Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study by Dhingra, LS, Aminorroaya, A, Sangha, V, Pedroso, AF, Asselbergs, FW, Brant, LCC, Barreto, SM, Ribeiro, ALP, Krumholz, HM, Oikonomou, EK, Khera, R

“…Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. …”

Extreme learning machines for feature learning by Liyanaarachchi Lekamalage, Chamara Kasun

“…Hence this thesis extends ELM for feature learning. This thesis introduces an ELM based feature learning framework with linear hidden layer activation function referred to as linear Extreme Learning Machine Auto-Encoder (ELM-AE) and linear Sparse Extreme Learning Machine Auto-Encoder (SELM-AE); ELM-AE and SELM-AE with sigmoid hidden layer activation function referred to as non-linear ELM-AE and non-linear SELM-AE. …”
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No Simple Fix: How AI Harms Reflect Power and Jurisdiction in the Workplace by Nedzhvetskaya, Nataliya, Tan, JS

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L’urgenza del suono come espressione propria: conversazione con Anna Oxa by Ammaturo, Francesca Romana

“…In questa conversazione la cantante italiana Anna Oxa riflette sulla sua carriera, sulla sua relazione con i fan, e sugli elementi chiave della sua pratica artistica, come ad esempio l’importanza di creare la propria musica al di là delle pressioni del mondo della celebrità, i dettami dell’industria musicale, o le aspettative tradizionali di genere che esistono nei confronti delle artiste donne nel mondo della musica. …”
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Indoor occupancy estimation using environmental parameters by Masood, Khalid Mustafa

“…The FS-ELM is in fact a novel architecture of the ELM, in which a feature layer is added to the standard ELM. …”
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Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter by Abadjiev, D., Adams, T., Adzic, P., Ahmad, M., Amendola, C., Andrews, M. B., Arcidiacono, R., Argiro, S., Askew, A., Auffray, E., Azzolini, V., Bailleux, D., Band, R., Barney, D., Barria, P., Bartosik, N., Basile, C., Bastos, D., Bell, K. W., Besancon, M., Bianco, R., Biino, C., Blinov, V., Borca, C., Bornheim, A., Brown, R. M., Campana, M., Castells, S., Cavallari, F., Cetorelli, F., Chatterjee, R. M., Chatterjee, S., Chaudhary, G., Chen, J. A., Chernyavskaya, N., Chung, H., Cipriani, M., Cokic, L., Cooke, C., Cossio, F., Couderc, F., Cristoforetti, D., Cucciati, G., Cunqueiro Mendez, L., Da Silva Di Calafiori, D., Dafinei, I., Cockerill, D. J. A., Dejardin, M., Re, D. Del, Ricca, G. Della, Depasse, P., Dervan, J., Marco, E. Di, Diemoz, M., Dimova, T., Dissertori, G., Dittmar, M., Dolgopolov, A., Donegà, M., Dordevic, M., Mamouni, H. El, Errico, F., Espinosa, F., Faure, J. L., Fay, J., Menendez, J. Fernandez, Ferri, F., Finco, L., Fiori, F., Frahm, E., Funk, W., Gadek, T., Gajownik, J., Galli, M., Ganjour, S., Gascon, S., Ghezzi, A., Ghose, P., Gninenko, S., Goadhouse, S., Godinovic, N., Golubev, N., Govoni, P., Gras, P., Hakala, J., de Monchenault, G. Hamel, Harilal, A., Härringer, N., Hashmi, R., Heath, H. F., Hirosky, R., Ho, K. W., Hou, X., Ingram, Q., Jain, Sh., Javaid, T., Jessop, C., Jimènez, R., Joshi, B. M., Jourd‘hui, E., Kaadze, K., Kao, Y.-W., Kardapoltsev, L., Khurana, R., King, J., Kirilovas, A., Konstantinov, D., Kovac, M., Krishna, A., Kuo, C. M., Lambrecht, L., Lavizzari, G., Lecoq, P., Ledovskoy, A., Legger, F., Lelas, D., Li, Y. y., Liang, Z., Lin, W., Longo, E., Loukas, N., Lu, R. -S., Lustermann, W., Lutton, L., Lyon, A. -M., Maeshima, K., Malcles, J., Mandrik, P., Manzoni, R. A., Marchese, L., Marinelli, N., Marini, A. C., Martin, L., Marzocchi, B., Mascellani, A., Massironi, A., Matveev, V., Mazza, G., Meridiani, P., Mijic, M., Mijuskovic, J., Milenovic, P., Milosevic, J., Monteno, M., Monti, F., Moortgat, F., Mousa, J., Mudholkar, T., Nessi-Tedaldi, F., Nicolaou, C., Nigamova, A., Obertino, M. M., Organtini, G., Orimoto, T., Orlandi, F., Ovtin, I., Paganis, E., Papagiannis, D., Pandolfi, F., Paramatti, R., Park, K., Pastrone, N., Paulini, M., Pauss, F., Petkovic, , A., Petraityte, E., Pettinacci, V., Petyt, D., Pigazzini, S., Pinolini, B. S., Prova, P. R., Quaranta, C., Ragazzi, S., Rahatlou, S., Rasteiro Da Silva, J. C., Razis, P. A., Teles, P. Rebello, Reis, T., Riti, F., Rogan, C., Romanteau, T., Rosowsky, A., Rovelli, C., Rovere, M., Rusack, R., Salvi, G., Sancar, O., Sanchez, A., Sandever, C., Santanastasio, F., Saradhy, R., Sarkar, U., Schneider, M., Schroeder, N., Sculac, A., Sculac, T., Shahzad, M. A., Shepherd-Themistocleous, C. H., Simkina, P., Singla, A., Singovsky, A., Skovpen, Y., Smith, V. J., Soffi, L., Stachon, K., Steen, A., Steggemann, J., Succar, M., Tao, J., Tishelman-Charny, A., Tiwari, P. C., Tornago, M., Tramontano, R., Tsai, L. -S., Usai, E., Valsecchi, D., Vagnerini, A., Varela, J., Venditti, R., Verma, P., Vlassov, E., Wachirapusitanand, V., Wamorkar, T., Wang, C., Wang, J., Wadud, M. A., Yu, S. S., Zabi, A., Zghiche, A., Zhang, L., Zhu, R. Y., Zygal, L.

