Information Technology Advancements Shaping the Evolution of Modern Accounting Systems by Uma. T.G

“…The research delves into key technological advancements such as cloud computing, big data analytics, and artificial intelligence and examines how they impact various aspects of accounting systems, including financial reporting, auditing, and decision-making. …”
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Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board by Jouahri Mohammed Amine, Moukhtari Manal, Oulhaj Nabil, Khimouj Mounir, Tajer Abdelouahed, Boulghasoul Zakaria

“…The results show that this method, combining machine vision, IoT and artificial intelligence, delivers significant energy savings without compromising user safety, offering a promising solution for modern cities.…”
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On the development of diagnostic support algorithms based on CPET biosignals data via machine learning and wavelets by Rafael F. Pinheiro, Rui Fonseca-Pinto

“…In this context, artificial intelligence has become increasingly prominent in the field of medicine, offering essential support for disease diagnosis. …”
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A data fusion-based method for pedestrian detection and flow statistics across different crowd densities by Ranpeng Wang, Hang Gao, Yi Liu

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Bayesian optimization-driven enhancement of the thermoelectric properties of polycrystalline III-V semiconductor thin films by Takamitsu Ishiyama, Koki Nozawa, Takeshi Nishida, Takashi Suemasu, Kaoru Toko

“…In view of this issue, artificial intelligence has recently been used to investigate and optimize thermoelectric materials. …”
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Decentralized Reactive Power Control in Distribution Grids With Unknown Reactance Matrix by Lintao Ye, Krishna Chaitanya Kosaraju, Vijay Gupta, Rodrigo D. Trevizan, Raymond H. Byrne, Babu R. Chalamala

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Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation by Seyedeh Fatemeh Saffari, Daeho Kim, Byungjoo Choi

“…Digital management of excavators has seen limited progress due to challenges in artificial intelligence (AI) training. The AI required for digital twinning of excavators necessitates a large volume of diverse imagery data, currently scarce in the construction domain. …”
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Algorithmic emergence? Epistemic in/justice in AI-directed transformations of healthcare by Imo Emah, SJ Bennett

“…Moves toward integration of Artificial Intelligence (AI), particularly deep learning and generative AI-based technologies, into the domains of healthcare and public health have recently intensified, with a growing body of literature tackling the ethico-political implications of this. …”
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Research on Human Pose Capture Based on the Deep Learning Algorithm by Wenbin Xie, Xueyun Gu, Xiuming Li, Luqi Zheng, Guodong Li, Haisheng Lu, Huakang Li

“…Aiming at the application demand of using artificial intelligence technology to accurately analyze and predict the motion training posture, a motion posture analysis and prediction system based on deep learning is designed in this paper. …”
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New strategies to enhance the efficiency and precision of drug discovery by Qi An, Liang Huang, Chuan Wang, Dongmei Wang, Yalan Tu

“…Looking ahead, Artificial Intelligence (AI) technologies and interdisciplinary collaboration are poised to address the growing complexity of drug discovery. …”
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An IoT and Machine Learning-Based Model to Monitor Perishable Food towards Improving Food Safety and Quality by Amina Batool, Souvik Ganguli, Hashem Ali Almashaqbeh, Muhammad Shafiq, A. L. Vallikannu, K. Sakthidasan Sankaran, Samrat Ray, F. Sammy

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Research Data Management Within the EU Data Policy Framework by Milčiuvienė Saulė, Amilevičius Darius, Raškinis Gailius

“…Despite these robust EU-level frameworks, Lithuania’s national policies lag behind, particularly in addressing the growing needs of artificial intelligence innovation.…”
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Cognitive FDA-MIMO Radar Network’s Transmit Element Selection Algorithm for Target Tracking in a Complex Interference Scenario by Yingfei Yan, Haihong Tao, Jingjing Guo, Biao Yang

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The Costs and Threats of the «Green» Energy Transition by I. I. Sechin

“…Concurrently, the demand for electricity will continue to rise with the advancement of artificial intelligence and the expansion of data processing centers.The article highlights that in Europe, the emphasis on the «green» energy transition has led to economic downturns, while in developing countries, the implementation of the energy transition has manifested as «green» neocolonialism, characterized by resource extraction. …”
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Explicit relation between thin film chromatography and column chromatography conditions from statistics and machine learning by Hao Xu, Wenchao Wu, Yuntian Chen, Dongxiao Zhang, Fanyang Mo

“…On this basis, an “artificial intelligence (AI) experience” is generated through a knowledge discovery framework, where the relationship between the retardation factor (RF) value from TLC and retention volume from CC is unveiled in the form of explicit equations. …”
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Incisor Malocclusion Using Cut-out Method and Convolutional Neural Network by Muhamad Farhin Harun, Azurah A Samah, Muhammmad Imran Ahmad Shabuli, Hairudin Abdul Majid, Haslina Hashim, Nor Azman Ismail, Syiral Mastura Abdullah, Aspalilah Alias

“…However, the manual orthodontic diagnostic procedure by dental experts to identify malocclusion is time-consuming and vulnerable to expert bias that results in delayed treatment completion time. Recently, artificial intelligence technology in image processing has gained attention in orthodontics treatment, accelerating the diagnosis and treatment process. …”
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Biologically Inspired Spatial–Temporal Perceiving Strategies for Spiking Neural Network by Yu Zheng, Jingfeng Xue, Jing Liu, Yanjun Zhang

“…However, current mainstream DNN (deep learning neural network)-based AI (artificial intelligence) is a ‘black box’. We cannot interpret or understand how the decision is made by these AIs. …”
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A New Approach to Fuzzy Differential Equations Using Weakly-Compatible Self-Mappings in Fuzzy Metric Spaces by Iqra Shamas, Saif Ur Rehman, Naeem Jan, Abdu Gumaei, Mabrook Al-Rakhami

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Customer management in B2B markets in the context of digitalization by M. V. Orlova, V. V. Orlov

“…The analysis of the research, in turn, revealed promising areas in the management of all the company’s clients, such as the gradual introduction of digital marketing methods, the use of technological advances (artificial intelligence and cloud computing), the provision of more personalized experience, the creation of scalable strategies, the development and implementation of domestic digital platforms for managing all the company’s clients.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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