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28 results on '"Ibitoye, Ayodeji"'

1. Cross-correlation between the thermal Sunyaev-Zeldovich effect and the Integrated Sachs-Wolfe effect

Author

Ibitoye, Ayodeji, Dai, Wei-Ming, Ma, Yin-Zhe, Vielva, Patricio, Tramonte, Denis, Abebe, Amare, Beesham, Aroonkumar, and Chen, Xuelei

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We present a joint cosmological analysis of the power spectra measurement of the Planck Compton parameter and the integrated Sachs-Wolfe (ISW) maps. We detect the statistical correlation between the Planck Thermal Sunyaev-Zeldovich (tSZ) map and ISW data with a significance of a $3.6\sigma$ confidence level~(CL), with the autocorrelation of the Planck tSZ data being measured at a $25 \sigma$ CL. The joint auto- and cross-power spectra constrain the matter density to be $\Omega_{\rm m}= 0.317^{+0.040}_{-0.031}$, the Hubble constant $H_{0}=66.5^{+2.0}_{-1.9}\,{\rm km}\,{\rm s}^{-1}\,{\rm Mpc}^{-1}$ and the rms matter density fluctuations to be $\sigma_{8}=0.730^{+0.040}_{-0.037}$ at the 68% CL. The derived large-scale structure $S_{8}$ parameter is $S_8 \equiv \sigma_{8}(\Omega_{\rm m}/0.3)^{0.5} = 0.755\pm{0.060} $. If using only the diagonal blocks of covariance matrices, the Hubble constant becomes $H_{0}=69.7^{+2.0}_{-1.5}\,{\rm km}\,{\rm s}^{-1}\,{\rm Mpc}^{-1}$. In addition, we obtain the constraint of the product of the gas bias, gas temperature, and density as $b_{\rm gas} \left(T_{\rm e}/(0.1\,{\rm keV}) \right ) \left(\bar{n}_{\rm e}/1\,{\rm m}^{-3} \right) = 3.09^{+0.320}_{-0.380}$. We find that this constraint leads to an estimate on the electron temperature today as $T_{\rm e}=(2.40^{+0.250}_{-0.300}) \times 10^{6} \,{\rm K}$, consistent with the expected temperature of the warm-hot intergalactic medium. Our studies show that the ISW-tSZ cross-correlation is capable of probing the properties of the large-scale diffuse gas., Comment: 25 pages, 15 figures, 2 tables

Published
2023

2. Suicide Ideation Prediction Through Deep Learning: An Integration of CNN and Bidirectional LSTM with Word Embeddings

Author

Oyewale, Christianah T., Ibitoye, Ayodeji O. J., Akinyemi, Joseph D., Onifade, Olufade F. W., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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3. Predicting Suicide Ideation from Social Media Text Using CNN-BiLSTM

Author

Oyewale, Christianah T., Akinyemi, Joseph D., Ibitoye, Ayodeji O.J, Onifade, Olufade F.W, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Patel, Kanubhai K., editor, Santosh, KC, editor, and Patel, Atul, editor

Published
2024
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4. Cross Correlation between the Thermal Sunyaev-Zel'dovich Effect and Projected Galaxy Density Field

