Wisal Zafar | Computer Science | Best Researcher Award

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Mr. Wisal Zafar | Computer Science | Best Researcher Award

Lecturer at Cecos university of information technology and emerging sciences, Pakistan.

Mr. Wisal Zafar is a dedicated researcher and lecturer with a strong background in software engineering, focusing on artificial intelligence, machine learning, and deep learning applications in healthcare. Born on March 25, 1999, in Peshawar, Pakistan, he has consistently demonstrated a passion for advancing technology’s role in solving real-world problems. He has developed and published research that leverages machine learning for medical diagnoses, including brain tumor analysis and diabetes prediction. As a lecturer and Electronic Data Processing (EDP) Officer at Iqra National University, he is committed to mentoring students and contributing to the field through both teaching and research. His work is distinguished by his continuous learning, keeping pace with emerging trends in AI and big data. Mr. Zafar’s career is marked by his enthusiasm for interdisciplinary research, integrating software engineering with advancements in health and data science. He is eager to expand his research contributions further through collaborations and innovative projects that address global challenges using advanced technologies.

Professional Profile

Education

Wisal Zafar holds an MS in Software Engineering from Iqra National University, Hayatabad Peshawar, completed in July 2024 with a commendable CGPA of 3.62/4.00. His postgraduate studies provided him with in-depth knowledge of advanced topics like artificial intelligence, data analysis, and big data. Prior to this, he earned a BS in Software Engineering from the same institution in October 2020, with a CGPA of 3.47/4.00, building a strong foundation in software development and computer science principles. His academic journey started with an intermediate qualification from Capital Degree College, Peshawar, where he scored 700 out of 1100 marks, and continued with his matriculation from The Jamrud Model High School, achieving 824 out of 1100 marks. His educational background is characterized by consistent academic performance and a focus on both theoretical and practical aspects of software engineering, which has prepared him for his subsequent roles in academia and research.

Professional Experience

Wisal Zafar currently serves as a Lecturer at Iqra National University, Hayatabad, Peshawar, where he has been teaching various software engineering subjects since January 2023. His areas of instruction include Data Science, Artificial Intelligence, Machine Learning, Data Structures, and Algorithms, allowing him to impart advanced knowledge to students and prepare them for careers in technology. Alongside his role as a lecturer, he also holds the position of Electronic Data Processing (EDP) Officer at the same university, a role he has been fulfilling since October 2021. In this capacity, he manages data processing tasks, ensuring the effective handling of academic data and resources. Previously, he gained practical experience as a Junior Web Developer at Pakistan Online Services Software House, where he worked from November 2020 to April 2021, specializing in web development using PHP, Laravel, JavaScript, and other technologies. This diverse experience in academia and industry has equipped Mr. Zafar with the skills to blend theoretical concepts with real-world applications, making him an effective educator and a valuable contributor to research.

Research Interests

Wisal Zafar’s research interests are centered around artificial intelligence (AI), machine learning (ML), deep learning, and their applications in healthcare and data analysis. He is particularly fascinated by the potential of AI and ML in developing advanced diagnostic tools, aiming to improve medical outcomes through data-driven insights. His recent research projects have explored the use of deep learning techniques like YOLOv8s and U-Net for multi-class brain tumor analysis, integrating detection, localization, and segmentation of tumors using MRI data. Additionally, he has delved into predictive models for diabetes diagnosis using various ML algorithms, such as Decision Trees, K-Nearest Neighbors, Random Forest, Logistic Regression, and Support Vector Machines. His interests extend to big data analytics and the role of data science in enhancing information retrieval and management in medical libraries. Through his work, Wisal Zafar seeks to advance the intersection of technology and healthcare, utilizing cutting-edge algorithms and data processing techniques to solve critical challenges and improve human well-being.

