The Impact of AI Software on Financial Transactions by Li Boyang

“…The rapid advancement of artificial intelligence (AI) has profoundly transformed financial trading, enhanced efficiency, accuracy, and customer service while introducing new challenges. This paper explores AI’s applications in quantitative trading, risk forecasting, and intelligent customer interactions, demonstrating its ability to optimize decision-making and reduce operational costs. …”
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An efficient detection of Sinkhole attacks using machine learning: Impact on energy and security. by Muhammad Zulkifl Hasan, Zurina Mohd Hanapi, Zuriati Ahmad Zukarnain, Fahrul Hakim Huyop, Muhammad Daniel Hafiz Abdullah

“…This paper introduces SFlexCrypt, an innovative approach tailored to address these security challenges while optimizing energy consumption in WSNs. SFlexCrypt stands out by seamlessly integrating advanced machine learning algorithms to achieve high-precision detection and effective mitigation of sinkhole attacks. …”
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Improving the Integrity of a Voting Process with Biometric Authentication and Data Encryption by Walter M. Molina, Lino R. Mac Kay, Daniel Subauste

“…Throughout the years, the voting process has under-gone a digital transformation, aiming to achieve greater control and optimize time while ensuring the integrity of each vote. …”
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Financial service composition with various privacy levels in multiple cloud environment by Xianmei Hua, Xinrong Zhan, Feng Li, Junwen Lu

“…To solve these challenges, a novel approach for financial service composition based on Quaternion Genetic Algorithms (QGA) is proposed. Our method optimizes the scheduling of cloud-based services to minimize execution time, energy consumption, and the file size transferred between clouds, while meeting specified privacy requirements. …”
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Range and angle estimation with spiking neural resonators for FMCW radar by Nico Reeb, Javier Lopez-Randulfe, Robin Dietrich, Alois C Knoll

“…This research presents a novel spiking neuron model for signal processing of frequency-modulated continuous wave (FMCW) radars that outperforms the state-of-the-art spectrum analysis algorithms in latency and data bandwidth. These spiking neural resonators are based on the resonate-and-fire neuron model and optimized to dynamically process raw radar data while simultaneously emitting an output in the form of spikes. …”
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Development and validation of an interpretable multi-task model to predict outcomes in patients with rhabdomyolysis: a multicenter retrospective cohort studyResearch in context by Chunli Liu, Jie Shi, Fengjuan Wang, Duo Li, Yu Luo, Bofan Yang, Yunlong Zhao, Li Zhang, Dingwei Yang, Heng Jin, Jie Song, Xiaoqin Guo, Haojun Fan, Qi Lv

“…Ten machine learning (ML) algorithms were applied to construct multi-task prediction models. …”
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Сombat wounds microflora in modern warfare by V. P. Kovalchuk, N. S. Fomina, V. M. Kondratiuk, О. О. Fomin, Y. V. Lazarenko, K. V. Gumenyuk

“…Analysis of the clonal characteristics and antibiotic resistance of the most prevalent wound pathogens among individuals injured in the Russian-Ukrainian war to support microbiota surveillance of combat wounds and optimize treatment strategies. Materials and Methods. …”
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Calculation Model of Multi-roll Straightening Process Based on Bilinear Hardening and Power Hardening by ZHU Xiaoyu, CHENG Zixing, WANG Xiaogang, HAN Peisheng

“…The bilinear model shows a linear increase in error, while the power model displays a nonlinear pattern. …”
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Application of LiDAR and SLAM Technologies in Autonomous Systems for Precision Grapevine Pruning and Harvesting by Obaid Saif, Mohammed G., Nagaraju Krishna Chythanya

“…The project aims to integrate advanced technologies in grapevine pruning and harvesting to optimize these processes. The system improves accuracy and speed, reducing labor costs while enhancing grape yield and quality, representing a promising approach to vineyard management. …”
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From PGC to UGC to AIGC: Change of content paradigm by Gao Ao

“…The study also points out that the development of AIGC technology relies on the support of algorithms, meta-universes and blockchain technology, while also sparking discussions on intellectual property, copyright, ethics and other issues. …”
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Multi-Objective Performance Evaluation Framework for Integrated Fiber-Reinforced Shading Systems by Salman Oukati Sadegh, Emad Tabadkani, Mahmoud Oukati Sadegh

“…The proposed framework benefits from integrating Multi-objective Evolutionary Algorithms (MOEAs). Such algorithms have proven to address conflicting parameters effectively while providing a range of different solutions to problems. …”
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AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration by Nagarajan Munusamy, Indragandhi Vairavasundaram

“…AI-powered predictive analytics can forecast energy demand and supply, enabling proactive charging and discharging strategies. ML algorithms can optimize charging rates, battery health, and grid stability while also detecting anomalies and preventing potential faults. …”
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The impact of artificial intelligence on human resource management practices: An investigation by Thandukwazi R. Ncube, Kusangiphila K. Sishi, Jane P. Skinner

“…These include potential biases within AI algorithms, concerns about data privacy and over-surveillance of employees, along with exacerbating the ‘digital divide’ between those with access to technology and those without. …”
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Increasing load factor in logistics and evaluating shipment performance with machine learning methods: A case from the automotive industry by Raziye Kılıç Sarıgül, Burak Erkayman, Bilal Usanmaz

“…To solve this problem, both supervised and unsupervised learning algorithms were applied. First, unsupervised clustering algorithms were used to group the shipment performance based on similarities. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…We will also assess its applicability by integrating other ML models, which could provide enhanced insights for optimizing scheduling algorithms across diverse fog-cloud settings.…”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…SVM achieved the highest predictive accuracy (R2 = 0.98), while KNN and ANN showed lower performance (R2 = 0.69 and 0.71, respectively). …”
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Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…Among the models, XGBoost achieved the best performance (R² > 0.93), followed closely by RF, while SVR showed relatively lower accuracy. The ML predictions closely matched experimental outcomes, demonstrating the potential of data-driven modeling in optimizing asphalt mix design. …”
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Edge-based detection and localization of adversarial oscillatory load attacks orchestrated by compromised EV charging stations by Khaled Sarieddine, Mohammad Ali Sayed, Sadegh Torabi, Ribal Atallah, Chadi Assi

“…The edge-based detection relies on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. …”
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Battery Cell Charging Behavior Analysis Using Constant Current and Constant Voltage Methods by Anna Cyntia, Muhammad Imran Hamid, Ayu Elsa Afriyanti

“…While CC/CV offers optimal management for lithium-ion battery charging, future research can focus on investigating the long-term effects of CC/CV on battery life under various environmental conditions, considering the findings and methodologies of similar studies.…”
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A Modified Differential Evolution for Source Localization Using RSS Measurements by Yunjie Tao, Lincan Li, Shengming Chang

“…While differential evolution (DE) has demonstrated notable efficacy in optimizing multimodal cost functions, conventional implementations often grapple with suboptimal convergence rates and susceptibility to local optima. …”
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