Author

Ibitoye, Ayodeji, Tramonte, Denis, Ma, Yin-Zhe, and Dai, Wei-Ming

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We present a joint analysis of the power spectra of the Planck Compton $y$-parameter map and the projected galaxy density field using the Wide Field Infrared Survey Explorer (WISE) all-sky survey. We detect the statistical correlation between WISE and Planck data (g$y$) with a significance of $21.8\,\sigma$. We also measure the auto-correlation spectrum for the tSZ ($yy$) and the galaxy density field maps (gg) with a significance of $150\,\sigma$ and $88\,\sigma$, respectively. We then construct a halo model and use the measured correlations $C^{\rm gg}_{\ell}$, $C^{yy}_{\ell}$ and $C^{{\rm g}y}_{\ell}$ to constrain the tSZ mass bias $B\equiv M_{500}/M^{\rm tSZ}_{500}$. We also fit for the galaxy bias, which is included with explicit redshift and multipole dependencies as $b_{\rm g}(z,\ell)=b_{\rm g}^0(1+z)^{\alpha}(\ell/\ell_0)^{\beta}$, with $\ell_0=117$. We obtain the constraints to be $B =1.50{\pm 0.07}\,(\textrm{stat}) \pm{0.34}\,(\textrm{sys})$, i.e. $1-b_{\rm H}=0.67\pm 0.03\,({\rm stat})\pm 0.16\,({\rm sys})$ (68\% confidence level) for the hydrostatic mass bias, and $b_{\rm g}^0=1.28^{+0.03}_{-0.04}\,(\textrm{stat}) \pm{0.11}\,(\textrm{sys})$, with $\alpha=0.20^{+0.11}_{-0.07}\,(\textrm{stat}) \pm{0.10}\,(\textrm{sys})$ and $\beta=0.45{\pm 0.01}\,(\textrm{stat}) \pm{0.02}\,(\textrm{sys})$ for the galaxy bias. Incoming data sets from future CMB and galaxy surveys (e.g. Rubin Observatory) will allow probing the large-scale gas distribution in more detail., Comment: 24 pages, 9 figures, 4 tables, accepted in the Astrophysical Journal

Published
2022
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5. Snowmass2021 Cosmic Frontier: Cosmic Microwave Background Measurements White Paper

Author

Chang, Clarence L., Huffenberger, Kevin M., Benson, Bradford A., Bianchini, Federico, Chluba, Jens, Delabrouille, Jacques, Flauger, Raphael, Hanany, Shaul, Jones, William C., Kogut, Alan J., McMahon, Jeffrey J., Meyers, Joel, Sehgal, Neelima, Simon, Sara M., Umilta, Caterina, Abazajian, Kevork N., Ahmed, Zeeshan, Akrami, Yashar, Anderson, Adam J., Ansarinejad, Behzad, Austermann, Jason, Baccigalupi, Carlo, Barkats, Denis, Barron, Darcy, Barry, Peter S., Battaglia, Nicholas, Baxter, Eric, Beck, Dominic, Bender, Amy N., Bennett, Charles, Beringue, Benjamin, Bischoff, Colin, Bleem, Lindsey, Bock, James, Bolliet, Boris, Bond, J Richard, Borrill, Julian, Brinckmann, Thejs, Brown, Michael L., Calabrese, Erminia, Carlstrom, John, Challinor, Anthony, Chang, Chihway, Chinone, Yuji, Clark, Susan E., Coulton, William, Cukierman, Ari, Cyr-Racine, Francis-Yan, Duff, Shannon M., Dvorkin, Cora, van Engelen, Alexander, Errard, Josquin, Eskilt, Johannes R., Essinger-Hileman, Thomas, Fabbian, Giulio, Feng, Chang, Ferraro, Simone, Filippini, Jeffrey, Freese, Katherine, Galitzki, Nicholas, Gawiser, Eric, Grin, Daniel, Grohs, Evan, Gruppuso, Alessandro, Gudmundsson, Jon E., Halverson, Nils W., Hamilton, Jean-Christophe, Harrington, Kathleen, Henrot-Versillé, Sophie, Hensley, Brandon, Hill, J. Colin, Hincks, Adam D., Hlozek, Renee, Holzapfel, William, Hotinli, Selim C., Hui, Howard, Ibitoye, Ayodeji, Johnson, Matthew, Johnson, Bradley R., Kang, Jae Hwan, Karkare, Kirit S., Knox, Lloyd, Kovac, John, Lau, Kenny, Legrand, Louis, Loverde, Marilena, Lubin, Philip, Ma, Yin-Zhe, Mroczkowski, Tony, Mukherjee, Suvodip, Münchmeyer, Moritz, Nagai, Daisuke, Nagy, Johanna, Niemack, Michael, Novosad, Valentine, Omori, Yuuki, Orlando, Giorgio, Pan, Zhaodi, Perotto, Laurence, Petroff, Matthew A., Pogosian, Levon, Pryke, Clem, Rahlin, Alexandra, Raveri, Marco, Reichardt, Christian L., Remazeilles, Mathieu, Rephaeli, Yoel, Ruhl, John, Schaan, Emmanuel, Shandera, Sarah, Shimon, Meir, Soliman, Ahmed, Stark, Antony A., Starkman, Glenn D., Stompor, Radek, Thakur, Ritoban Basu, Trendafilova, Cynthia, Tristram, Matthieu, Trivedi, Pranjal, Tucker, Gregory, Di Valentino, Eleonora, Vieira, Joaquin, Vieregg, Abigail, Wang, Gensheng, Watson, Scott, Wenzl, Lukas, Wollack, Edward J., Wu, W. L. Kimmy, Xu, Zhilei, Zegeye, David, and Zhang, Cheng

