9
2023, www.msha.gov/sites/default/files/DataReports/
Charts/FY2023.
[10] Sair Kahraman, Jamal Rostami, and Ali Naeimipour.
Review of ground characterization by using instru-
mented drills for underground mining and con-
struction. Rock Mechanics and Rock Engineering,
49:585–602, 2016.
[11] U.S. Department of Labor, Mine Safety and Health
Administration. Fatality alert, 2023. Report accidents
and hazardous conditions: 1-800746-1553.
[12] Christopher Mark. The introduction of roof bolting
to us underground coal mines (1948–1960): a cau-
tionary tale. 2002.
[13] Syd Peng. Roof bolting and underground roof falls.
Geohazard Mechanics, 1(1):32–37, 2023.
[14] Jamal Rostami, Sair Kahraman, Ali Naeimipour, and
Craig Collins. Rock characterization while drilling
and application of roof bolter drilling data for evalua-
tion of ground conditions. Journal of Rock Mechanics
and Geotechnical Engineering, 7(3):273–281, 2015.
[15] Syd S Peng and DHY Tang. Roof bolting in under-
ground mining: a state-of-the-art review. International
Journal of Mining Engineering, 2:1–42, 1984.
[16] Syd S Peng. Evaluation of roof bolting requirements
based on in-mine roof bolter drilling. Technical
report, West Virginia Univ., Morgantown, WV
(United States), 2005.
[17] Robert H Peters. Improving safety at small under-
ground mines:.
[18] proceedings: Bureau of mines technology transfer
seminar. 1994.
[19] JJ Sammarco, A Podlesny, EN Rubinstein, and B
Demich. An analysis of roof bolter fatalities and inju-
ries in us mining. Transactions of Society for Mining,
Metallurgy, and Exploration, Inc, 340(1):11, 2016.
[20] Elham Rahimi, Younes Shekarian, Naser Shekarian,
and Pedram Roghanchi. Investigation of respirable
coal mine dust (rcmd) and respirable crystalline silica
(rcs) in the us underground and surface coal mines.
Scientific Reports, 13(1):1767, 2023.
[21] Faris Azhari, Charlotte C Sennersten, Craig A Lindley,
and Ewan Sellers. Deep learning implementations
in mining applications: a compact critical review.
Artificial Intelligence Review, pages 1–36, 2023.
[22] Ahmed Abd Elwahab, Erkan Topal, and Hyong Doo
Jang. Review of machine learning application in mine
blasting. Arabian Journal of Geosciences, 16(2):133,
2023.
[23] Zeshan Hyder, Keng Siau, and Fiona Nah. Artificial
intelligence, machine learning, and autonomous
technologies in mining industry.
[24] Journal of Database Management (JDM), 30(2):67–
79, 2019.
[25] Jane Gallwey, Matthew Eyre, and John Coggan. A
machine learning approach for the detection of sup-
porting rock bolts from laser scan data in an under-
ground mine. Tunnelling and Underground Space
Technology, 107:103656, 2021.
[26] Sarvesh Kumar Singh, Simit Raval, and Bikram
Banerjee. A robust approach to identify roof bolts
in 3d point cloud data captured from a mobile laser
scanner. International Journal of Mining Science and
Technology, 31(2):303–312, 2021.
[27] Yunliang Qi, Zhen Yang, Wenhao Sun, Meng Lou,
Jing Lian, Wenwei Zhao, Xiangyu Deng, and Yide
Ma. A comprehensive overview of image enhance-
ment techniques. Archives of Computational Methods
in Engineering, pages 1–25, 2021.
[28] Guillermo Gallego, Tobi Delbrück, Garrick Orchard,
Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan
Leutenegger, Andrew J. Davison, Jörg Conradt,
Kostas Daniilidis, and Davide Scaramuzza. Event-
based vision: A survey. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 44(1):154–180,
2022.
[29] Inigo Alonso and Ana C Murillo. Ev-segnet: Semantic
segmentation for event-based cameras. In Proceedings
of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition Workshops, pages 0–0, 2019.
[30] Olaf Ronneberger, Philipp Fischer, and Thomas Brox.
U-net: Convolutional networks for biomedical image
segmentation. In Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2015: 18th
International Conference, Munich, Germany, October
5-9, 2015, Proceedings, Part III 18, pages 234–241.
Springer, 2015.
[31] Connor Shorten and Taghi M Khoshgoftaar. A sur-
vey on image data augmentation for deep learning.
Journal of big data, 6(1):1–48, 2019.
[32] Nour Eldeen M Khalifa, Mohamed Hamed N Taha,
Aboul Ella Hassanien, and Sarah Hamed N Taha. The
detection of covid-19 in ct medical images: A deep
learning approach. Big Data Analytics and Artificial
Intelligence against COVID-19: Innovation Vision and
Approach, pages 73–90, 2020.
