Michael Bates | Economics | Best Researcher Award

Prof. Michael Bates | Economics | Best Researcher Award

Assistant Professor, UC Riverside, United States

Michael David Bates is currently an Assistant Professor of Economics at the University of California, Riverside. He earned his Ph.D. in Economics from Michigan State University in 2015 and has since focused his research on labor economics, economics of education, and applied econometrics.

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Education ๐Ÿ“š

Michael Bates completed his B.A. in Economics and History at the University of Michigan, Residential College, Ann Arbor, MI in 2006. He then pursued his M.A. and Ph.D. in Economics at Michigan State University, East Lansing, MI, graduating in 2011 and 2015, respectively.

Experience ๐Ÿ’ผ

Since 2015, Michael has served as an Assistant Professor of Economics at the University of California, Riverside. His teaching includes Ph.D. and undergraduate courses in labor economics and empirical research seminars.

Research Interests ๐Ÿ“Š

Michael Bates’ research interests encompass labor economics, economics of education, and applied econometrics. His work explores topics such as labor market policies, education outcomes, and econometric methodologies.

Awards ๐Ÿ†

Throughout his career, Michael Bates has received several awards, including the Harold D. Osterweil Prize in Economics from the University of Michigan in 2006 and the Blum Initiativeโ€™s Faculty Research Seed Grant at UCR from 2017 to 2019.

Publications

Nonlinear Correlated Random Effects Models with Endogeneity and Unbalanced Panels

Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity

Do learning communities increase first-year college retention? Evidence from a randomized control trial

Public and Private Employer Learning: Evidence from the Adoption of Teacher Value Added

Hedonic Prices and Equilibrium Sorting in Housing Markets: A Classroom Simulation

 

Hitesh Mohapatra | Engineering | Best Researcher Award

Assoc Prof Dr. Hitesh Mohapatra | Engineering | Best Researcher Award

Associate Professor, KIIT Deemed to be University, India

๐ŸŽ“ Dr. Hitesh Mohapatra, based in Bhubaneswar, Odisha, India, is a distinguished education professional with over 15 years of experience spanning both academic and corporate environments. He holds a Ph.D. in Computer Science & Engineering from VSS University of Technology (formerly UCE), Burla, Odisha, focusing on Designing Fault Tolerant Models for Wireless Sensor Networks and integrating them with Smart City applications under the supervision of Prof. Amiya Kumar Rath. His academic journey includes an M.Tech in Computer Science & Engineering from Odisha University of Technology and Research and a B.Tech in Information Technology from GIET University, Gunupur, Odisha.

 

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๐Ÿ‘จโ€๐ŸŽ“ Education

Ph.D. in Computer Science & Engineering from VSS University of Technology, Burla, Odisha, India, specializing in Designing Fault Tolerant Models for Wireless Sensor Networks with integration into Smart City applications. M.Tech in Computer Science & Engineering from Odisha University of Technology and Research, Bhubaneswar, Odisha, India. B.Tech in Information & Technology from GIET University, Gunupur, Odisha, India.

๐Ÿ‘จโ€๐Ÿ’ผ Experience

Current: Associate Professor at KIIT University, Bhubaneswar, Odisha, India. Previous Positions: Associate Professor at KL University, Vijayawada, Andhra Pradesh; Assistant Professor at SRES College of Engineering, Kopargaon, Maharashtra; Sr. Corporate Trainer & Freelancer at iGlue Soft Technologies Pvt. Ltd., Bangalore, Karnataka; Assistant Professor at GITA Autonomous College, Bhubaneswar, Odisha; and other teaching roles.

๐Ÿ“š ย Research

Extensive teaching experience in subjects like Internet of Things, Cloud Computing, Data Communication, and more. Published extensively in SCIE/SCI and Scopus/ESCI indexed journals on topics like IoT, WSN, and smart city applications. Presented papers at numerous national and international conferences, including SCOPUS indexed events.