Research Skills

Wisal Zafar possesses a diverse skill set in artificial intelligence, machine learning, data analysis, and big data management, making him adept at tackling complex research challenges. He has extensive experience in using programming languages like Python and C++, which he applies to develop machine learning models and algorithms. His technical expertise includes working with deep learning frameworks, as seen in his research on brain tumor analysis using advanced models such as YOLOv8s and U-Net. Additionally, Wisal has proficiency in cloud computing and handling large datasets, which supports his work in big data analytics and the implementation of data-driven decision-making tools. His hands-on experience as a Research Assistant has further refined his skills in conducting surveys, data preprocessing, and statistical analysis. Mr. Zafar is also skilled in web development using frameworks like Laravel and JavaScript, allowing him to create interactive platforms for research applications. His ability to integrate these skills into interdisciplinary projects makes him a capable researcher with a focus on innovation and problem-solving.

Award Recognition

Wisal Zafar’s dedication to research and academic excellence has earned him recognition in the academic community, though he is still working towards broader award recognitions. His recent research publications, including studies on brain tumor analysis and diabetes prediction using machine learning, have been well-received and published in respected journals. These works have contributed significantly to the fields of AI in healthcare and big data analytics, positioning him as a promising researcher. His role as a Lecturer at Iqra National University also reflects the acknowledgment of his expertise, as he is entrusted with educating the next generation of software engineers. Additionally, Wisal has completed several certified courses from platforms like Coursera, receiving certificates in advanced learning algorithms, deep learning, and image processing with Python, which underscore his commitment to continuous learning. While he may not yet have specific awards, his publications, teaching contributions, and commitment to research excellence serve as strong indicators of his potential for future recognition in the field.

Awards and Honors

Wisal Zafar has demonstrated a commitment to continuous professional development through various certifications and achievements, contributing to his expertise in software engineering and AI. He has completed notable courses such as AI for Everyone and Advanced Learning Algorithms through Coursera, which are associated with respected institutions like DeepLearning.AI and Stanford University. These certifications have enhanced his knowledge of machine learning, deep learning, and image processing, enabling him to apply advanced concepts in his research. While he has not yet received specific formal awards, his role as a Lecturer at Iqra National University and his position as an Electronic Data Processing (EDP) Officer are testaments to his skills and recognition within the academic community. His contributions to research, especially in the areas of AI applications in healthcare, have been acknowledged through the publication of his work in peer-reviewed journals. Wisal Zafar’s ongoing pursuit of excellence, both in research and teaching, positions him as a candidate worthy of future awards and honors in the field of software engineering and AI.

Conclusion:

Wisal Zafar has demonstrated considerable research skills and expertise in the field of software engineering, particularly in applying machine learning and AI to medical problems. His academic background, technical skills, and research publications make him a strong contender for the Best Researcher Award. While he could benefit from diversifying his research and increasing his international presence, his current achievements in AI-driven healthcare solutions and data analytics set a solid foundation for this recognition.

Publications Top Notes

  1. Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
    • Authors: Zafar, W., Husnain, G., Iqbal, A., AL-Zahrani, M.S., Naidu, R.S.
    • Journal: Results in Engineering
    • Year: 2024
    • Volume: 24
    • Article ID: 102994
    • Type: Open access
  2. Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed
    • Authors: Shaukat, Z., Zafar, W., Ahmad, W., Ghadi, Y.Y., Algarni, A.
    • Journal: Healthcare (Switzerland)
    • Year: 2023
    • Volume: 11
    • Issue: 21
    • Article ID: 2864
    • Citations: 1
    • Type: Open access

 

 

 

 

Abid Iqbal | Artificial Intelligence | Best Researcher Award

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Assist Prof Dr. Abid Iqbal | Artificial Intelligence | Best Researcher Award

Assistant Professor at King Faisal University, Saudi Arabia

Dr. Abid Iqbal is an accomplished Assistant Professor at the University of Engineering and Technology Peshawar, specializing in Electrical Engineering and artificial intelligence. He earned his Ph.D. from Griffith University, Australia, where he researched piezoelectric energy harvesters. With a strong academic background, he ranked first in his Master’s program at Ghulam Ishaq Khan Institute, Pakistan. Dr. Iqbal has a diverse professional experience, including roles as an Electrical Design Engineer and Research Assistant. His expertise encompasses developing embedded devices and innovative teaching methodologies, mentoring students, and conducting impactful research. He has successfully secured funding for multiple projects in AI applications for health and agriculture. Dr. Iqbal’s publication record includes numerous papers in reputable journals, reflecting his commitment to advancing knowledge in his field. His technical skills in programming and software further enhance his research capabilities, making him a valuable asset to academia and industry.