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This is a solicited whitepaper for the Snowmass 2021 community planning exercise. The paper focuses on measurements and science with the Cosmic Microwave Background (CMB). The CMB is foundational to our understanding of modern physics and continues to be a powerful tool driving our understanding of cosmology and particle physics. In this paper, we outline the broad and unique impact of CMB science for the High Energy Cosmic Frontier in the upcoming decade. We also describe the progression of ground-based CMB experiments, which shows that the community is prepared to develop the key capabilities and facilities needed to achieve these transformative CMB measurements., Comment: contribution to Snowmass 2021

Published
2022

6. Snowmass 2021 CMB-S4 White Paper

Author

Abazajian, Kevork, Abdulghafour, Arwa, Addison, Graeme E., Adshead, Peter, Ahmed, Zeeshan, Ajello, Marco, Akerib, Daniel, Allen, Steven W., Alonso, David, Alvarez, Marcelo, Amin, Mustafa A., Amiri, Mandana, Anderson, Adam, Ansarinejad, Behzad, Archipley, Melanie, Arnold, Kam S., Ashby, Matt, Aung, Han, Baccigalupi, Carlo, Baker, Carina, Bakshi, Abhishek, Bard, Debbie, Barkats, Denis, Barron, Darcy, Barry, Peter S., Bartlett, James G., Barton, Paul, Thakur, Ritoban Basu, Battaglia, Nicholas, Beall, Jim, Bean, Rachel, Beck, Dominic, Belkner, Sebastian, Benabed, Karim, Bender, Amy N., Benson, Bradford A., Besuner, Bobby, Bethermin, Matthieu, Bhimani, Sanah, Bianchini, Federico, Biquard, Simon, Birdwell, Ian, Bischoff, Colin A., Bleem, Lindsey, Bocaz, Paulina, Bock, James J., Bocquet, Sebastian, Boddy, Kimberly K., Bond, J. Richard, Borrill, Julian, Bouchet, Francois R., Brinckmann, Thejs, Brown, Michael L., Bryan, Sean, Buza, Victor, Byrum, Karen, Calabrese, Erminia, Calafut, Victoria, Caldwell, Robert, Carlstrom, John E., Carron, Julien, Cecil, Thomas, Challinor, Anthony, Chan, Victor, Chang, Clarence L., Chapman, Scott, Charles, Eric, Chauvin, Eric, Cheng, Cheng, Chesmore, Grace, Cheung, Kolen, Chinone, Yuji, Chluba, Jens, Cho, Hsiao-Mei Sherry, Choi, Steve, Clancy, Justin, Clark, Susan, Cooray, Asantha, Coppi, Gabriele, Corlett, John, Coulton, Will, Crawford, Thomas M., Crites, Abigail, Cukierman, Ari, Cyr-Racine, Francis-Yan, Dai, Wei-Ming, Daley, Cail, Dart, Eli, Daues, Gregorg, de