Customer-centered mobile network intelligent operation approach by Kai LI, Wei LIU, Guiyang LUO, Jinglin LI

“…The rapid development of new types of services places greater demands on support ability of the personalized service in mobile communication systems.Traditional intelligent pipe carriers only consider limited factors such as current traffic status of the network and number of loading binding quantity,indicating that they don’t take account of the demands and features of wireless customers.Meanwhile,the rule-based method is overwhelmed due to numerous metrics of the wireless network,the complicated correlation among them and heterogeneous requirements of metrics from various users.Therefore,a user-centric intelligent network operation service was proposed,which utilized machine learning and deep learning method.The service had the ability to learn the correlation between user QoS guarantee requirements and wireless network metric status to construct a joint support architecture based on user requirements and wireless network status.The validity of the method on real data was verified.…”
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Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction by Joon Yul Choi, Doo Eun Kim, Sung Jin Kim, Hannuy Choi, Tae Keun Yoo

“…ChatGPT-4 effectively analyzed ocular data, calculated key indicators, generated calculator codes, and outperformed traditional machine learning models and indicators in handling unstructured data and corneal topography. …”
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Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
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Hybrid precoding method for mmWave massive MIMO systems based on LFM by Haoyi CHEN, Yifan DING, Ying CHENG

“…Analog-digital hybrid precoding is a key technology for millimeter wave massive MIMO systems that reduce hardware costs while balancing system performance.However,the traditional hybrid precoding scheme often needed to find a suitable codebook for precoding,and some codebooks were not easy to obtain or had deviations in actual situations.An analog-digital hybrid precoding method based on latent factor model (LFM) in machine learning without codebook was proposed for this problem.The LFM decomposition and stochastic gradient descent method were used to approximate the designed precoding matrix to the optimal full digital precoding matrix for good performance.The simulation results show that compared with the hybrid precoding design method based on orthogonal matching pursuit (OMP) algorithm,this method not only does not need a codebook,but also has better performance than the hybrid precoding algorithm based on OMP algorithm,which is closer to optimal full digital precoding method.…”
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DEEP NEURAL NETWORK-BASED APPROACH FOR COMPUTING SINGULAR VALUES OF MATRICES by Diyari A. Hassan

“… Matrix factorization techniques, such as Singular Value Decomposition (SVD), Eigenvalue Decomposition (EVD), and QR decomposition, have long been pivotal in computational mathematics, particularly for applications in signal processing, machine learning, and data analysis. With the growing size and complexity of data, traditional methods of matrix factorization face challenges in efficiency and scalability. …”
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Short-term power consumption forecasting using neural networks with first- and second-order differencing by Meftah Elsaraiti

“…Recent advancements in machine learning, particularly long short-term memory (LSTM) networks, have addressed some of these limitations by leveraging neural network architectures capable of learning complex temporal dependencies. …”
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Southeast Australia encapsulates the recent decade of extreme global weather and climate events by Milton Speer, Lance Leslie

“…Accelerated GW amplifies the impacts of SEAUS climate drivers, depending on their phases. We used machine learning attribution to identify climate drivers responsible for a range of extreme events. …”
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Transverse cracking in glass fibre-reinforced composites monitored with synchrotron X-ray multi-projection imaging by Elise Van Vlierberghe, Jeroen Soete, Eleni Myrto Asimakopoulou, Zisheng Yao, Julia Rogalinski, Zhe Hu, Kannara Mom, Bratislav Lukić, Christian Breite, Pablo Villanueava Perez, Yentl Swolfs

“…An extensive data set was gathered to make a 3D reconstruction of the crack evolution over time using XMPI and machine learning-based reconstruction algorithms.  …”
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Survey on reinforcement learning based adaptive bit rate algorithm for mobile video streaming services by Li’na DU, Li ZHUO, Shuo YANG, Jiafeng LI, Jing ZHANG

“…In recent years, with the continuous release of HTTP adaptive streaming (HAS) video datasets and network trace datasets, the machine learning methods, such as deep learning and reinforcement learning, have been continuously applied to adaptive bit rate (ABR) algorithms, which obtain the optimal strategy of rate control through interactive learning, and achieve superior performance that surpasses the traditional heuristic methods.Based on the analysis of the research difficulties of ABR algorithms, the research advances of ABR algorithms based on reinforcement learning (including deep reinforcement learning) was investigated.Furthermore, several representative HAS video datasets and network trace datasets were summarized, the evaluation metrics of the performance were depicted.Finally, the existing problems and the future tendency of ABR research were discussed.…”
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Research and development of network representation learning by Ying YIN, Lixin JI, Ruiyang HUANG, Lixin DU

“…Network representation learning is a bridge between network raw data and network application tasks which aims to map nodes in the network to vectors in the low-dimensional space.These vectors can be used as input to the machine learning model for social network application tasks such as node classification,community discovery,and link prediction.The traditional network representation learning methods are based on homogeneous information network.In the real world,the network is often heterogeneous with multiple types of nodes and edges.Moreover,from the perspective of time,the network is constantly changing.Therefore,the research method of network representation learning is continuously optimized with the complexity of network data.Different kinds of network representation learning methods based on different networks were introduced and the application scenarios of network representation learning were expounded.…”
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Prediction of Compressive Strength of Concrete and Rock Using an Elementary Instance-Based Learning Algorithm by Shun-Chieh Hsieh

“…The use of machine learning techniques to predict material strength is becoming popular. …”
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Features of using Cox regression in various instrumental environments by I. V. Kramarenko, L. A. Konstantinova

“…The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods. These models require the definition of tuning parameters related to the specifics of the subject area. …”
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Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest by Zhijun Liao, Ying Ju, Quan Zou

“…All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.…”
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Urban Vitality Measurement Through Big Data and Internet of Things Technologies by Young-Long Kim

“…This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. …”
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Revolutionizing cross-border e-commerce: A deep dive into AI and big data-driven innovations for the straw hat industry. by Junjie Dai, Xiaoyan Mao, Pengyue Wu, Huijie Zhou, Lei Cao

“…It identifies market and consumer demand trends through machine learning analysis of comprehensive e-commerce data and leverages generative AI to revolutionize production and marketing processes. …”
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Exploring Artificial Intelligence for Enhanced Endodontic Practice: Applications, Challenges, and Future Directions by Santosh R. Patil, Mohmed Isaqali Karobari

“…The primary objective is to synthesize current knowledge on AI technologies such as machine learning, deep learning, and neural networks and their integration into endodontic practice. …”
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RespiroDynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning by Ahmed Sharshar, Muhammad Sharshar, Hosam Elhady, Ahmed Aboeitta, Youssef Nafea, Yasser Ashraf, Mohammad Yaqub, Mohsen Guizani

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Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network by Hans Juwiantho, Esther Irawati Setiawan, Joan Santoso, Mauridhi Hery Purnomo

“…Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis, tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. …”
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Enhancing Semi-Supervised Learning With Concept Drift Detection and Self-Training: A Study on Classifier Diversity and Performance by Jose L. M. Perez, Roberto S. M. Barros, Silas G. T. C. Santos

“…Machine learning algorithms that assist in decision-making are becoming crucial in several areas, such as healthcare, finance, marketing, etc. …”
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Dengue dynamics, predictions, and future increase under changing monsoon climate in India by Yacob Sophia, Mathew Koll Roxy, Raghu Murtugudde, Anand Karipot, Amir Sapkota, Panini Dasgupta, Kalpana Baliwant, Sujata Saunik, Abhiyant Tiwari, Rajib Chattopadhyay, Revati K. Phalkey

“…Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. …”
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