MARSweb: a fully automated web service for set-based association testing by Taegun Kim, Jaeseung Song, Jong Wha J. Joo

“…Unfortunately, due to its complex running process, time complexity, and the requirement of high-performance computing resources, it is not widely used. …”
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Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…However, these methods are time-consuming and computationally costly, which makes ASR prediction challenging. …”
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Enhanced Cloud Detection Using a Unified Multimodal Data Fusion Approach in Remote Images by Yan Mo, Puhui Chen, Wanting Zhou, Wei Chen

“…Aiming at the complexity of network architecture design and the low computational efficiency caused by variations in the number of modalities in multimodal cloud detection tasks, this paper proposes an efficient and unified multimodal cloud detection model, M2Cloud, which can process any number of modal data. …”
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Real-Time Interference Mitigation for Reliable Target Detection with FMCW Radar in Interference Environments by Youlong Weng, Ziang Zhang, Guangzhi Chen, Yaru Zhang, Jiabao Chen, Hongzhan Song

“…DL-based approaches typically have high computational costs, which makes them unsuitable for real-time applications with strict latency requirements and limited computing resources. …”
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THE LEGAL STATUS OF THE SHARES TRADED ON RASDAQ MARKET by Cristian GHEORGHE

“…The role designated for RASDAQ was as platform for valuing papers issued in the privatization program in Romania (mass privatization process - MPP). In fact the participating companies to MPP had the legal obligation, under first Romanian Capital Market Law, No 52/1194, to be listed on a stock exchange. …”
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Dynamic and efficient vehicular cloud management scheme with privacy protection by Min XIAO, Tao YAO, Yuanni LIU, Yonghong HUANG

“…The vehicular cloud (VC) formed by vehicles is used for localization processing and consumption of traffic sensing data to achieve timely intelligent traffic management.The vehicle cloud is highly dynamic, self-organizing and timely, in which the identity and location privacy of vehicle users need to be protected as this poses challenges to the vehicular cloud management.A dynamic and self-organizing vehicle cloud management scheme based on the asymmetric group key agreement protocol was designed, where the vehicle cloud is automatically formed through the self-organized group key agreement of vehicles.The group key was used to control the provision and access of vehicle cloud services, and the dynamic management of the vehicle cloud was implemented through group key update.The scheme used traceable one-time pseudonym technology to achieve anonymous authentication and conditional privacy protection of vehicle users, and the group key agreement stage only included one bilinear pair operation to achieve high efficiency.In addition, the key negotiation and update process used lightweight signatures, supporting batch verification, to achieve efficient message source authentication and integrity authentication.Then the security and efficiency of vehicle cloud communications in the self-organizing environment can be ensured.The dynamic key update mechanism of the key agreement protocol realized the dynamic joining or exiting of vehicles in the vehicle cloud, adapting to the dynamic characteristics of the vehicle cloud.Under the random oracle model and the difficult assumption of the inverse computational Diffie Hellman (ICDH) problem, it was proved that the asymmetric group key agreement scheme satisfied the selective-plaintext security.The security analysis shows that the scheme can protect the identity and location privacy of vehicle users, realize the legal tracking of malicious vehicles, and ensure the confidentiality, integrity and anti-counterfeiting of communications, as well as the forward security of vehicle cloud dynamic management.The performance comparison analysis shows that this scheme has certain advantages in communication and computing efficiency under the condition of the same function and security level.…”
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Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks by Federica Colonnese, Francesco Di Luzio, Antonello Rosato, Massimo Panella

“…This refinement process significantly improves both accuracy and computational efficiency, achieving a test accuracy of 94.35% while using only 77% of the dataset, showing that the proposed GBAC outperforms the same model trained on the full dataset and random sample reductions, as well as the benchmarks. …”
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Developing stock sentiment rank metric for forecasting stock sentiments: a statistical perspective by Rachna Sable, Aman Singh, Sudhanshu Gupta, Shivani Goel, Pallavi Parlewar

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A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs by Ahmed Alabd-Aljabar, Zain Raisan, Mohammed Adnan, Salam Dhou

“…The rise in the emphasis on oral diseases has elevated the need to automate the diagnostic process of such diseases. Fortunately, the availability of modern computing devices has made the automated diagnosis of teeth readily possible using deep learning. …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

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A Quality-Centered Analysis of Eye Tracking Data in Foveated Rendering by Thorsten Roth, Martin Weier, André Hinkenjann, Yongmin Li, Philipp Slusallek

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Framework for the Multi-Objective Optimization of Hybrid Fuel Cell System Design and Operation by Luis M. Winkler, Alexander Kabza, Michael A. Danzer, Herbert Palm

“…The added value of the generic process is demonstrated by a MOO of an automotive FCS power train with fuel consumption and fuel cell aging as conflicting targets. …”
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SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity by Xun Wang, Zhijun Xia, Runqiu Feng, Tongyu Han, Hanyu Wang, Wenqian Yu, Xingguang Wang

“…Abstract Background Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research. …”
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A Review of the Industry 4.0 to 5.0 Transition: Exploring the Intersection, Challenges, and Opportunities of Technology and Human–Machine Collaboration by Md Tariqul Islam, Kamelia Sepanloo, Seonho Woo, Seung Ho Woo, Young-Jun Son

“…The Industrial Revolution (IR) involves a centuries-long process of economic and societal transformation driven by industrial and technological innovation. …”
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Wind Turbine Blade Defect Recognition Method Based on Large-Vision-Model Transfer Learning by Xin Li, Jinghe Tian, Xinfu Pang, Li Shen, Haibo Li, Zedong Zheng

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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

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Unmanned Ground Vehicles for Continuous Crop Monitoring in Agriculture: Assessing the Readiness of Current ICT Technology by Maurizio Agelli, Nicola Corona, Fabio Maggio, Paolo Vincenzo Moi

“…The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. …”
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High-throughput screening of multimetallic catalysts for three-way catalysis by Son Dinh Le, Nhan Nu Thanh Ton, Kalaivani Seenivasan, Patchanee Chammingkwan, Koichi Higashimine, Supareak Praserthdam, Toshiaki Taniike

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Machine learning-based prediction of CO2 solubility in methyldiethanolamine solutions: A comparative study by Sajjad Fazeli, Mohammad Amin Moradkhani, Behrouz Bayati

“…In order to construct robust models, three heuristic soft computing methods, including radial basis function neural network (RBF-NN), gaussian process regression (GPR) and multilayer perceptron neural network (MLP-NN) were employed. …”
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Blockchain Enabled Federated Learning for Detection of Malicious Internet of Things Nodes by Rachid Alami, Anjanava Biswas, Varun Shinde, Ahmad Almogren, Ateeq Ur Rehman, Tahseen Shaikh

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