Haan, Tijmen, Deaconu, Cosmin, Delabrouille, Jacques, Derylo, Greg, Devlin, Mark, Di Valentino, Eleonora, Dierickx, Marion, Dober, Brad, Doriese, Randy, Duff, Shannon, Dutcher, Daniel, Dvorkin, Cora, Dünner, Rolando, Eftekhari, Tarraneh, Eimer, Joseph, Bouhargani, Hamza El, Elleflot, Tucker, Emerson, Nick, Errard, Josquin, Essinger-Hileman, Thomas, Fabbian, Giulio, Fanfani, Valentina, Fasano, Alessandro, Feng, Chang, Ferraro, Simone, Filippini, Jeffrey P., Flauger, Raphael, Flaugher, Brenna, Fraisse, Aurelien A., Frisch, Josef, Frolov, Andrei, Galitzki, Nicholas, Gallardo, Patricio A., Galli, Silvia, Ganga, Ken, Gerbino, Martina, Giannakopoulos, Christos, Gilchriese, Murdock, Gluscevic, Vera, Goeckner-Wald, Neil, Goldfinger, David, Green, Daniel, Grimes, Paul, Grin, Daniel, Grohs, Evan, Gualtieri, Riccardo, Guarino, Vic, Gudmundsson, Jon E., Gullett, Ian, Guns, Sam, Habib, Salman, Haller, Gunther, Halpern, Mark, Halverson, Nils W., Hanany, Shaul, Hand, Emma, Harrington, Kathleen, Hasegawa, Masaya, Hasselfield, Matthew, Hazumi, Masashi, Heitmann, Katrin, Henderson, Shawn, Hensley, Brandon, Herbst, Ryan, Hervias-Caimapo, Carlos, Hill, J. Colin, Hills, Richard, Hivon, Eric, Hlozek, Renée, Ho, Anna, Holder, Gil, Hollister, Matt, Holzapfel, William, Hood, John, Hotinli, Selim, Hryciuk, Alec, Hubmayr, Johannes, Huffenberger, Kevin M., Hui, Howard, nez, Roberto Ibá, Ibitoye, Ayodeji, Ikape, Margaret, Irwin, Kent, Jacobus, Cooper, Jeong, Oliver, Johnson, Bradley R., Johnstone, Doug, Jones, William C., Joseph, John, Jost, Baptiste, Kang, Jae Hwan, Kaplan, Ari, Karkare, Kirit S., Katayama, Nobuhiko, Keskitalo, Reijo, King, Cesiley, Kisner, Theodore, Klein, Matthias, Knox, Lloyd, Koopman, Brian J., Kosowsky, Arthur, Kovac, John, Kovetz, Ely D., Krolewski, Alex, Kubik, Donna, Kuhlmann, Steve, Kuo, Chao-Lin, Kusaka, Akito, Lähteenmäki, Anne, Lau, Kenny, Lawrence, Charles R., Lee, Adrian T., Legrand, Louis, Leitner, Matthaeus, Leloup, Clément, Lewis, Antony, Li, Dale, Linder, Eric, Liodakis, Ioannis, Liu, Jia, Long, Kevin, Louis, Thibaut, Loverde, Marilena, Lowry, Lindsay, Lu, Chunyu, Lubin, Phil, Ma, Yin-Zhe, Maccarone, Thomas, Madhavacheril, Mathew