Deteksi Objek menggunakan Dashboard Camera untuk Sistem Peringatan Pencegah Kecelakaan pada Mobil by Raymond Sutjiadi, Timothy John Pattiasina

“…This application was developed using Object Detection Method in Tensorflow Open Source Machine Learning Library. The research stage was started from problem analysis, literature study to search comparison from previous research, also software development and finalized with testing to measure system performance. …”
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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Radiomic features were extracted from the bone marrow of thoracic vertebrae on non-contrast chest CT scans. Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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Priority effects, nutrition and milk glycan-metabolic potential drive Bifidobacterium longum subspecies dynamics in the infant gut microbiome by Nicholas Pucci, Joanne Ujčič-Voortman, Arnoud P. Verhoeff, Daniel R. Mende

“…We further implemented machine learning tools to identify significant features associated with B. longum subspecies abundance. …”
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Pengembangan Sistem Pemantauan Sentimen Berita Berbahasa Indonesia Berdasarkan Konten dengan Long-Short-Term Memory by Dewi Yanti Liliana, Nadia Nurul Hikmah, Maykada Harjono

“…Sistem pengklasifikasi secara otomatis berbasis machine learning dilakukan dengan membangun model pembelajaran dari korpus berita yang berasal dari situs berita daring. …”
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Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. by Xiaohua Zeng, Changzhou Liang, Qian Yang, Fei Wang, Jieping Cai

“…Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. …”
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SOCIOLOGY OF KNOWLEDGE SECURITY IN THE DIGITAL EDUCATIONAL SPACE by M. Yu. Zakharov, I. E. Starovoytova, A. V. Shishkova

“…Artificial intelligence and machine learning technologies, big data analysis, robotics and virtualization have taken their place in the educational process, and traditional offline teaching methods require revision. …”
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Neutrosophic Clustering Algorithm Based on Sparse Regular Term Constraint by Dan Zhang, Yingcang Ma, Hu Zhao, Xiaofei Yang

“…Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. …”
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Diversity Perceptions in the UK Retail Industry: A Case of Tesco by Walid Ahmed, Asim Majeed, Ayesha Asim

“…It can be argued that with exponential technological changes like the emergence of artificial intelligence, machine learning, the internet of things (IoT), etc. retailers around the world are facing continuous challenges in keeping their business model updated and relevant for sustainable business growth and profitability. …”
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Dependable identity recognition and authorization based on visual information by Chunlei PENG, Xinbo GAO, Nannan WANG, Jie LI

“…Recently,deep learning has been widely applied to video and image based identity recognition and authorization tasks,including face recognition and person identification.However,machine learning models,especially deep learning models,can be easily fooled by adversarial attacks,which may cause the identity recognition systems to make a wrong decision.Therefore,dependable identity recognition and authorization has become one of the hot topics currently.Recent advances on dependable identity recognition and authorization from both information space and physical space were presented,where the development of the attack models on face detection,face recognition,person re-identification,and face anti-spoofing as well as printable adversarial patches were introduced.The algorithms of visual identity anonymization and privacy protection were further discussed.Finally,the datasets,experimental protocols and performance of dependable identity recognition methods were summarized,and the possible directions in the future research were presented.…”
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An incentive mechanism with bandwidth allocation for federated learning by Yingyun GUO, Bo GAO, Zhifei ZHANG, Yu ZHANG, Ke XIONG

“…Federated learning (FL) is an emerging machine learning paradigm that can make full use of crowd sourced mobile resources for training on decentralized data.However, it is challenging to deploy FL over a wireless network because of the limited bandwidth and clients’ selfishness.To address these challenges, an incentive mechanism with bandwidth allocation (IMBA) was proposed.Considering the difference between clients' data quality and computing power, IMBA designs a payment scheme to incentivize high-quality clients to contribute their computing resources, thus improving the training accuracy of the model.By minimizing the weight sum of training time and payment cost, the optimal payment and bandwidth allocation scheme was determined, and the training delay was reduced by optimizing bandwidth allocation.Experiments show that IMBA effectively improves training accuracy, reduces the training delay and helps the server flexibly balance training delay and hiring payment.…”
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Data Lakehouse: Benefits in small and medium enterprises by Blaž Čuš, Darko Golec

“…It is based on open data formats with direct access and has excellent support for machine learning and data science. To the enterprises that currently process only structured data, so they are technologically behind the competition, Data Lakehouse can help to solve big challenges based on Data Warehouses and Data Lakes, including data obsolescence, unmanageable data, and misplaced data. …”
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Research on Routing Optimization in Satellite Internet Based on Deep Reinforcement Learning by Linhui WEI, Guowen LIU, Yu LIU, Yumei WANG