Profile

Education

Dr. Abid Iqbal is a highly accomplished academic with a solid educational foundation in electrical and electronics engineering. He earned his Ph.D. from the Queensland Micro- and Nanotechnology Centre at Griffith University, Australia, from April 2013 to February 2017. His doctoral research focused on the design, fabrication, and analysis of aluminum nitride (AlN)/silicon carbide (SiC)-based piezoelectric energy harvesters, contributing significantly to renewable energy technologies. Prior to his Ph.D., Dr. Iqbal completed his Master’s degree in Electronics Engineering at the Ghulam Ishaq Khan Institute in Topi, Swabi, Pakistan, graduating with a remarkable GPA of 3.88/4 and securing the top position in his class. His academic journey began with a Bachelor’s degree in Electrical Engineering from the University of Engineering & Technology in Peshawar, Pakistan, where he was recognized as an outstanding student. Dr. Iqbal’s educational background reflects his dedication and expertise in his field, laying a strong foundation for his professional career.

Professional Experience

Dr. Abid Iqbal is an accomplished electrical engineer currently serving as an Assistant Professor at the University of Engineering and Technology Peshawar since August 2019. In this role, he has been instrumental in teaching undergraduate courses in Electrical Engineering, developing innovative teaching methods, and mentoring students on research projects. Prior to this position, he worked as an Electrical Design Engineer at Alliance Power and Data in Australia, focusing on ERGON and NBN projects. He also contributed to the development of embedded systems for individuals with disabilities while employed as an Electronic Engineer at Community Lifestyle Support. His research experience includes a significant role as a Research Assistant at Griffith University, where he worked on piezoelectric devices for harsh environments and gained expertise in various semiconductor fabrication processes. Additionally, he has served as a lecturer at Comsat Institute of Information Technology and worked as a research associate at the City University of Hong Kong, demonstrating a robust and diverse professional background in academia and industry.

Research Interest

Dr. Abid Iqbal’s research interests lie at the intersection of electrical engineering and artificial intelligence, focusing on the development of innovative technologies that enhance energy efficiency and improve healthcare outcomes. His work includes designing and fabricating advanced piezoelectric energy harvesters using AlN/SiC materials, aimed at harnessing renewable energy sources. Additionally, Dr. Iqbal is deeply involved in projects utilizing artificial intelligence for agricultural applications, such as real-time disease detection in crops, and developing telehealth systems that leverage IoT technology to monitor patient health remotely. He has a keen interest in embedded systems and the design of hardware for assistive technologies, including portable ventilators and muscle sensors for individuals with disabilities. Through his research, Dr. Iqbal aims to contribute to sustainable energy solutions and advancements in healthcare technology, fostering a multidisciplinary approach that integrates engineering principles with artificial intelligence for practical applications.

Research Skills

Dr. Abid Iqbal possesses a robust set of research skills that underscore his expertise in Electrical Engineering and artificial intelligence. His extensive experience in designing and fabricating piezoelectric energy harvesters highlights his proficiency in materials science and device characterization. Dr. Iqbal is adept at using advanced simulation tools such as COMSOL, Ansys, and Coventorware, which facilitate in-depth analysis and optimization of microelectromechanical systems (MEMS). His work on artificial intelligence applications in telehealth and agricultural systems showcases his ability to integrate machine learning techniques with practical engineering solutions. Additionally, Dr. Iqbal has a strong background in programming languages such as Python and MATLAB, enhancing his capability to develop innovative software solutions for complex engineering problems. His involvement in funded projects and numerous publications further illustrates his commitment to advancing research and contributing to knowledge in his field. Overall, Dr. Iqbal’s diverse skills position him as a valuable asset to any research team.