S., Maldonado, Felipe, Mantz, Adam, Marques, Gabriela, Matsuda, Frederick, Mauskopf, Philip, May, Jared, McCarrick, Heather, McCracken, Ken, McMahon, Jeffrey, Meerburg, P. Daniel, Melin, Jean-Baptiste, Menanteau, Felipe, Meyers, Joel, Millea, Marius, Miranda, Vivian, Mitchell, Don, Mohr, Joseph, Moncelsi, Lorenzo, Monzani, Maria Elena, Moshed, Magdy, Mroczkowski, Tony, Mukherjee, Suvodip, Münchmeyer, Moritz, Nagai, Daisuke, Nagarajappa, Chandan, Nagy, Johanna, Namikawa, Toshiya, Nati, Federico, Natoli, Tyler, Nerval, Simran, Newburgh, Laura, Nguyen, Hogan, Nichols, Erik, Nicola, Andrina, Niemack, Michael D., Nord, Brian, Norton, Tim, Novosad, Valentine, O'Brient, Roger, Omori, Yuuki, Orlando, Giorgio, Osherson, Benjamin, Osten, Rachel, Padin, Stephen, Paine, Scott, Partridge, Bruce, Patil, Sanjaykumar, Petravick, Don, Petroff, Matthew, Pierpaoli, Elena, Pilleux, Mauricio, Pogosian, Levon, Prabhu, Karthik, Pryke, Clement, Puglisi, Giuseppe, Racine, Benjamin, Raghunathan, Srinivasan, Rahlin, Alexandra, Raveri, Marco, Reese, Ben, Reichardt, Christian L., Remazeilles, Mathieu, Rizzieri, Arianna, Rocha, Graca, Roe, Natalie A., Rotermund, Kaja, Roy, Anirban, Ruhl, John E., Saba, Joe, Sailer, Noah, Salatino, Maria, Saliwanchik, Benjamin, Sapozhnikov, Leonid, Rao, Mayuri Sathyanarayana, Saunders, Lauren, Schaan, Emmanuel, Schillaci, Alessandro, Schmitt, Benjamin, Scott, Douglas, Sehgal, Neelima, Shandera, Sarah, Sherwin, Blake D., Shirokoff, Erik, Shiu, Corwin, Simon, Sara M., Singari, Baibhav, Slosar, Anze, Spergel, David, Germaine, Tyler St., Staggs, Suzanne T., Stark, Antony A., Starkman, Glenn D., Steinbach, Bryan, Stompor, Radek, Stoughton, Chris, Suzuki, Aritoki, Tajima, Osamu, Tandoi, Chris, Teply, Grant P., Thayer, Gregg, Thompson, Keith, Thorne, Ben, Timbie, Peter, Tomasi, Maurizio, Trendafilova, Cynthia, Tristram, Matthieu, Tucker, Carole, Tucker, Gregory, Umiltà, Caterina, van Engelen, Alexander, van Marrewijk, Joshiwa, Vavagiakis, Eve M., Vergès, Clara, Vieira, Joaquin D., Vieregg, Abigail G., Wagoner, Kasey, Wallisch, Benjamin, Wang, Gensheng, Wang, Guo-Jian, Watson, Scott, Watts, Duncan, Weaver, Chris, Wenzl, Lukas, Westbrook, Ben, White, Martin, Whitehorn, Nathan, Wiedlea, Andrew, Williams, Paul, Wilson, Robert, Winch, Harrison, Wollack, Edward J., Wu, W. L. Kimmy, Xu, Zhilei, Yefremenko, Volodymyr G., Yu, Cyndia, Zegeye, David, Zivick, Jeff, and Zonca, Andrea