๐Ÿ† Awards

Actively involved in consultancy projects for organizations like Odisha High Court and TECHNOWELL ENTERPRISE SERVICES Pvt. Ltd. Supervisor for multiple Ph.D. and M.Tech students in their research pursuits.

๐Ÿ“˜ Publications

Fault Tolerance in WSN Through PE-LEACH Protocol
H Mohapatra, AK Rath
IET Wireless Sensor Systems 9 (6), 358-365
1412019
Detection and Avoidance of Water Loss Through Municipality Taps in India by Using Smart Tap and ICT
H Mohapatra, AK Rath
IET Wireless Sensor Systems 9 (6), 447-457
1152019
Fault-tolerant mechanism for wireless sensor network
H Mohapatra, AK Rath
IET Wireless Sensor Systems 10 (1), 23 โ€“ 30
1092020
A Survey on Fault Tolerance Based Clustering Evolution in WSN
H Mohapatra, AK Rath
IET Networks 9 (4), 145-155
932020
IoE based framework for smart agriculture: Networking among all agricultural attributes
H Mohapatra, AK Rath
Journal of Ambient Intelligence and Humanized Computing, 18
872021
Fundamentals of Software Engineering

Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

Mr. Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

PhD student, Queensland University of Technology, Australia

Researcher specializing in drone-based remote sensing solutions for environmental and surveillance needs, focusing on precision agriculture and biosecurity. Expertise includes UAV remote sensing, artificial intelligence, and multispectral/hyperspectral image processing. Currently pursuing a PhD in Precision Agriculture at Queensland University of Technology.

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๐ŸŽ“ Education

PhD: Precision Agriculture, Queensland University of Technology (2021 – Present). MSc: Agricultural Engineering, Eastern University, Sri Lanka (2016 – 2018). BSc: Agriculture, Agricultural Engineering, Eastern University, Sri Lanka (2010 – 2015). BIT: Software Engineering, University of Colombo School of Computing, Sri Lanka (2011 – 2015)

๐Ÿ” Experience

Research Assistant at Charles Sturt University and Sunshine Coast Council on projects integrating AI and drone technology for environmental monitoring and invasive species detection.

๐Ÿ† Awards

QUT/Accelerate Higher Education Development Expansion and Development (AHEAD) World Bank Project Scholarship. Vice Chancellor’s Award for Early Career Researcher, Faculty of Technology, 2022.

๐ŸŒ Research Interests

Precision Agriculture, UAV-based Remote Sensing, Multispectral Image Processing, AI, Biosystems Engineering, Environmental Management.

๐Ÿ“š Publications

Co-authored numerous peer-reviewed articles in Q1 and non-Q1 ranking journals on topics related to UAV-based remote sensing and AI applications in agriculture and environmental management.

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops
Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imageryDetection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
N Amarasingam, F Gonzalez, ASA Salgadoe, J Sandino, K Powell
E-agricultural concepts for improving productivity: A review

Onder Aybastฤฑer | DNA damage | Best Researcher Award

Assoc Prof Dr. Onder Aybastฤฑer | DNA damage | Best Researcher Award

Assoc Prof Dr, Bursa Uludag University, Turkey

Assoc. Prof. Dr. ร–nder Aybastier was born on February 1, 1983, in Bursa, Turkey. He is of Turkish nationality and currently resides at Uludag University, Faculty of Science and Arts, Department of Chemistry, Bursa, Turkey.

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๐Ÿ“š Education:

Ph.D. in Analytical Chemistry (2010-2016) from Uludag University, Graduate School of Natural and Applied Sciences, Bursa, Turkey. M.Sc. in Analytical Chemistry (2006-2010) from Uludag University, Graduate School of Natural and Applied Sciences, Bursa, Turkey. B.Sc. in Chemistry (2001-2005) from Uludag University, Faculty of Science and Arts, Bursa, Turkey.

๐Ÿ‘จโ€๐Ÿ’ผ Experience:

Assoc. Prof. Dr. ร–nder Aybastier has held the position of Associate Professor at Uludag University, Faculty of Science and Arts, Department of Chemistry since 2021.

