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. …”
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DualPFL: A Dual Sparse Pruning Method with Efficient Federated Learning for Edge-Based Object Detection by Shijin Song, Sen Du, Yuefeng Song, Yongxin Zhu

“…However, existing pruning algorithms exhibit high sensitivity to network architectures and typically require multiple sessions of retraining to identify optimal structures. …”
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High-voltage defibrillator unit with increased energy output from a controlled capacitor storage by E. E. Gorokhov-Miroshnikov, G. V. Slyusarev

“…It has been established that it is possible to form a pulse with a virtually constant output energy in a wide range of patient resistances. The model of the proposed high-voltage unit shows the possibility of increasing the energy output from the capacitor bank when forming a pulse of optimal duration with a flat front. …”
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Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness by Yalin Gao, Dongwei Xu, Huiyan Zhu, Qi Xuan

“…To solve these problems, we propose a super-resolution denoising residual network (SDRNet), which combines the advantages of the super-resolution convolutional neural network (SRCNN) and the denoising convolutional neural network (DnCNN) to construct a pilot-based OFDM signal model, train SDRNet using OFDM pilot data containing Gaussian noise, and optimize its feature enhancement ability in frequency-selective fading channels. …”
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Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: a synoptic review by Sunshine A. De Caires, Chaney St Martin, Melissa A. Atwell, Fuat Kaya, Glorious A. Wuddivira, Mark N. Wuddivira

“…Future research should focus on improving model accuracy, interpretability, transparency, and explainability. …”
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Emerging trends in sustainable energy system assessments: integration of machine learning with techno-economic analysis and lifecycle assessment by Ebrahimpourboura Zahra, Mosalpuri Manish, Jonas Baltrusaitis, Dubey Pallavi, Mba Wright Mark

“…TEA and LCA methods are enhanced through ML’s predictive modeling, optimization algorithms, and data analysis capabilities, providing more precise and efficient evaluations of SES. …”
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Underwater Target 3D Reconstruction via Integrated Laser Triangulation and Multispectral Photometric Stereo by Yang Yang, Yimei Liu, Eric Rigall, Yifan Yin, Shu Zhang, Junyu Dong

“…At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. …”
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Recent advances in machine learning applications for MXene materials: Design, synthesis, characterization, and commercialization for energy and environmental applications by Sodiq Abiodun Kareem, Makinde Akindeji Ibrahim, Justus Uchenna Anaele, Olajesu Favor Olanrewaju, Emmanuel Omosegunfunmi Aikulola, Michael Oluwatosin Bodunrin

“…Recent studies confirm that ML models have been instrumental in improving MXene synthesis processes, enabling higher yields and optimization of properties, better purity, and scalability through real-time process control and reinforcement learning. …”
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A SAC-Bi-RRT Two-Layer Real-Time Motion Planning Approach for Robot Assembly Tasks in Unstructured Environments by Qinglei Zhang, Siyao Hu, Jianguo Duan, Jiyun Qin, Ying Zhou

“…To realize the safe assembly of assembly robots in dynamic and complex environments, a dynamic obstacle avoidance trajectory planning method for robots combining traditional planning algorithms and deep reinforcement learning algorithms is proposed to improve the robot’s agent and obstacle avoidance ability in dynamic and complex environments. …”
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Design and analysis of intelligent service chain system for network security resource pool by Zenan WANG, Jiahao LI, Chaohong TAN, Dechang PI

“…The traditional network security architecture ensures network security by directing traffic through hardware based network security function devices.Since the architecture consists of fixed hardware devices, it leads to a single form of network security area deployment and poor scalability.Besides, the architecture cannot be flexibly adjusted when facing network security events, making it difficult to meet the security needs of future networks.The intelligent service chain system for network security resource pool was based on software-defined network and network function virtualization technologies, which can effectively solve the above problems.Network security functions of virtual form were added based on network function virtualization technology, combined with the existing hardware network elements to build a network security resource pool.In addition, the switching equipment connected to the network security elements can be flexibly controlled based on software-defined network technology.Then a dynamically adjustable network security service chain was built.Network security events were detected based on security log detection and a expert library consisting of security rules.This enabled dynamic and intelligent regulation of the service chain by means of centralized control in the face of network security events.The deployment process of the service chain was mathematically modeled and a heuristic algorithm was designed to realize the optimal deployment of the service chain.By building a prototype system and conducting experiments, the results show that the designed system can detect security events in seconds and automatically adjust the security service chain in minutes when facing security events, and the designed heuristic algorithm can reduce the occupation of virtual resources by 65%.The proposed system is expected to be applied to the network security area at the exit of the campus and data center network, simplifying the operation and maintenance of this area and improving the deployment flexibility of this area.…”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. …”
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State-of-the-Art on IoV-Based Deep Learning Framework for Enhanced Driving Behavior Recognition: Recent Progress, Technology Updates, Challenges, and Future Direction by Hongguang Li, Shafrida Sahrani, Mahidur R. Sarker, Yinglin Xiao

“…However, these advanced algorithms still face several challenges. We analyze relevant literature from 2015 to 2024, to uncover key trends and gaps in the development of applications for identifying dangerous driving behaviors, with a primary focus on optimizing integration to improve the accuracy and real-time capability of driving behavior prediction. …”
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Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions by Maryam Abbasi, Paulo Váz, José Silva, Pedro Martins

“…Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. …”
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Churn prediction for SaaS company with machine learning by Hugo Eduardo Sanches, Ayslan Trevizan Possebom, Linnyer Beatrys Ruiz Aylon

“…Design/methodology/approach – Through a preprocessing and normalization of data, seven machine learning algorithms were applied. The models were trained, and also cross-validation and parameter tuning techniques were applied to improve results. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…The study also delves into the potential of machine learning to optimize geothermal design, monitor performance, improve performance, find errors, and more. …”
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Unraveling C-to-U RNA editing events from direct RNA sequencing by Adriano Fonzino, Caterina Manzari, Paola Spadavecchia, Uday Munagala, Serena Torrini, Silvestro Conticello, Graziano Pesole, Ernesto Picardi

“…Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. …”
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Effect of tool angle in nanocutting of single crystal GaN using diamond cutter by Yongqiang WANG, Hao XIA, Zhihang HU, Shuaiyang ZHANG, Shaohui YIN

“…Large-scale MD simulations are meticulously performed to model the nanoscale cutting process of single-crystal GaN using a diamond tool. …”
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Automated Body Condition Scoring in Dairy Cows Using 2D Imaging and Deep Learning by Reagan Lewis, Teun Kostermans, Jan Wilhelm Brovold, Talha Laique, Marko Ocepek

“…The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. …”
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Enhancing action recognition in educational settings using AI-driven information systems for public health monitoring by Changchun Lu, Han Ruijuan

“…These limitations hinder their effectiveness, particularly in detecting health-related behaviors such as sedentary patterns, social interactions, and hygiene compliance.MethodsTo overcome these shortcomings, this research introduces an AI-driven information system that leverages advanced deep learning models and an Adaptive Knowledge Embedding Network (AKEN) to improve action recognition accuracy. …”
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Disseminated intravascular coagulation by Satoshi Gando, Marcel Levi, Cheng-Hock Toh

“…Anticoagulant use has been extensively debated; however, the selection of optimal target patients could optimize their application and improve patient outcomes in the near future. …”
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