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

This Snowmass 2021 White Paper describes the Cosmic Microwave Background Stage 4 project CMB-S4, which is designed to cross critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. We provide an overview of the science case, the technical design, and project plan., Comment: Contribution to Snowmass 2021. arXiv admin note: substantial text overlap with arXiv:1908.01062, arXiv:1907.04473

Published
2022

11. Heart failure and diabetes: Understanding the bidirectional relationship

Author

Elendu, Chukwuka, primary, Amaechi, Dependable C., additional, Elendu, Tochi C., additional, Ashna, Manizha, additional, Ross-Comptis, Jennifer, additional, Ansong, Sandra O., additional, Egbunu, Emmanuel O., additional, Okafor, Geraldine C., additional, Jingwa, Klein A., additional, Akintunde, Akinbayo A., additional, Ogah, Chidinma M., additional, Edeko, Melissa O., additional, Ibitoye, Ayodeji V., additional, Ogunseye, Mojetoluwa O., additional, Alakwe-Ojimba, Chisom E., additional, Omeludike, Eunice K., additional, Oguine, Chibuike A., additional, Afuh, Rechner N., additional, Olawuni, Clinton A., additional, Ekwem, Onyekachukwu R., additional, Oyedele, Boluwatife A., additional, Pius, Erica I., additional, Asekhauno, Mercy O., additional, Ladele, John A., additional, Okoro, Chinonso B., additional, Monika Pouekoua, Brandon C., additional, Adenikinju, Joseph S., additional, Agu-ben, Chigozirim M., additional, and Aborisade, Ooreofe, additional

Published
2023
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13. Conceptual Voice Based Querying Support Model for Relevant Document Retrieval

Author

Onifade, Olufade F. W., Ibitoye, Ayodeji O. J., Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Snášel, Václav, editor, Abraham, Ajit, editor, Krömer, Pavel, editor, Pant, Millie, editor, and Muda, Azah Kamilah, editor

Published
2016
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14. Analysis of the Emotional and Psychological Health of the Nigerian Military During COVID-19

Author

Oyewale, Christianah T. and Ibitoye, Ayodeji O.

Subjects
Nigerian Military, COVID-19, Psychology, Emotional, Power BI
Abstract

This article considers the analysis of the effect COVID-19 had on Nigerian military personnel in terms of their psychological and emotional well-being. It identifies the reasons why outside there; the military personnel are termed rich which causes them to be a low priority during the era of COVID-19. Such affected them psychologically and emotionally. The methodology for the study revolves around the hybrid of questionnaires and interviews. Primary data coming from the methodology employed were analyzed and to consolidate responses from questionnaires, stakeholders considered relevant to the study were interviewed. Results analysis was carried out, using Microsoft Power Business Intelligence software. The study’s major finding revealed that despite the work-at-home policies occasioned by the COVID-19 phenomenon, the Nigerian Military was still going to work making them more vulnerable to COVID-19 infection. The study also found out that the distribution of COVID-19 relief was given more to the Non-military than the Military which exacerbated negative emotional and psychological effects on them. Despondently, the Nigerian military was considered as being rich and this led to less than satisfactory attention on mitigating the effect of COVID-19 on them despite their unrelenting efforts in keeping the peace of the country. Based on the findings of this study, the study concludes that the Nigerian Military should be considered for better attention in the event of a pandemic such as COVID-19 for the sake of their psychological and emotional stability.

Published
2023
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15. Job recommendation model using association rule on applicants' contextualised data

Author

Ibitoye, Ayodeji O.J., Abiodun, Adeyinka O., Siyanbade, Adeoluwa M., Oladimeji, Tijesu O., and Oyewale, Christianah Titilope

Abstract

Globally, the growing number of graduates is outpacing the availability of job opportunities. Not all degree holders possess the desired skills that align with their obtained degree for employment purposes, Hence, applicants are faced with the challenges of determining, which skillsets and earned degree align with their desired job positions. Here, a job dataset of 26705 samples, which contained user profile, and job descriptions were mined in context through association rule to recommend applicants next job opportunity using distinct and optimal hyperparameter of high confidence, and lift set as thresholds on different runs for validation. Overall experiments and evaluations showed a stronger positive association between the antecedents and consequents over a higher lift value using association mining when compared with apriori mining. The research presented sample exploratory information as outputs, highlighting the potential of association rule mining in bridging the gap between applicants' skill sets, degrees and desired jobs.