๐Ÿ”ฌ Research Interests:

His research interests include:Immobilization of enzymes on various matrices for biotechnological applications. Extraction and isolation of bioactive compounds from natural sources. Antioxidant properties and oxidative damage prevention. Analytical chemistry methodologies for bioactive compound analysis.

๐Ÿ† Awards:

Patent (2021): A novel hydrogel containing galangin, with Saliha ลžahin and Eftal Alp Dorken. Numerous awards for research excellence and contributions to the field of chemistry

๐Ÿ“„ Publications: International Journal Publications:

Optimization of Immobilization Conditions of Thermomyceslanuginosus Lipase on Styrene-divinylbenzene Copolymer Using Response Surface Methodology, Journal of Molecular Catalysis B: Enzymatic, 2010.

Determination of Total Phenolic Content in Prunella L. by Horseradish Peroxidase Immobilized onto Chitosan Bioreactor, Analytical Methods, 2011.

Orthogonal Signal Correction-based Prediction of Total Antioxidant Activity Using Partial Least Squares Regression from Chromatograms, Journal of Chemometrics, 2012.

Response Surface Optimized Ultrasonic-Assisted Extraction of Quercetin and Isolation of Phenolic Compounds From HypericumPerforatum L. by Column Chromatography, Separation Science and Technology, 2013.

Optimization of Ultrasonic-Assisted Extraction of Antioxidant Compounds from Blackberry Leaves Using Response Surface Methodology, Industrial Crops and Products, 2013.

Paula Montoya Lopera | Planetary Sciences | Best Researcher Award

Dr. Paula Montoya Lopera | Planetary Sciences | Best Researcher Award

Research Fellow, CODES – UTAS, Australia

Dedicated and hard-working Economic Geologist Scientist with 23 years of experience specializing in the exploration and research of various mineral deposits, including Ag/Au polymetallic epithermal, orogenic gold vein systems, and gold, copper-molybdenum porphyry deposits and skarns. Known for strong leadership and project implementation skills in applied geoscience and economic development.

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๐ŸŽ“ Education:

Specialization in Pedagogy, Universidad del Norte, Colombia (2022). Ph.D. Earth Science (Economic Geology), CGEO, UNAM University, Mexico (2016 – 2020). M.Sc. Earth Science (Geometallurgy), University of Tasmania, Australia (2012 – 2014). B.Sc. Geologist Engineer, National University, Colombia (1999 – 2004)

๐Ÿ† Awards:

๐Ÿ† Throughout her career, Dr. Montoya Lopera has received numerous awards, including the prestigious BAL-UNAM Award for the best Earth Science PhD thesis (2020) and a PhD Honorific Mention from CGEO, UNAM (2020). She was nominated for the Alfonso Caso Medal for outstanding PhD candidates at UNAM University and received the UNAM Recognition Award for PhD graduation on time, all in 2020. Earlier, she was honored with the OAS-CONACYT-AMEXID Award for the best PhD student at CGEO, UNAM (2016) and the CONACYT Scholarship Award for her PhD studies (2016-2020), along with the AngloGold Ashanti Scholarship Award for her MSc studies at UTAS, Tasmania, Australia (2012-2014).

๐Ÿ’ผ Experience:

Senior Economic Geologist Consultant at CCS, North University

Senior Geologist at AngloGold Ashanti

๐Ÿ” Research Interests:

๐Ÿ”ฌ Dr. Montoya Lopera’s professional skills span analytical techniques such as XRF, QXRD, and MLA data analysis, complemented by extensive experience in mine geology, geometallurgy, and applied geoscience. She is proficient in using software tools like Datamine (RM – FUSION X), Leapfrog Geo, and IoGas, among others, contributing significantly to her research and consultancy roles in economic geology and mineral exploration.

