Predicting amyloid proteins using attention-based long short-term memory by Zhuowen Li

“…Recently, although several machine learning methods proposed for amyloid protein identification have shown promising results, most of them have not yet fully exploited the sequence information of the amyloid proteins. …”
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A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods by Meng Meng, Kun Zhu, Keqin Chen, Hang Qu

“…With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. …”
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Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches by Leu Ying Chen

“…With this in mind. this study looks into the usage of machine learning models to predict the yield spread between 10-year and 2-year US Treasury bonds (T10Y2Y). …”
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Education, Quality and the Digital Transformation by N. E. Serditova, A. V. Belotserkovsky

“…The necessity to develop universal competencies in the field of information and communication technologies, as well as skills for using methods of pattern recognition theory, which is fundamental for deep machine learning, is noted. Possibilities of using both Mass Open Online Courses (MOOC) and Online Program Management (OPM) services in academic programs are considered.…”
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Multimodal Interaction, Interfaces, and Communication: A Survey by Elias Dritsas, Maria Trigka, Christos Troussas, Phivos Mylonas

“…With advancements in sensor technology and machine learning (ML), multimodal systems are becoming increasingly important in various applications, including virtual assistants, intelligent environments, healthcare, and accessibility technologies. …”
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A Kernel-Based Approach for Biomedical Named Entity Recognition by Rakesh Patra, Sujan Kumar Saha

“…Support vector machine (SVM) is one of the popular machine learning techniques used in various text processing tasks including named entity recognition (NER). …”
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Systematic Review of Remote Sensing Prediction Models and Tools for Estimating Surface Soil Moisture Content of an Area by P. Yamakili, M. R. Nicholaus, K. A. Greyson

“…As a result, this systematic review paper presents a wide range of comparative assessments of RS SM estimation models and tools by assessing their technique and methods, their performance Evaluation level (Coefficient of Determination R), the environment where the model could suitably perform better and the essential parameters considered for improving the known Machine Learning models for SM prediction further attention as discussed under this paper. …”
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Geographical Origin Identification of <italic>Panax notoginseng</italic> Using a Modified K-Nearest Neighbors Model With Near-Infrared Spectroscopy by Xuefeng Cheng, Min Liao, Juan Liu

“…These results demonstrate the potential of combining spectral data processing with advanced machine learning techniques for efficient and accurate geographical origin identification, providing a robust tool for quality assurance and traceability in traditional Chinese medicine.…”
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The Norwegian Parliamentary Debates Dataset by Jon H. Fiva, Oda Nedregård, Henning Øien

“…Abstract Recent advancements in computing power and machine learning techniques have facilitated the digitization of new corpora, as well as new methods for studying high-dimensional data. …”
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Artificial intelligence in rheumatology research: what is it good for? by Diego Benavent, José Miguel Sequí-Sabater

“…Artificial intelligence (AI) is transforming rheumatology research, with a myriad of studies aiming to improve diagnosis, prognosis and treatment prediction, while also showing potential capability to optimise the research workflow, improve drug discovery and clinical trials. Machine learning, a key element of discriminative AI, has demonstrated the ability of accurately classifying rheumatic diseases and predicting therapeutic outcomes by using diverse data types, including structured databases, imaging and text. …”
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Transfer learning for linear regression with differential privacy by Yiming Hou, Yunquan Song, Zhijian Wang

“…Abstract Transfer learning, as a machine learning approach to enhance model generalization, has found widespread applications across various domains. …”
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An Efficient and Accurate 2W-PE Method for Solving Spatial Field Strength Based on CVNN and PINN by An-Qi Li, Cheng-You Yin, Qian-Qian Zhang, Han Liu

“…The proposed 2W-PE method has high computational accuracy and efficiency, which reflects the applicability of machine learning in solving the computational efficiency problem of radio wave propagation. …”
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DDoS attack detection and defense based on hybrid deep learning model in SDN by Chuanhuang LI, Yan WU, Zhengzhe QIAN, Zhengjun SUN, Weiming WANG

“…Software defined network (SDN) is a new kind of network technology,and the security problems are the hot topics in SDN field,such as SDN control channel security,forged service deployment and external distributed denial of service (DDoS) attacks.Aiming at DDoS attack problem of security in SDN,a DDoS attack detection method called DCNN-DSAE based on deep learning hybrid model in SDN was proposed.In this method,when a deep learning model was constructed,the input feature included 21 different types of fields extracted from the data plane and 5 extra self-designed features of distinguishing flow types.The experimental results show that the method has high accuracy,it’s better than the traditional support vector machine (SVM) and deep neural network (DNN) and other machine learning methods.At the same time,the proposed method can also shorten the processing time of classification detection.The detection model is deployed in SDN controller,and the new security policy is sent to the OpenFlow switch to achieve the defense against specific DDoS attack.…”
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Experiments on Detection of Voiced Hesitations in Russian Spontaneous Speech by Vasilisa Verkhodanova, Vladimir Shapranov

“…In this paper we address an issue of voiced hesitations (filled pauses and sound lengthenings) detection in Russian spontaneous speech by utilizing different machine learning techniques, from grid search and gradient descent in rule-based approaches to such data-driven ones as ELM and SVM based on the automatically extracted acoustic features. …”
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Optimizing cryptographic protocols against side channel attacks using WGAN-GP and genetic algorithms by Purushottam Singh, Prashant Pranav, Sandip Dutta

“…This study underscores the potential of leveraging machine learning and evolutionary algorithms to advance cryptographic protocol security and efficiency, laying a robust foundation for future advancements in cybersecurity.…”
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Dilated SE-DenseNet for brain tumor MRI classification by Yuannong Mao, Jiwook Kim, Lena Podina, Mohammad Kohandel

“…The study highlights the significant role of machine learning in improving diagnostic accuracy in medical imaging, with potential applications extending beyond brain tumor detection to other medical imaging tasks.…”
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Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients. by Yingjie Kuang, Zhixin Cheng, Jun Zhang, Chunxu Yang, Yue Zhang

“…Logistic regression analysis was employed to identify related factors, and machine learning algorithms were used to construct the risk prediction model.…”
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Exploring parameter dependence of atomic minima with implicit differentiation by Ivan Maliyov, Petr Grigorev, Thomas D. Swinburne

“…The implicit expansion accurately predicts lattice distortion and defect formation energies and volumes with classical and machine-learning potentials, enabling high-dimensional uncertainty propagation without prohibitive overhead. …”
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Changing States of Multistage Process Chains by Thorsten Wuest, Christopher Irgens, Klaus-Dieter Thoben

“…The novel perspective this concept introduces is that the processes and their parameters are not the priority; they are rather included in the analysis by implication. A supervised machine learning based feature ranking method is used to identify and rank relevant state characteristics and thereby the processes’ inter- and intrarelationships. …”
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How data science and AI-based technologies impact genomics by Jing Lin, Kee Yuan Ngiam

“…Following the rise of artificial intelligence (AI) technology such as machine learning and deep learning, an increasing number of GWAS/PheWAS studies have successfully leveraged this technology to overcome the aforementioned challenges. …”
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