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892 results on '"AHMAD, M."'

14. Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

Author

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., 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., and Zygal, L.

Abstract

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

Published
2024

22. Correction to: Feasibility and outcomes of living‑donor liver transplantation utilizing the right hemi‑liver graft with portal vein anatomical variations

Author

Shehta, Ahmed, primary, Elshobari, Mohamed, additional, Salah, Tarek, additional, Sultan, Ahmad M., additional, Yasen, Amr, additional, Shiha, Usama, additional, El-Saadany, Mohamed, additional, Monier, Ahmed, additional, Said, Rami, additional, Elsabbagh, Ahmed M, additional, Habl, Mohamed S., additional, Adly, Reham, additional, Ged, Basma Abd Elmoaem El, additional, Karam, Rasha, additional, Khaled, Reem, additional, Razek, Hassan Magdy Abd El, additional, Abdel-Khalek, Ehab E., additional, and Wahab, Mohamed Abdel, additional

Published
2024
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25. Risk Factors Associated With the Development of Acute Peripancreatic Fluid Collections on Follow-Up Imaging After Acute Pancreatitis: What Physicians Need to Know

Author

Molla, Nouran W, primary, Zaini, Renad H, additional, Alfaiz, Fahad A, additional, Alkhayatt, Ahmad M, additional, AlJohani, Majed A, additional, Alomar, Mohammed O, additional, Aljohani, Abdulaziz A, additional, BinMayouf, Mohammed S, additional, Alyamani, Abduljabbar A, additional, and Alsergani, Abdullah H, additional

Published
2023
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34. Ab Initio Investigations of Optoelectronic and Transport Behavior of Mn and Eu‐Doped ZnxAxSiGeN4 Using GGA + U Functional: A Study for Optoelectronic Devices

Author

Irfan, Muhammad, primary, Moharam, M. M., additional, Saleh, Ebraheem Abdu Musad, additional, Saeedi, Ahmad M., additional, Solre, Gideon F. B., additional, Abbas, Waseem, additional, El-Zahhar, Adel A., additional, Al-Hazmi, Gamil A. A. M., additional, Eldin, Sayed M., additional, and Asif, Sana Ullah, additional

Published
2023
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35. Exploring the Mechanical, Electronic, and Optical Properties of Gallium Based LmGaAs2 (Lm = In, Eu, Ta) Chalcopyrites Implications for Photovoltaic Applications: An ab Initio DFT Study

Author

Albalawi, Karma M., primary, Moharam, M. M., additional, Saleh, Ebraheem Abdu Musad, additional, Saeedi, Ahmad M., additional, Solre, Gideon F. B., additional, Ibrahim, Fatma A., additional, Hamdy, Mohamed S., additional, Eldin, Sayed M., additional, Irfan, Muhammad, additional, Asif, Sana Ullah, additional, and Bashir, Rabia, additional

Published
2023
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38. Feasibility and outcomes of living-donor liver transplantation utilizing the right hemi-liver graft with portal vein anatomical variations

Author

Shehta, Ahmed, primary, Elshobari, Mohamed, additional, Salah, Tarek, additional, Sultan, Ahmad M., additional, Yasen, Amr, additional, Shiha, Usama, additional, El-Saadany, Mohamed, additional, Monier, Ahmed, additional, Said, Rami, additional, Habl, Mohamed S., additional, Adly, Reham, additional, El Ged, Basma Abd Elmoaem, additional, Karam, Rasha, additional, Khaled, Reem, additional, El Razek, Hassan Magdy Abd, additional, Abdel-Khalek, Ehab E., additional, and Wahab, Mohamed Abdel, additional

Published
2023
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39. Antibacterial effects of quercetagetin are significantly enhanced upon conjugation with chitosan engineered copper oxide nanoparticles

Author

Alvi, Adeelah, primary, Alqassim, Saif, additional, Khan, Naveed Ahmed, additional, Khatoon, Bushra, additional, Akbar, Noor, additional, Kawish, Muhammad, additional, Faizi, Shaheen, additional, Shah, Muhammad Raza, additional, Alharbi, Ahmad M., additional, Alfahemi, Hasan, additional, and Siddiqui, Ruqaiyyah, additional

Published
2023
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45. Evaluation of Xpert MTB/RIF Ultra for the Diagnosis of Extrapulmonary Tuberculosis: A Retrospective Analysis in Saudi Arabia

Author

Slail, Mousa J., primary, Booq, Rayan Y., additional, Al-Ahmad, Ibrahim H., additional, Alharbi, Arwa A., additional, Alharbi, Shafi F., additional, Alotaibi, Mutlaq Z., additional, Aljubran, Abdulaziz M., additional, Aldossary, Ahmad M., additional, Memish, Ziad A., additional, Alyamani, Essam J., additional, Tawfik, Essam A., additional, and Binjomah, Abdulwahab Z., additional

Published
2023
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47. Hybrid deep learning diagonal recurrent neural network controller for nonlinear systems

Author

Ahmad M. El-Nagar, Ahmad M. Zaki, F. A. S. Soliman, and Mohammad El-Bardini

Subjects
Artificial Intelligence, Software
Abstract

In the present paper, a hybrid deep learning diagonal recurrent neural network controller (HDL-DRNNC) is proposed for nonlinear systems. The proposed HDL-DRNNC structure consists of a diagonal recurrent neural network (DRNN), whose initial values can be obtained through deep learning (DL). The DL algorithm, which is used in this study, is a hybrid algorithm that is based on a self-organizing map of the Kohonen procedure and restricted Boltzmann machine. The updating weights of the DRNN of the proposed algorithm are developed using the Lyapunov stability criterion. In this concern, simulation tasks such as disturbance signals and parameter variations are performed on mathematical and physical systems to improve the performance and the robustness of the proposed controller. It is clear from the results that the performance of the proposed controller is better than other existent controllers.

Published
2022
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