Published
2024
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22. Cross Correlation between the Thermal Sunyaevâ€"Zel’dovich Effect and Projected Galaxy Density Field.

Author

Ibitoye, Ayodeji, Tramonte, Denis, Ma, Yin-Zhe, and Dai, Wei-Ming

Subjects
CROSS correlation, COSMIC background radiation, GAS distribution, STATISTICAL correlation, GALAXIES, POWER spectra
Abstract

We present a joint analysis of the power spectra of the Planck Compton y parameter map and the projected galaxy density field using the Wide Field Infrared Survey Explorer (WISE) all-sky survey. We detect the statistical correlation between WISE and Planck data (g y) with a significance of 21.8 σ. We also measure the autocorrelation spectrum for the thermal Sunyaevâ€"Zel’dovich (tSZ) (yy) and the galaxy density field maps (gg) with a significance of 150 σ and 88 σ, respectively. We then construct a halo model and use the measured correlations C â„" gg , C â„" yy , and C â„" g y to constrain the tSZ mass bias B ≡ M 500 / M 500 tSZ . We also fit for the galaxy bias, which is included with explicit redshift and multipole dependencies as b g (z , â„") = b g 0 (1 + z) α (â„" / â„" 0) β , with â„" 0 = 117. We obtain the constraints to be B = 1.50 ± 0.07(stat) ± 0.34(sys), i.e., 1 âˆ' b H = 0.67 ± 0.03(stat) ± 0.16(sys) (68% confidence level) for the hydrostatic mass bias, and b g 0 = 1.28 âˆ' 0.04 + 0.03 (stat) ± 0.11 (sys) , with α = 0.20 âˆ' 0.07 + 0.11 (stat) ± 0.10 (sys) and β = 0.45 ±0.01(stat) ± 0.02(sys) for the galaxy bias. Incoming data sets from future CMB and galaxy surveys (e.g., Rubin Observatory) will allow probing the large-scale gas distribution in more detail. [ABSTRACT FROM AUTHOR]

Published
2022
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25. Predictive System for Heart Disease Using a Machine Learning Trained Model

Author

N. Emuoyibofarhe, Ozichi, Adebayo, Segun, Ibitoye, Ayodeji, O. Ayomide, Madamidola, Taye, Aderibigbe, N. Emuoyibofarhe, Ozichi, Adebayo, Segun, Ibitoye, Ayodeji, O. Ayomide, Madamidola, and Taye, Aderibigbe

Abstract

Heart as one of the essential organ of the human body and with its related disease such as cardiovascular diseases accounts for the death of many in our society over the last decades, and also regarded as one of the most life-threatening diseases in the world.  Hence we seek to predict a system for Heart disease using a supervised Machine Learning (ML) trained model in MATLAB2018 workflow in a real-time environment. To develop the system, 299 heart sounds from patients were obtained and labeled as normal and abnormal heart sound. Features were extracted and labeled as dataset; K Nearest Neighbour (KNN), Support Vector Machine (SVM) and Decision Tree (DT) algorithm were used as the training platform. From the classification analysis developed using the supervised ML trained model in MATLAB2018 in conjunction with system software features for the prediction of the heartbeat for both current and predefined of a heart condition algorithms used in training the dataset for the prediction when principle component analysis was enabled, the result shows that KNN algorithm has the highest and best accuracy of 94.4%, followed by the SVM with 84.4% and DT had 81.1%.  while from the evaluation analysis, KNN on Receive Operation Characteristic Curve (ROC) with 90% variance and training time of 12.88 seconds on positive class of abnormal over false classes of normal heart sound has AUC as 0.94 and on ROC curve with PCA 90% variance and training time of 1.7119 seconds on positive class of normal over negative classes of abnormal heart sound has AUC as 0.89 efficiency. Hence the analysis from the result shows that out of the three classified algorithms used, KNN predicts and have the highest accuracy and is more efficient with respect to real-time environment.

Published
2019

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