, event-based cameras, which operate asynchronously and capture rapid changes with high temporal resolution and expansive dynamic range, offer a promising augmentation. While the previous research on event-based object detection has predominantly focused on algorithmic enhancements via advanced preprocessing and network optimizations to improve detection accuracy, the practical engineering and integration challenges of deploying these sensors in real-world systems remain underexplored. …”
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Quantum annealing feature selection on light-weight medical image datasets by Merlin A. Nau, Luca A. Nutricati, Bruno Camino, Paul A. Warburton, Andreas K. Maier

“…Abstract We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets. …”
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Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

“…However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages, are computationally expensive and do not always yield optimal results. …”
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Preliminary Study on Sensor-Based Detection of an Adherent Cell’s Pre-Detachment Moment in a MPWM Microfluidic Extraction System by Marius-Alexandru Dinca, Mihaita Nicolae Ardeleanu, Dan Constantin Puchianu, Gabriel Predusca

“…This optimizes detachment while minimizing liquid absorption and reflux volume, ensuring efficient extraction. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…This study's novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.…”
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MODEL OF DATA TRAFFIC QOS FAST REROUTING IN INFOCOMMUNICATION NETWORKS by Oleksandr Lemeshko, Maryna Yevdokymenko, Oleksandra Yeremenko

“…The application of this solution contributes to the optimal use of the available network resource while providing the specified level of Quality of Service in terms of bandwidth and probability of packet loss along both the primary and backup routes in the case of a failure of the infocommunication network elements. …”
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Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design by Yingzhao Shao, Junyi Wang, Xiaodong Han, Yunsong Li, Yaolin Li, Zhanpeng Tao

“…To meet the high-reliability requirements of real-time on-orbit tasks, this paper proposes a fault-tolerant reinforcement design method for spaceborne intelligent processing algorithms based on convolutional neural networks (CNNs). …”
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Students' performance prediction employing Decision Tree by Abtahi Ahmed, Farzana Akter Nipa, Wasi Uddin Bhuyian, Khaled Md Mushfique, Kamrul Islam Shahin, Huu-Hoa Nguyen, Dewan Md. Farid

“…Traditional machine learning algorithms perform quite well in general, with SVM giving a best accuracy of around 95% with academic data, while training and testing the model without academic data still gives a good performance of 93%. …”
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Advances in UAV Path Planning: A Comprehensive Review of Methods, Challenges, and Future Directions by Wenlong Meng, Xuegang Zhang, Lvzhuoyu Zhou, Hangyu Guo, Xin Hu

“…A fundamental aspect of UAV autonomy lies in route optimization, which determines efficient paths while considering factors like mission goals, safety, and power consumption. …”
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Reinforcement Learning With Selective Exploration for Interference Management in mmWave Networks by Son Dinh-van, van-Linh Nguyen, Berna Bulut Cebecioglu, Antonino Masaracchia, Matthew D. Higgins

“…Numerical results demonstrate that selective exploration improves per-user spectral efficiency by up to 22.5% compared to scenarios without it. Additionally, our algorithms significantly outperform existing methods by 50% in terms of per-user spectral effciency and achieve 90% of the per-user spectral efficiency of the exhaustive search approach while requiring only 0.1% of its computational runtime.…”
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Integrative Analysis of Epidemiological Profile of Sensitization Clinical Laboratory Markers in Children: Cross-Sectional Study Results by L. G. Levina, L. S. Namazova-Baranova, E. A. Vishneva, K. S. Volkov, K. Y. Efendieva, A. A. Alekseeva, V. G. Kalugina, P. S. Arimova

“…Allergy diagnosis in children should be economically optimal and as sparing as possible, while ensuring the high quality of medical care. …”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…This research aims to compare the key machine learning approaches, Namely Support Vector Machines (SVM), Random Forest (RF) and Decision Trees (DT), in their ability to classify Intrusion Detection Systems (IDS) via routing networks over distributed computing systems. In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. …”
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High-efficiency sparse convolution operator for event-based cameras by Sen Zhang, Fusheng Zha, Fusheng Zha, Xiangji Wang, Mantian Li, Wei Guo, Pengfei Wang, Xiaolin Li, Lining Sun

“…However, despite their inherent sparsity, most existing visual processing algorithms are optimized for conventional standard cameras and dense images captured from them, resulting in computational redundancy and high latency when applied to event-based cameras. …”
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Advanced Brain Tumor Segmentation With a Multiscale CNN and Conditional Random Fields by Ala Guennich, Mohamed Othmani, Hela Ltifi

“…The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. …”
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Innovative Approaches to Sustainable Urban Planning: Analysing Current Trends by Meruyert Baidrakhmanova, Gani Karabayev, Seimur Mamedov

“…Artificial intelligence facilitates urban planning through data analysis and pattern recognition, while generative adversarial networks and transformers improve design and data processing functionalities. …”
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Parameter Tuning for Local-Search-Based Matheuristic Methods by Guillermo Cabrera-Guerrero, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, Enrique Cabrera

“…One common strategy used by matheuristic methods to solve such optimisation problems is to divide the main optimisation problem into several subproblems. While heuristics are used to seek for promising subproblems, exact methods are used to solve them to optimality. …”
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A PSO weighted ensemble framework with SMOTE balancing for student dropout prediction in smart education systems by Achin Jain, Arun Kumar Dubey, Shakir Khan, Arvind Panwar, Mohammad Alkhatib, Abdulaziz M Alshahrani

“…Furthermore, PSO offers computational efficiency advantages over Firefly and Ant Colony Optimization, reducing hyperparameter tuning time while improving ensemble performance. …”
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Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler by Zhigui Lin, Xiaofeng Zhang, Qi Liu, Jun Cui

“…We construct an FPGA + CPU heterogeneous detection system featuring a five-tuple segmented matching architecture, which integrates hash bitmap and shift-or algorithms to achieve fast-pattern matching. A hardware compiler supporting 10,000+ detection rules is developed, enhancing hardware adaptability through packet optimization and mask compilation. …”
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Survey of Quantum Generative Adversarial Networks (QGAN) to Generate Images by Mohammadsaleh Pajuhanfard, Rasoul Kiani, Victor S. Sheng

“…In addition, developing QGANs can generate and train parameters in quantum approximation optimization algorithms. One of the useful applications of QGAN is generating medical datasets that can generate medical images from limited datasets to train specific medical models for the recognition of diseases.…”
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