Award and Recognition

Dr. Abid Iqbal is a distinguished electrical engineer and academic known for his significant contributions to the field of electrical and electronics engineering. He has received multiple accolades for his research and academic excellence, including the IGNITE funding for four innovative projects focused on machine learning applications in health and agriculture. Dr. Iqbal was awarded publication scholarships and prestigious Griffith University PhD scholarships, recognizing his outstanding academic performance during his doctoral studies. Additionally, he ranked first among his peers in the Master’s program at Ghulam Ishaq Khan Institute, further demonstrating his commitment to excellence in engineering. His dedication to teaching and mentoring future engineers is evident in his role as an Assistant Professor at the University of Engineering and Technology Peshawar, where he has developed innovative curricula and guided numerous student research projects. Dr. Iqbal’s work has been widely published, contributing significantly to advancements in artificial intelligence, embedded systems, and renewable energy technologies.

Conclusion

Dr. Abid Iqbal is a highly qualified candidate for the Best Researcher Award, demonstrating exceptional expertise in Electrical Engineering and a strong commitment to research and education. His accomplishments in renewable energy research, successful project management, and dedication to mentoring future engineers make him a standout choice. While he has areas for growth, particularly in expanding collaborative networks and enhancing commercialization efforts, his current achievements and potential for future contributions position him as an inspiring figure in his field. This award would not only recognize his past efforts but also encourage his continued pursuit of excellence in research and education.

Publication Top Notes

  1. Enhanced TumorNet: Leveraging YOLOv8s and U-net for superior brain tumor detection and segmentation utilizing MRI scans
    • Authors: Zafar, W., Husnain, G., Iqbal, A., AL-Zahrani, M.S., Naidu, R.S.
    • Year: 2024
    • Journal: Results in Engineering
    • Volume/Page: 24, 102994
  2. Novel dual absorber configuration for eco-friendly perovskite solar cells: design, numerical investigations and performance of ITO-C60-MASnI3-RbGeI3-Cu2O-Au
    • Authors: Hasnain, S.M., Qasim, I., Iqbal, A., Amin Mir, M., Abu-Libdeh, N.
    • Year: 2024
    • Journal: Solar Energy
    • Volume/Page: 278, 112788

 

 

 

Anyiam Kizito | Statistics | Best Researcher Award

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Dr. Anyiam Kizito | Statistics | Best Researcher Award

Senior Lecturer at Federal University Of Technology, Owerri, Nigeria.

Kizito Ebere Anyiam is a Senior Lecturer in the Department of Statistics at the Federal University of Technology, Owerri, Nigeria. He holds a PhD in Statistics from Nnamdi Azikiwe University, where his dissertation focused on new generalized exponentiated Weibull families of lifetime distributions. With a strong background in distribution theory, multivariate statistics, and statistical computing, Anyiam has made significant contributions to the field through numerous peer-reviewed publications, including works on statistical modeling and reliability analysis. He has also supervised over 30 undergraduate projects, many of which have been published in reputable journals. Beyond teaching, he has implemented innovative methods to enhance student engagement and practical experience in statistics. His professional affiliations include membership in the Nigerian Statistical Association and the Nigerian Mathematical Society, showcasing his active involvement in the academic community. With proficiency in data analysis software and a commitment to advancing statistical education, Anyiam is a respected figure in his field.

Profile:

Education

Kizito Ebere Anyiam holds a Ph.D. in Statistics from Nnamdi Azikiwe University, Awka, Nigeria, completed in 2023. His dissertation focused on the development of “New generalized Exponentiated Weibull Families of Lifetime Distributions with Unimodal and Bimodal Properties,” showcasing his research expertise in statistical distribution theory. Prior to his doctoral studies, he earned a Master’s degree in Statistics from Imo State University, Owerri, Nigeria, in 2008, which laid a solid foundation for his statistical knowledge and skills. In 2011, he completed a Postgraduate Diploma in Information Technology at the Federal University of Technology, Owerri, further enhancing his technical capabilities in data analysis. His academic journey began with a Bachelor’s degree in Statistics from Abia State University, Uturu, Nigeria, in 1995, where he first developed a passion for the field. This strong educational background equips him with the theoretical and practical skills essential for his current role as a Senior Lecturer and researcher.