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Weighted online sequential extreme learning machine for class imbalance learning by Lin, Zhiping, Mirza, Bilal., Toh, Kar-Ann.

“…In this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) algorithm for class imbalance learning (CIL). …”
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Further studies of extreme learning machine and compressed signal detection by Cao, Jiuwen

“…In Chapter 1, we give literature reviews of extreme learning machine (ELM) and compressed sensing (CS). In part I of the thesis (Chapters 2, 3, and 4), we consider the recent ELM for training neural networks and present several improved algorithms. …”
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Extreme learning machine for classification and clustering on multiview data by Chen, Jichao

“…The ELM auto-encoder (ELM-AE) is adopted for feature extraction and feature fusion, and an ELM classifier is ultilized for the final classification. …”
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Inferno XVI: from the circling sodomites to Geryon’s cord by Gilson, S

“…Questa lectura analizza le principali sezioni del canto XVI dell’Inferno con particolare attenzione al dialogo fra Dante personaggio e i tre dannati fiorentini, al trattamento della sodomia nella cultura medievale e ai suoi contesti teologici, e al ruolo della profezia in tale canto. …”

Development of localization algorithms for a WiFi based indoor positioning system with machine learning techniques by Lu, Xiaoxuan

“…We model the indoor positioning problem under a non-parametric stochastic framework, and modify the well-known ML tool, extreme learning machine (ELM), to achieve the above goal. Firstly, under the assumption that noises merely lie in input data, we modify ELM by introducing a dead zone, which is called DZ-ELM, and integrate it into our IPS. …”
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Extreme learning machine based speaker recognition by Hu, Zongjiang.

“…Finally and most importantly, the performance of ELM and SVM will be compared. With the results shown in Chapter 6, we can draw the conclusion that ELM is superior to SVM in terms of tuning simplicity, time efficiency and testing accuracy.…”
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Enhanced extreme learning machines for image classification by Cui, Dongshun

“…Among numerous machine learning methods, we choose the Extreme Learning Machine (ELM) for our image classification applications. …”
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Sequential learning for extreme learning machine by Liang, Nanying

“…A novel sequential learning algorihtm for training Single Hidden Layer Feedforward Neural Network (SLFN), Online Sequential Extreme Learning Machine (OS-ELM) is proposed. OS-ELM is based on the combination of Extreme Learning Machine (ELM) and the recursive least-squares (RLS) algorithm. …”
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Short-term load forecasting by wavelet transform and evolutionary extreme learning machine by Li, Song, Wang, Peng, Goel, Lalit

“…Each component of the load series is then separately forecasted by a hybrid model of ELM and MABC (ELM-MABC). The global search technique MABC is developed to find the best parameters of input weights and hidden biases for ELM. …”
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ADC architectures for low power analog machine learning by David Bose, Christin

“…First, a 1-bit ADC (current comparator) is designed for application in a novel ELM based conditional branch prediction system designed for pipelined processors. …”
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EEG based mind controlled car by Li, Yue

“…Furthermore, in order to demo the result, we built a remote control car which controlled by the classification result from ELM classifier.…”
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Emotional states classification from brain signals by Lee, Vinson Bing Jun.

“…Results from the experimental data have indicated that Kernel ELM is the best option as opposed to other SVM variants. …”
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Extreme learning machine based fast object recognition by Xu, Jiantao, Zhou, Hongming, Huang, Guang-Bin

“…Besides, the parameter tuning process for ELM is much easier than SVM as well.…”
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Low-power processors for implantable epileptic seizure detection system by Baihaqi, Muhammad Rayhan

“…Extreme Learning Machine (ELM) has gained some attentions recently, due to the fact that learning speed of ELM is really fast compared to the traditional learning algorithm. …”
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