Publicationsย 

New insights into the geology and tectonics of the San Dimas mining district, Sierra Madre Occidental, Mexico P Montoya-Lopera, L Ferrari, G Levresse, F Abdullin, L Mata Ore Geology Reviews 105, 273-294 17 2019 Development of a predictive geometallurgical recovery model for the La Colosa, porphyry gold deposits, Colombia. S Leichliter, J Hunt, R Berry, L Keeney, P Montoya-Lopera, … The first AusIMM International Geometallurgy Conference: GeoMet 2011. Brisbane 13 2011 Construcciรณn del pensamiento pedagรณgico BE Garcรญa, JG Lรณpez, M Lopera, P Andrea, AF Moreno, PA Osorio Medellรญn: Universidad Pontifica Bolivariana 9 2007 New geological, geochronological and geochemical characterization of the San Dimas mineral system: Evidence for a telescoped Eocene-Oligocene Ag/Au deposit in the Sierra Madreย โ€ฆ

Pushpendra Singh| Electrical Engineering | Excellence in Research

Dr. Pushpendra Singh| Electrical Engineering | Excellence in Research

Program Director and Professor -Energy Sciences, Atria University Bengaluru, India

Dr. Pushpendra Singh is a distinguished academician and researcher in Electrical Engineering, currently serving as Program Director and Professor of Energy Sciences at Atria University, Bengaluru. With over 19 years of experience, his expertise spans AI & ML applications, Smart Grid technologies, IoT applications in Electrical systems, Game theory, Power system restructuring, and integration of Distributed Energy Resources and Electric Vehicles.

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๐ŸŽ“ Education:

Dr. Pushpendra Singh holds a Ph.D. in Electrical Engineering from MNIT Jaipur, an M.Tech. in Power Systems also from MNIT Jaipur, and completed a Post Graduate Program in Artificial Intelligence and Machine Learning from NIT Warangal.

๐Ÿ’ผ Experience:

His professional journey includes leadership roles such as Professor of Electrical Engineering at JK Lakshmipat University, Jaipur, Principal at Sunrise Group of Institutions, Udaipur, and various academic positions at Jaipur Engineering College & Research Centre, Jaipur.

๐Ÿ” Research Interests:

His research interests focus on AI & ML applications in electrical systems, Smart Grid technologies, IoT applications, Game theory in energy systems, Power system restructuring, and integration of Electric Vehicles.

๐Ÿ† Awards:

Dr. Pushpendra Singh has been recognized with awards such as IEEE PES HAC 2023 Ambassador, Excellence in Innovation by ITSR and Institution of Engineers (India), and multiple honors for his contributions to education and engineering services.

ย publicationsย 

Revolutionizing EV Charging stations through IoT

Published Year: 2024

Journal: To be presented at The International Conference (IEEE) on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC 2024)

Cited by: Accepted for presentation

Green Credit Incentivization for EV Charging: A Game-Theoretic Approach

Published Year: 2023

Journal: Presented at The 6th International Symposium on Hydrogen Energy and Energy Technologies (HEET 2023)Cited by: Best Paper Award, published in Journal of Physics: Conference Series

Electric Vehicle Adoption and Integration in Smart Cities: A Game-Theoretic Approach

Published Year: 2023Journal: Presented at The 6th International Symposium on Hydrogen Energy and Energy Technologies (HEET 2023)Cited by: Best Paper Award, published in Journal of Physics: Conference Series

Mohammed Allawi | Engineering | Best Researcher Award

Dr. Mohammed Allawi | Engineering | Best Researcher Award

University of Anbar,ย  Iraq

Mohammed Falah Allawi, an Iraqi national, is a distinguished civil engineer specializing in water surface hydrology, dams engineering, and fluid mechanics. He holds a PhD in Civil Engineering from the National University of Malaysia and serves as a lecturer at the University of Anbar.

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๐Ÿ“š Education:

Mohammed earned his B.Sc. in Dams and Water Resources Engineering from the University of Anbar in 2010, followed by an M.Sc. and a PhD in Civil Engineering from the National University of Malaysia in 2016 and 2019, respectively.

๐Ÿข Experience:

He has extensive experience as a field engineer in various construction projects and has lectured at Al Maarif University College and the University of Anbar.