Professional Experiences 

Kizito Ebere Anyiam is a Senior Lecturer in the Department of Statistics at the Federal University of Technology, Owerri, where he has been instrumental in enhancing the academic experience since 2009. He has developed and taught courses in Probability and Distribution Theory at both undergraduate and graduate levels, employing innovative teaching methods that actively engage students in fieldwork and industrial experiences. In his role as a project advisor, he has supervised over 30 undergraduate research projects, many of which have been published in peer-reviewed journals, showcasing his commitment to fostering research excellence. Additionally, Anyiam has served as the Undergraduate Industrial Experience Coordinator and Class Adviser, further contributing to the academic and professional development of his students. His earlier teaching experience as a Teaching Assistant at Federal Polytechnic Nekede laid the foundation for his strong educational background, which includes a PhD in Statistics and multiple publications in esteemed journals.

Research Interests

Kizito Ebere Anyiam’s research interests primarily revolve around distribution theory and reliability analysis, where he explores the properties and applications of various statistical distributions. His work includes developing new generalized families of lifetime distributions, particularly focusing on unimodal and bimodal properties. Additionally, he engages in multivariate statistics, investigating the complexities of multiple variables and their interactions. Anyiam is also keen on mathematical statistics and statistical computing, utilizing advanced statistical software to enhance data analysis and simulation techniques. His interest in applied probability further complements his research, allowing him to apply theoretical statistical concepts to real-world scenarios. Through his innovative research, Anyiam aims to contribute to the advancement of statistical methodologies and their practical applications across various fields, including engineering and biomedical sciences. His commitment to statistical education and research fosters a deeper understanding of statistical principles and their relevance in addressing contemporary challenges.

Research Skills 

Kizito Ebere Anyiam possesses a robust skill set in statistical research, encompassing distribution theory, reliability analysis, and multivariate statistics. His proficiency in statistical computing and simulation enables him to effectively analyze complex data sets using software such as R, Stata, and SPSS. Anyiam has demonstrated his expertise through the successful supervision of over 30 undergraduate projects, many of which have been published in peer-reviewed journals. His research focuses on new generalized lifetime distributions and their applications, showcasing his ability to innovate within the field. Moreover, he actively engages in collaborative research efforts, evidenced by his numerous publications in reputable journals. His strong foundation in mathematical statistics allows him to tackle real-world problems through applied probability and statistical modeling. Overall, Anyiam’s research skills, combined with his commitment to teaching and mentoring, position him as a valuable contributor to the field of statistics.

Award and Recognition 

Kizito Ebere Anyiam, a Senior Lecturer in the Department of Statistics at the Federal University of Technology, Owerri, has garnered significant recognition for his contributions to the field of statistics. He earned his PhD in 2023 from Nnamdi Azikiwe University, where his dissertation on generalized exponentiated Weibull families showcased innovative approaches to lifetime distributions. His research interests encompass distribution theory, reliability analysis, and statistical computing, leading to multiple peer-reviewed publications in reputable journals, such as Heliyon and the Journal of Modern and Applied Statistical Methods. Additionally, Anyiam’s commitment to education is evident through his development of innovative teaching methods, supervising over 30 undergraduate projects, many of which have been published. His active involvement in professional organizations, including the Nigeria Statistical Association and the Nigerian Mathematical Society, further emphasizes his dedication to advancing statistical research and education in Nigeria.

Conclusion

Kizito Ebere Anyiam is a strong candidate for the Best Researcher Award due to his impressive academic background, extensive teaching and supervisory experience, and significant contributions to statistical research. His commitment to student engagement and professional development further underscores his suitability for this recognition. By addressing areas for improvement, such as expanding his research scope and increasing his visibility in the academic community, Anyiam can further enhance his impact as a researcher and educator. Awarding him this honor would not only recognize his past achievements but also encourage his continued growth and contributions to the field of statistics.

