Modeling of laser beam absorption on rough surfaces, powder beds and sparse powder layers by Giandomenico Lupo, Martin Niemann, Christoph Goniva, Wojciech Szmyt, Xiao Jia, Vladyslav Turlo

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THE DEVELOPMENT OF INTELLIGENT MANAGEMENT USING CLOUDE TECHNOLOGIES by U. A. Vishniakou

“…The particularities of cloud computing are given and its implementation for the development of the modern intelligent management are considered.…”
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Tseng-Type Algorithms for the Split Variational Inclusion by Li-Jun Zhu, Tzu-Chien Yin

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Applications of Artificial Intelligence in Food Industry by Heera Jayan, Weiqing Min, Zhiming Guo

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Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Scoping Review by Mattias Seth, Hoor Jalo, Åsa Högstedt, Otto Medin, Bengt Arne Sjöqvist, Stefan Candefjord

“…Message queuing telemetry transport (MQTT; 7/30, 23%) and WebSocket (7/30, 23%) were commonly used for real-time emergency alerts, while fog and edge computing were often combined with cloud computing for enhanced processing power and reduced latencies. …”
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A New Zero Waste Design for a Manufacturing Approach for Direct-Drive Wind Turbine Electrical Generator Structural Components by Daniel Gonzalez-Delgado, Pablo Jaen-Sola, Erkan Oterkus

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Memristive Bellman solver for decision-making by Zhe Feng, Zuheng Wu, Jianxun Zou, Lingli Cheng, Xiaolong Zhao, Xumeng Zhang, Jian Lu, Cong Wang, Yilin Wang, Haochen Wang, Wenbin Guo, Zhibin Qian, Yunlai Zhu, Zuyu Xu, Yuehua Dai, Qi Liu

“…Abstract The Bellman equation, with a resource-consuming solving process, plays a fundamental role in formulating and solving dynamic optimization problems. …”
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DIAGONALISASI MATRIKS UNTUK MENYELESAIKAN MODEL MANGSA-PEMANGSA by Elvinus R. Persulessy

“…The purpose of diagonalization technique is to make the computing process to solve a predator-prey model more easier…”
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A secure end-to-end communication framework for cooperative IoT networks using hybrid blockchain system by Suresh Babu Erukala, Dimitar Tokmakov, Anoosha Perumalla, Rajesh Kaluri, Anna Bekyarova-Tokmakova, Nevena Mileva, Slavi Lubomirov

“…Fog computing has emerged as a viable solution, extending cloud capabilities to the edge through a distributed peer-to-peer (P2P) network, enabling decentralized data processing and management. …”
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Advancing Applications of Robot Audition Systems: Efficient HARK Deployment with GPU and FPGA Implementations by Zirui Lin, Hideharu Amano, Masayuki Takigahira, Naoya Terakado, Katsutoshi Itoyama, Haris Gulzar, Kazuhiro Nakadai

“…Although robot audition systems are versatile and suitable for various scenarios, efficiently deploying them can be challenging due to their high computational demands and extensive processing times. …”
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URBAN PASSENGER TRANSPORT ROAD NETWORKS: PROSPECTS OF METHODS’ OPTIMIZATION by S. M. Mochalin, J. A. Koleber

“…The existing numerous methods for optimizing urban route networks could be divided into two types: heuristic, which have become classical today, and qualitatively new ones – metaheuristic, allowing managing tasks that contain nonlinear functions in the process of optimizing urban route networks. As modern science, software and computing facilities in the studied area have been developing very fast, metaheuristic methods are becoming a promising direction.Results. …”
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The road to commercial success for neuromorphic technologies by Dylan Richard Muir, Sadique Sheik

“…Solving two key problems—how to program general Neuromorphic applications; and how to deploy them at scale—clears the way to commercial success of Neuromorphic processors. Ultra-low-power Neuromorphic technology will find a home in battery-powered systems, local compute for internet-of-things devices, and consumer wearables. …”
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Integrating and retrieving learning analytics data from heterogeneous platforms using ontology alignment: Graph-based approach by Mohd Hafizan Musa, Sazilah Salam, Siti Feirusz Ahmad Fesol, Muhammad Syahmie Shabarudin, Jack Febrian Rusdi, Mohd Adili Norasikin, Ibrahim Ahmad

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Survey of Multimodal Federated Learning: Exploring Data Integration, Challenges, and Future Directions by Mumin Adam, Abdullatif Albaseer, Uthman Baroudi, Mohamed Abdallah

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Novel load balancing mechanism for cloud networks using dilated and attention-based federated learning with Coati Optimization by Atul B. Kathole, Viomesh Kumar Singh, Ankur Goyal, Shiv Kant, Amit Sadanand Savyanavar, Swapnaja Amol Ubale, Prince Jain, Mohammad Tariqul Islam

“…Abstract Load balancing (LB) is a critical aspect of Cloud Computing (CC), enabling efficient access to virtualized resources over the internet. …”
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Self-Supervised Learning of End-to-End 3D LiDAR Odometry for Urban Scene Modeling by Shuting Chen, Zhiyong Wang, Chengxi Hong, Yanwen Sun, Hong Jia, Weiquan Liu

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Emerging Copper-to-Copper Bonding Techniques: Enabling High-Density Interconnects for Heterogeneous Integration by Wenhan Bao, Jieqiong Zhang, Hei Wong, Jun Liu, Weidong Li

“…Additionally, the explosive growth of artificial intelligence (AI) demands ever-high computing power and energy-efficient computing platforms. …”
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Accelerating Spark-Based Applications with MPI and OpenACC by Saeed Alshahrani, Waleed Al Shehri, Jameel Almalki, Ahmed M. Alghamdi, Abdullah M. Alammari

“…To the best of our knowledge, there is no existing method in big data applications related to utilizing graphics processing units (GPUs), which are now an essential part of high-performance computing (HPC) as a powerful resource for fast computation.…”
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Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review by Dhekre Saber Saleh, Mohd Shahizan Othman

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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

“…Python 3 was used in all the experiments using windows 10 laptop with 8GB RAM and 2.0GHZ processor. The performance of the supervised machine learning algorithms using the original datasets includes 60.33%, 56.24%, 57.01% and 59.06% for KNN, CART, NB, and SVM respectively. …”
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