2023, www.msha.gov/sites/default/files/DataReports/
Charts/FY2023.
[10] Sair Kahraman, Jamal Rostami, and Ali Naeimipour.
Review of ground characterization by using instru-
mented drills for underground mining and con-
struction. Rock Mechanics and Rock Engineering,
49:585–602, 2016.
[11] U.S. Department of Labor, Mine Safety and Health
Administration. Fatality alert, 2023. Report accidents
and hazardous conditions: 1-800746-1553.
[12] Christopher Mark. The introduction of roof bolting
to us underground coal mines (1948–1960): a cau-
tionary tale. 2002.
[13] Syd Peng. Roof bolting and underground roof falls.
Geohazard Mechanics, 1(1):32–37, 2023.
[14] Jamal Rostami, Sair Kahraman, Ali Naeimipour, and
Craig Collins. Rock characterization while drilling
and application of roof bolter drilling data for evalua-
tion of ground conditions. Journal of Rock Mechanics
and Geotechnical Engineering, 7(3):273–281, 2015.
[15] Syd S Peng and DHY Tang. Roof bolting in under-
ground mining: a state-of-the-art review. International
Journal of Mining Engineering, 2:1–42, 1984.
[16] Syd S Peng. Evaluation of roof bolting requirements
based on in-mine roof bolter drilling. Technical
report, West Virginia Univ., Morgantown, WV
(United States), 2005.
[17] Robert H Peters. Improving safety at small under-
ground mines:.
[18] proceedings: Bureau of mines technology transfer
seminar. 1994.
[19] JJ Sammarco, A Podlesny, EN Rubinstein, and B
Demich. An analysis of roof bolter fatalities and inju-
ries in us mining. Transactions of Society for Mining,
Metallurgy, and Exploration, Inc, 340(1):11, 2016.
[20] Elham Rahimi, Younes Shekarian, Naser Shekarian,
and Pedram Roghanchi. Investigation of respirable
coal mine dust (rcmd) and respirable crystalline silica
(rcs) in the us underground and surface coal mines.
Scientific Reports, 13(1):1767, 2023.
[21] Faris Azhari, Charlotte C Sennersten, Craig A Lindley,
and Ewan Sellers. Deep learning implementations
in mining applications: a compact critical review.
Artificial Intelligence Review, pages 1–36, 2023.
[22] Ahmed Abd Elwahab, Erkan Topal, and Hyong Doo
Jang. Review of machine learning application in mine
blasting. Arabian Journal of Geosciences, 16(2):133,
2023.
[23] Zeshan Hyder, Keng Siau, and Fiona Nah. Artificial
intelligence, machine learning, and autonomous
technologies in mining industry.
[24] Journal of Database Management (JDM), 30(2):67–
79, 2019.
[25] Jane Gallwey, Matthew Eyre, and John Coggan. A
machine learning approach for the detection of sup-
porting rock bolts from laser scan data in an under-
ground mine. Tunnelling and Underground Space
Technology, 107:103656, 2021.
[26] Sarvesh Kumar Singh, Simit Raval, and Bikram
Banerjee. A robust approach to identify roof bolts
in 3d point cloud data captured from a mobile laser
scanner. International Journal of Mining Science and
Technology, 31(2):303–312, 2021.
[27] Yunliang Qi, Zhen Yang, Wenhao Sun, Meng Lou,
Jing Lian, Wenwei Zhao, Xiangyu Deng, and Yide
Ma. A comprehensive overview of image enhance-
ment techniques. Archives of Computational Methods
in Engineering, pages 1–25, 2021.
[28] Guillermo Gallego, Tobi Delbrück, Garrick Orchard,
Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan
Leutenegger, Andrew J. Davison, Jörg Conradt,
Kostas Daniilidis, and Davide Scaramuzza. Event-
based vision: A survey. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 44(1):154–180,
2022.
[29] Inigo Alonso and Ana C Murillo. Ev-segnet: Semantic
segmentation for event-based cameras. In Proceedings
of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition Workshops, pages 0–0, 2019.
[30] Olaf Ronneberger, Philipp Fischer, and Thomas Brox.
U-net: Convolutional networks for biomedical image
segmentation. In Medical Image Computing and
Computer-Assisted Intervention–MICCAI 2015: 18th
International Conference, Munich, Germany, October
5-9, 2015, Proceedings, Part III 18, pages 234–241.
Springer, 2015.
[31] Connor Shorten and Taghi M Khoshgoftaar. A sur-
vey on image data augmentation for deep learning.
Journal of big data, 6(1):1–48, 2019.
[32] Nour Eldeen M Khalifa, Mohamed Hamed N Taha,
Aboul Ella Hassanien, and Sarah Hamed N Taha. The
detection of covid-19 in ct medical images: A deep
learning approach. Big Data Analytics and Artificial
Intelligence against COVID-19: Innovation Vision and
Approach, pages 73–90, 2020.