Publication Top Notes
  1. A novel feature selection framework for incomplete data
  2. Iterative missing value imputation based on feature importance
  3. KNCFS: Feature selection for high-dimensional datasets based on improved random multi-subspace learning

 

 

SAI KRISHNA MANOHAR CHEEMAKURTHI | Computer Science | Best Researcher Award

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Mr. Sai Krishna Manohar Cheemakurthi | Computer Science | Best Researcher Award

Sai Krishna Manohar Cheemakurthi, U.S. BANK, United States.

Sai Krishna Manohar Cheemakurthi is a seasoned IT professional with over 8 years of experience specializing in Big Data Analytics, Splunk architecture, and cloud-based solutions. He holds numerous certifications, including Splunk Core Certified Consultant and AWS Solutions Architect. Sai Krishna has expertise in designing and implementing Splunk infrastructure for both on-premises and cloud environments, particularly on AWS and Azure. His strong technical background includes scripting in Python, Shell, and Perl, and experience with Hadoop, RDBMS, and various data warehousing tools. Sai Krishna has led teams in migrating vast amounts of data, optimizing infrastructure costs, and enhancing performance through DevOps practices. His research work has been published in reputed journals, covering topics like data science analytics and secure cloud storage. His leadership roles at major financial institutions demonstrate his ability to drive technical innovation and efficiency in complex, large-scale environments.

Profile:

Education

Sai Krishna Manohar Cheemakurthi has a strong educational background that forms the foundation of his expertise in Information Technology and Big Data Analytics. He holds a Bachelor’s degree in Electronics and Communication Engineering, which equipped him with the fundamental skills in computer systems, software engineering, and electronics. His academic training in engineering has allowed him to develop a solid technical understanding of various programming languages, including Python, C++, and Java. Complementing his formal education, Sai Krishna has pursued multiple industry-recognized certifications such as AWS Certified Solutions Architect, Splunk Core Certified Consultant, and Proofpoint Certified Insider Threat Specialist. These certifications demonstrate his commitment to staying at the forefront of technology trends and expanding his knowledge in cloud computing, cybersecurity, and big data platforms. His blend of formal education and specialized certifications enables him to effectively architect and implement advanced IT solutions for a range of business challenges.

Professional Experiences 

Sai Krishna Manohar Cheemakurthi is an accomplished IT professional with over 8 years of experience in Big Data Analytics, Splunk architecture, and cloud solutions. Currently serving as Vice President – Lead Infrastructure Engineer at U.S. Bank, he leads a team in designing and implementing scalable Splunk infrastructures across global regions, optimizing costs, and automating processes. Previously, he was Vice President – Global Splunk Architect at Brown Brothers Harriman & Co., where he managed a global team and drove automation and cloud security solutions. As a Senior Splunk Architect at First Republic Bank, Sai Krishna successfully migrated large-scale Splunk infrastructures from on-premise to cloud platforms, improving disaster recovery and performance. His extensive experience includes leveraging AWS, Azure, Ansible, and Terraform to streamline operations, implementing DevOps methodologies, and delivering robust business intelligence solutions. Throughout his career, Sai Krishna has demonstrated strong leadership, technical expertise, and a commitment to innovation and optimization.

Awards and Honors

Sai Krishna Manohar Cheemakurthi has been recognized for his outstanding contributions in the field of Information Technology, particularly in Big Data Analytics and Splunk Architecture. His technical expertise and leadership have earned him numerous certifications, including Splunk Core Certified Consultant, Splunk Enterprise Certified Architect, and AWS Certified Solutions Architect, showcasing his proficiency in cloud and data platforms. He holds certifications in Sumo Logic, Proofpoint, and IBM’s Big Data Fundamentals, further enhancing his capabilities in cybersecurity and data analysis. His achievements extend to academia, where he has authored multiple research papers published in prestigious journals such as IOSR Journals and Elixir International Journal. These papers focus on cloud computing, wireless sensor networks, and quantum key distribution, demonstrating his innovative approach to solving complex challenges in IT. Sai Krishna’s ability to seamlessly integrate technical expertise with research and practical application has solidified his reputation as a leader in his domain.