๐Ÿ” Research Interests:

His research interests span water resources planning, steel structures, advanced soil mechanics, and the application of artificial intelligence in hydro-environmental modeling.

๐Ÿ† Awards:

Mohammed has been recognized for his contributions in environmental ergonomics and holds memberships in several engineering associations across Iraq and the Arab region.

๐Ÿ“ Publications:

Neurocomputing, 2022 – Groundwater level prediction using machine learning models: A comprehensive review. Cited by 186.

Neural Computing and Applications, 2018 – Non-tuned machine learning approach for hydrological time series forecasting. Cited by 101.

Neural Computing and Applications, 2019 – A hybrid batโ€“swarm algorithm for optimizing dam and reservoir operation. Cited by 97.

Scientific Reports, 2020 – Input attributes optimization using the feasibility of genetic nature inspired algorithm: application of river flow forecasting. Cited by 73.

Environmental Science and Pollution Research, 2018 – Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models. Cited by 66.

RBFNN-based model for heavy metal prediction for different climatic and pollution conditions

Pouya Sepehr | Artificial Intelligence |Best Researcher Award

Dr. Pouya Sepehr | Artificial Intelligence |Best Researcher Award

Research Fellow, Siena University, Italy

Pouya Sepehr is a researcher and urban planner specializing in the intersections of science, technology, and urban studies. He explores how technological infrastructures influence urban environments, focusing on sustainability and socio-environmental innovation.

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๐Ÿ“š Education:

Pouya holds a PhD in Science-Technology-Society from the University of Vienna, completed in 2023, with a dissertation defense in February 2024. He also holds master’s degrees from the University of Vienna in Science-Technology-Society and from Oxford Brooks University in Development and Emergency Practice. His bachelor’s degree is from Tehran University in Restorations and Conservation of Historical Buildings.

๐Ÿ’ผ Experience:

Pouya has extensive experience in project management and research. He has served as a Post-Doc Research Fellow at the University of Siena, focusing on digital social innovation in European urban contexts. Previously, he worked as a Researcher at the Institute for Advanced Studies Vienna and as a Research Assistant at various academic institutions across Europe.

๐Ÿ”ฌ Research Interests:

His research interests include the governance of technology and innovation in urban settings, urban sustainability, and the societal impacts of technological infrastructures. He is particularly interested in advancing multimodal and multispecies urbanism and promoting inclusivity and resilience in urban environments.

๐Ÿ† Awards:

Pouya Sepehr is an elected council member of 4S (Society for Social Studies of Science), recognizing his contributions to the field of Science and Technology Studies (STS).

Publications

Sepehr, Pouya. (2024). Mundane Urban Governance and AI Oversight: The Case of Vienna’s Intelligent Pedestrian Traffic Lights. Journal of Urban Technology, 31(1).

Felt, Ulrike, and Pouya Sepehr. (2024). Infrastructuring Citizenry in Smart City Vienna: Investigating Participatory Smartification between Policy and Practice. Journal of Responsible Innovation, 11(2).

Sepehr, Pouya and Ulrike Felt. (2023). Urban Imaginaries as Tacit Governing Devices: The Case of Smart City Vienna. Science, Technology, & Human Values, 48(9).

 

Joseph Arhavbarien | Green Operations | Best Researcher Award

Dr. Joseph Arhavbarien | Green Operations | Best Researcher Award

Director / Researcher, Rockedge Ventures (UK) Ltd, United Kingdom

๐ŸŒฑ๐Ÿ“Š Dr. Joseph Arhavbarien is an accomplished researcher with a Ph.D. in Business and Management from the University of Bedfordshire, UK. With over three decades of industrial experience, he focuses on green processes, sustainable operations, and supply chain management. Transitioning to academia, he combines his rich industrial background with his academic expertise to teach and conduct research, applying quantitative techniques to explore green value internalisation.