“…With the rapid development of satellite communication, the satellite internet is one of the core technologies of 6G network to realize global coverage, full-time access and full scene service.The high dynamics and limited capacity of satellite network lead to a series of management and control challenges such as heterogeneous network management, dynamic resource allocation and so on.Since the machine learning-based technologies have strength in network design, the intelligent architecture of software-defi ned satellite internet was put forward.In view of the intelligent routing in satellite internet, and leverages the deep reinforcement algorithm based on double delayed deep deterministic policy gradient (TD3) to solve the network routing optimization problem.The experimental results showed that compared with DDPG algorithm, the TD3 algorithm reduced the delay by 19.19%.…”
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Patent research in academic literature. Landscape and trends with a focus on patent analytics by Cristian Mejia, Yuya Kajikawa

“…The integration of advanced analytical techniques, including AI and machine learning, is observed across various domains. This study provides insights for researchers and practitioners, highlighting opportunities for cross-disciplinary collaboration and future research directions.…”
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Research on P2P streaming media identification based on UDP by Shi DONG, Gang WANG

“…Several popular P2P networks TV were studied,and their differences in port usage and packet size distribution were analyzed thoroughly.By observing the above characteristics,the conclusions that network TV application employs only one port to generate most of UDP traffic in one communicat period were summarized,and the UDP packet sizes in various network TV differ significantly.Thus,a method that can identify P2P application’s UDP traffic accurately and effectively was proposed based on extended flow records.Through identifying and verifying the five P2P streaming application traffic which was called Trace data collected from the backbone channel of CERNET (China education and research network) border in Jiangsu Province,and traffic identification results compared with machine learning algorithms show that the proposed method has a high precision rate and recall rate,high time efficient,and not susceptible to the impact of the proportion of the sample.…”
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A survey of federated learning for 6G networks by Guanglei GENG, Bo GAO, Ke XIONG, Pingyi FAN, Yang LU, Yuwei WANG

“…It is an important feature of the 6G that how to realize everything interconnection through large-scale complex heterogeneous networks based on native artificial intelligence (AI).Thanks to the distinct machine learning architecture of data processing locally, federated learning (FL) is regarded as one of the promising solutions to incorporate distributed AI in 6G scenarios, and has become a critical research direction of 6G.Therefore, the necessity of introducing distributed AI into the future 6G especially for internet of things (IoT) scenarios was analyzed.And then, the potentials of FL in meeting the 6G requirements were discussed, and the state-of-the-arts of FL related technologies such as architecture design, resource utilization, data transmission, privacy protection, and service provided for 6G were investigated.Finally, several key technical challenges and potential valuable research directions for FL-empowered 6G were put forward.…”
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Research on security architecture of strong PUF by adversarial learning by Yan LI, Wei LIU, Yuanlu SUN

“…To overcome the vulnerability of strong physical unclonable function, the adversarial learning model of strong PUF was presented based on the adversarial learning theory, then the training process of gradient descent algorithm was analyzed under the framework of the model, the potential relationship between the delay vector weight and the prediction accuracy was clarified, and an adversarial sample generation strategy was designed based on the delay vector weight.Compared with traditional strategies, the prediction accuracy of logistic regression under new strategy was reduced by 5.4% ~ 9.5%, down to 51.4%.The physical structure with low overhead was designed corresponding to the new strategy, which then strengthened by symmetrical design and complex strategy to form a new PUF architecture called ALPUF.ALPUF not only decrease the prediction accuracy of machine learning to the level of random prediction, but also resist hybrid attack and brute force attack.Compared with other PUF security structures, ALPUF has advantages in overhead and security.…”
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Crypto Currency Price Forecast: Neural Network Perspectives by Yuriy Kleban, Tetiana Stasiuk

“…The study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. …”
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Image classification with rotation-invariant variational quantum circuits by Paul San Sebastian Sein, Mikel Cañizo, Román Orús

“…Adding geometric inductive bias to the quantum models has been proposed as a potential solution to mitigate this problem, leading to a new field called geometric quantum machine learning. In this work, an equivariant architecture for variational quantum classifiers is introduced to create a label-invariant model for image classification with C_{4} rotational label symmetry. …”
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Internet traffic classification using SVM with flexible feature space by Yaguan QIAN, Xiaohui GUAN, Bensheng YUN, Qiong LOU, Pengfei MA

“…SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.…”
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Network traffic classification using decision tree based on minimum partition distance by Zhe YANG, Ling-zhi LI, Qi-jin JI, Yan-qin ZHU

“…Before data communications,every application protocol to handshake at application layer and transmit some parameters.This process is quite different according to the protocols,such as the packet direction,payload size and the information entropy of each packet payload.So according to these features,decision tree algorithm based on minimum partition distance was used to train the classifier.The results of the offline experiments on real network traces and the online classification experiments in campus network indicate that,analyzing the first four or six packets of each flow is enough to classify eight common used application protocols with high overall accuracy and low cost.Contrast to other machine learning algorithms,decision tree can achieve better ent traces and low classification time.So it is very suitable for real-time traffic classification.…”
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