Research Interest

Sai Krishna Manohar Cheemakurthi’s research interests focus on leveraging cutting-edge technologies in big data analytics, cloud computing, and cybersecurity to optimize IT infrastructure and improve data-driven decision-making. With a strong foundation in Splunk architecture, he explores advanced methods for data ingestion, transformation, and analysis, aiming to enhance the performance and security of enterprise systems. His work spans cloud migration strategies, particularly from on-premise to cloud environments like AWS, and includes innovative solutions such as quantum key distribution and secure data storage in cloud computing. Sai Krishna is also interested in the development of scalable solutions for monitoring and responding to security incidents in real-time using SIEM technologies. His research extends to cost optimization strategies, automation, and the integration of machine learning in data analytics, reflecting a forward-thinking approach to emerging trends in IT infrastructure and cybersecurity.

Research Skills

Sai Krishna Manohar Cheemakurthi possesses exceptional research skills honed over 8+ years in Information Technology, specializing in Big Data Analytics and Splunk Architecture. He is adept at designing, implementing, and optimizing complex infrastructures, focusing on Splunk and cloud technologies like AWS and Azure. His research interests include secure data management, cloud migration, and cost optimization, reflected in his publications on data analytics, cloud computing, and wireless sensor networks. Sai has a proven ability to conduct deep analysis of vast datasets, using tools like Splunk, Hadoop, and various BI platforms to generate actionable insights. He has demonstrated proficiency in developing proof-of-concept solutions for enhanced infrastructure health and performance. His expertise in scripting languages (Python, Shell, Perl) enables automation and innovative approaches in data ingestion, security monitoring, and system upgrades. Sai’s strong technical acumen, combined with a focus on optimizing IT processes, underscores his impactful contributions to the field.

Publication Top Notes
  • Cloud Observability In Finance: Monitoring Strategies For Enhanced Security
    • Authors: NB Kilaru, SKM Cheemakurthi
    • Year: 2023
    • Journal: NVEO-Natural Volatiles & Essential Oils
    • Volume/Issue/Page: 10(1), 220-226
  • Mitigating Threats in Modern Banking: Threat Modeling and Attack Prevention with AI and Machine Learning
    • Authors: SK Manohar, V Gunnam, NB Kilaru
    • Year: 2022
    • Journal: Turkish Journal of Computer and Mathematics Education (TURCOMAT)
  • Next-gen AI and Deep Learning for Proactive Observability and Incident Management
    • Authors: NBK Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam
    • Year: 2022
    • Journal: Turkish Journal of Computer and Mathematics Education
    • Volume/Issue/Page: 13(3), 1550-1564
  • Scaling DevOps with Infrastructure as Code in Multi-Cloud Environments
    • Authors: NBK Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam
    • Year: 2022
    • Journal: Turkish Journal of Computer and Mathematics Education
    • Volume/Issue/Page: 13(3), 1189-1200
  • Advanced Anomaly Detection In Banking: Detecting Emerging Threats Using SIEM
    • Authors: NBK Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam
    • Year: 2021
    • Journal: International Journal of Computer Science and Mechatronics (IJCSM)
    • Volume/Issue/Page: 7(04), 28-33
  • Analytics of Data Science using Big Data
    • Authors: CSK Manohar
    • Year: 2013
    • Journal: IOSR Journal of Computer Engineering
    • Volume/Issue/Page: 10(2), 19-21
  • AI-Powered Fraud Detection: Harnessing Advanced Machine Learning Algorithms for Robust Financial Security
    • Authors: SKM Cheemakurthi, NB Kilaru, V Gunnam
    • Year: (Not provided)
  • Deep Learning Models For Fraud Detection In Modernized Banking Systems: Cloud Computing Paradigm
    • Authors: Y Vasa, SKM Cheemakurthi, NB Kilaru
    • Year: (Not provided)
  • SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security
    • Authors: NB Kilaru, SKM Cheemakurthi, V Gunnam
    • Year: (Not provided)
  • AI-Driven SOAR in Finance: Revolutionizing Incident Response and PCI Data Security with Cloud Innovations
    • Authors: V Gunnam, SKM Cheemakurthi, NB Kilaru
    • Year: (Not provided)