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๐Ÿ“š Education:

๐ŸŽ“ Dr. Arhavbarien holds a Ph.D. in Business and Management from the University of Bedfordshire (2023). He earned his MBA in International Business from the University of East London (2015) and a B.Sc. (Hons) in Microbiology from the University of Lagos, Nigeria (1989). Additionally, he has a Certificate in Agile Project Management from the University of Oxford (2023).

๐Ÿ’ผ Experience:

๐Ÿ’ผ Dr. Arhavbarien’s diverse professional journey includes roles in retail, manufacturing, logistics, and consultancy. Notably, he has worked with Tesco, Morrisons, Gosh Food Ltd, and FedEx, where he led teams, managed operations, and drove continuous improvement initiatives. His expertise spans statistical analysis, project management, and sustainable business practices.

๐Ÿ”ฌ Research Interests:

๐Ÿ”๐Ÿ“ˆ Dr. Arhavbarienโ€™s research interests are centered on green value internalisation, sustainable operations, and supply chain resilience. He employs quantitative methods and tools like Qualtrics and SPSS/AMOS to analyze data, aiming to develop green criteria for stakeholder engagement and enhance eco-efficiency in industrial operations.

๐Ÿ† Awards:

๐Ÿ† Dr. Arhavbarien has been recognized for his academic and professional contributions. A notable achievement includes guiding an M.Sc. student to win the 2023 Logistics Research Network CILT (UK)โ€™s MSc Dissertation of the Year award, reflecting his ability to inspire and mentor students.

Publications

๐Ÿ“šย  An investigation of antecedents and consequences of green value internalisation among sampled UK enterprises – Journal of Environmental Management, 2024

๐Ÿ“„ An examination of antecedents of green value internalisation for firm-level supply chain collaboration – British Academy of Management (BAM) 2022 Conference, Alliance Manchester Business School, UK.

๐Ÿ“„ Green supply chain management: an investigation of firm-level antecedents of green value internalisation – 29th European Operations Management Association (EurOMA) Conference, Berlin, Germany, 2022.

๐Ÿ“„ An investigation of firm-level antecedents of green value internalisation for Upstream-Downstream supply chain interactions – 32nd Production and Operations Management Society (POMS) Conference (online), 2022.

Ali Raza | artificial intelligence | Best Researcher Award

Mr. Ali Raza | artificial intelligence | Best Researcher Award

Lecturer, The University of Lahore, Pakistan

Ali Raza is a dedicated research scholar specializing in data science, known for his expertise in machine learning and deep learning applications. With a strong academic background and extensive professional experience in software development, he has contributed significantly to research in artificial intelligence and health informatics.

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๐Ÿ“š Education:

Ali completed his Bachelor of Science in Computer Science at KFUEIT after graduating from Iqra Degree College with a degree in Pre-Engineering. He further pursued his passion for computer science by earning a Master’s degree in Computer Science from KFUEIT, where his research focused on novel approaches in deep learning for image detection.

๐Ÿ’ผ Experience:

Ali’s professional journey includes roles as a Research Assistant at KFUEIT, where he published research articles on artificial intelligence. He has also worked as a Desktop App Developer at DexDevs Company and as a Full Stack Python Developer at BuiltinSoft Company, gaining expertise in business application development and machine learning frameworks.

๐Ÿ”ฌ Research Interests:

Ali’s research interests revolve around data science, particularly in machine learning model optimization, health informatics, and artificial intelligence applications in diverse domains such as pregnancy health analysis and network security.

๐Ÿ† Awards:

Ali has contributed significantly to research, evident from his publications and contributions as a peer reviewer for IEEE Access and PLOS ONE, highlighting his recognition in the academic community.

๐Ÿ“„ Publications:

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, Plos one, 2022 (cited 46 times)

A novel deep learning approach for deepfake image detection, Applied Sciences, 2022 (cited 58 times)

Predicting employee attrition using machine learning approaches, Applied Sciences, 2022 (cited 44 times)

A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence, Technologies, 2023 (cited 23 times)

Novel class probability features for optimizing network attack detection with machine learning, IEEE Access, 2023