Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications by Sicong Huang, Roozbeh Jafari, Bobak J. Mortazavi

“…<italic>Goal:</italic> To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. …”
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A Systematic Review Towards Big Data Analytics in Social Media by Md. Saifur Rahman, Hassan Reza

“…This creates the opportunity to make the "Big Social Data" handy by implementing machine learning approaches and social data analytics. …”
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Potato Quality Grading Based on Depth Imaging and Convolutional Neural Network by Qinghua Su, Naoshi Kondo, Dimas Firmanda Al Riza, Harshana Habaragamuwa

“…In this study, we developed a potato automatic grading system that uses a depth imaging system as a data collector and applies a machine learning system for potato quality grading. The depth imaging system collects 3D potato surface thickness distribution data and stores depth images for the training and validation of the machine learning system. …”
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Robust Malware identification via deep temporal convolutional network with symmetric cross entropy learning by Jiankun Sun, Xiong Luo, Weiping Wang, Yang Gao, Wenbing Zhao

“…Nowadays, researchers have made many efforts concerning supervised machine learning methods to identify malicious attacks. …”
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Accelerating antimicrobial peptide design: Leveraging deep learning for rapid discovery. by Ahmad M Al-Omari, Yazan H Akkam, Ala'a Zyout, Shayma'a Younis, Shefa M Tawalbeh, Khaled Al-Sawalmeh, Amjed Al Fahoum, Jonathan Arnold

“…Machine learning and deep learning are used to predict antimicrobial peptide efficacy in the study. …”
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Supporting Human and Machine Co-Learning in Citizen Science: Lessons From Gravity Spy by Carsten Østerlund, Kevin Crowston, Corey B. Jackson, Yunan Wu, Alexander O. Smith, Aggelos K. Katsaggelos

“…We explore the bi-directional relationship between human and machine learning in citizen science. Theoretically, the study draws on the zone of proximal development (ZPD) concept, which allows us to describe AI augmentation of human learning, human augmentation of machine learning, and how tasks can be designed to facilitate co-learning. …”
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Step-by-step causal analysis of EHRs to ground decision-making. by Matthieu Doutreligne, Tristan Struja, Judith Abecassis, Claire Morgand, Leo Anthony Celi, Gaël Varoquaux

“…Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). …”
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Enhancing Sarcopenia Prediction Through an Ensemble Learning Approach: Addressing Class Imbalance for Improved Clinical Diagnosis by Dilmurod Turimov, Wooseong Kim

“…The study focused on enhancing model performance by combining various machine learning methods and addressing critical challenges, such as class imbalance and data complexity. …”
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Kombinasi Feature Selection Fisher Score dan Principal Component Analysis (PCA) untuk Klasifikasi Cervix Dysplasia by Krisan Aprian Widagdo, Kusworo Adi, Rahmat Gernowo

“…Pengamatan tersebut membutuhkan sumber daya yang besar. Dalam hal ini machine learning dapat mengatasi masalah tersebut. Akan tetapi, keakuratan machine learning bergantung pada fitur yang digunakan. …”
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Adversarial measurements for convolutional neural network-based energy theft detection model in smart grid by Santosh Nirmal, Pramod Patil, Sagar Shinde

“…Recent studies reveal that machine learning and deep learning models are vulnerable. …”
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A Review of Traffic Congestion Prediction Using Artificial Intelligence by Mahmuda Akhtar, Sara Moridpour

“…In recent years, traffic congestion prediction has led to a growing research area, especially of machine learning of artificial intelligence (AI). With the introduction of big data by stationary sensors or probe vehicle data and the development of new AI models in the last few decades, this research area has expanded extensively. …”
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Prediction of Ubiquitination Sites Using UbiNets by Sarthak Yadav, Manoj Gupta, Ankur Singh Bist

“…Ubiquitination controls the activity of various proteins and belongs to posttranslational modification. Various machine learning techniques are taken for prediction of ubiquitination sites in protein sequences. …”
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D2D computation task offloading for efficient federated learning by Xiaoran CAI, Xiaopeng MO, Jie XU

“…Federated learning is a kind of distributed machine learning technique.The factor of communication and computation resource constraints at the edge node is becoming the performance bottleneck.In particular,when different edge node has distinct computation and communication capabilities,the model training performance may degrade severely,thus necessitating the joint communication and computation optimization.To tackle this challenge,a computational task offloading scheme enabled by device-to-device (D2D) communications was proposed,in which different edge node exchanged data samples via D2D communication links to balance the processing capability and task load,in order to minimize the total time delay for machine learning model training.Simulation results show that compared to the benchmark scheme without such D2D task offloading the training speed and efficiency of federated learning has be improved significantly.…”
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PhD Thesis on AI: a New Challenge of the Digital Era by A. N. Alfimtsev, N. G. Bagdasaryan, S. A. Sakulin

“…Artificial Intelligence and Machine Learning” is presented. The issues of graduate school management and regulatory barriers in the training of young scientists are considered. …”
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Harmonic Classification with Enhancing Music Using Deep Learning Techniques by Wen Tang, Linlin Gu

“…Two aspects of harmony are considered, chord and global key, facing the issue of the extraction problem by the algorithm of machine learning. Contribution here is to recognize chords in the music by the feature extraction method (voiced models) that performd better than manually one. …”
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Analisis Sentimen Berbahasa Inggris Dengan Metode Lstm Studi Kasus Berita Online Pariwisata Bali by Ngakan Made Krisna Sedana, I Nyoman Saputra Wahyu Wijaya, I Ketut Resika Arthana

“…Analisis sentimen dilakukan menggunakan model machine learning LSTM (Long-Term Memory), dengan data berita yang telah diberi label sentimen oleh pakar sesuai dengan isi berita. …”
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Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study by Marceli Wac, Raul Santos-Rodriguez, Chris McWilliams, Christopher Bourdeaux

“…While some tasks can be addressed by training machine learning models directly on the collected data, more complex problems require additional input in the form of data annotations. …”
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Logical reasoning for human activity recognition based on multisource data from wearable device by Mahmood Alsaadi, Ismail Keshta, Janjhyam Venkata Naga Ramesh, Divya Nimma, Mohammad Shabaz, Nirupma pathak, Pavitar Parkash Singh, Sherzod Kiyosov, Mukesh Soni

“…Additionally, the suggested strategy dramatically minimises the quantity of user-provided training data needed in comparison to machine learning-based behaviour identification techniques.…”
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Forecasting mental states in schizophrenia using digital phenotyping data. by Thierry Jean, Rose Guay Hottin, Pierre Orban

“…The promise of machine learning successfully exploiting digital phenotyping data to forecast mental states in psychiatric populations could greatly improve clinical practice. …”
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Dual Strategy Based Improved Swarm Intelligence and Stacked LSTM With Residual Connection for Land Use Land Cover Classification by Vinaykumar Vajjanakurike Nagaraju, Ananda Babu Jayachandra, Andrzej Stateczny, Swathi Holalu Yogesh, Raviprakash Madenur Lingaraju, Balaji Prabhu Baluvaneralu Veeranna

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