 

 

Navid Ghaffarzadeh | Engineering | Best Researcher Award

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Assoc Prof Dr. Navid Ghaffarzadeh | Engineering | Best Researcher Award

Assoc Prof Dr. Navid Ghaffarzadeh, Imam Khomeini International University, Iran

Assoc Prof Dr. Navid Ghaffarzadeh is an accomplished engineer recognized for his innovative contributions to the field of engineering. With a focus on [specific area of expertise], he has been instrumental in advancing research and development initiatives. His dedication and impactful work earned him the prestigious Best Researcher Award, highlighting his commitment to excellence and collaboration. Navid continues to inspire through his research, aiming to drive advancements that benefit both industry and society.

 

Profile:

Education

Navid Ghaffarzadeh earned his PhD in Electrical Engineering from Iran University of Science and Technology in Tehran, completing his studies from September 2007 to April 2011. Prior to that, he obtained his Master of Science in Electrical Engineering from Amirkabir University of Technology (Tehran Polytechnic) between September 2005 and August 2007. He also holds a Bachelor of Science in Electrical Engineering from Zanjan University, where he studied from September 2001 to June 2005.

Professional Activities

Navid Ghaffarzadeh is actively engaged in the academic community as a reviewer for numerous prestigious journals in the field of electrical engineering. His reviewing contributions span a wide array of publications, including Renewable and Sustainable Energy Reviews, Applied Energy, Journal of Energy Storage, and IEEE Transactions on Power Systems, among others, with impact factors ranging from 1.276 to 16.799. With over 100 reviewed journal papers, Navid plays a vital role in advancing research quality and integrity in the field. His extensive experience demonstrates his commitment to fostering innovation and excellence in engineering research.

Research Interests

Navid Ghaffarzadeh’s research interests encompass a wide range of cutting-edge topics in electrical engineering. He focuses on renewable energy, exploring innovative solutions in battery energy storage systems and electric vehicles. His work in microgrid and smart grid design aims to enhance the efficiency and reliability of power systems. Navid is particularly interested in the application of artificial intelligence in renewable energy systems, as well as power systems protection and transients. Additionally, he investigates intelligent systems and optimization techniques to improve power systems, with a strong emphasis on ensuring power quality.

Honors and Awards: ‌

Navid Ghaffarzadeh has received numerous honors and awards throughout his academic and professional career. In 2012, he was honored with the IET Science, Measurement and Technology Premium Award for his outstanding paper on power quality disturbances, recognized as one of the best published in the journal. He has been named Outstanding Researcher at I.K International University multiple times, in 2013, 2014, 2016, and 2020, and has also received the Outstanding Professor award in 2017, 2019, 2020, 2021, and 2023. Additionally, he was awarded the Best Iranian PhD Dissertation in power system protection, highlighting his significant contributions to the field. Navid achieved top rankings in his studies, finishing first among PhD electrical power engineering students at Iran University of Science and Technology with a GPA of 18.72 out of 20, first among M.Sc. students at Amirkabir University of Technology with a GPA of 19.18 out of 20, and first among B.Sc. students at Zanjan University with a GPA of 18.36 out of 20.

 

Publication Top Note

A. Bamshad, N. Ghaffarzadeh, “A novel smart overcurrent protection scheme for renewables-dominated distribution feeders based on quadratic-level multi-agent system (Q-MAS),” Electrical Engineering, vol. 105, pp. 1497–1539, February 2023.

S. Ansari, N. Ghaffarzadeh, “A Novel Superimposed Component-Based Protection Method for Multi Terminal Transmission Lines Using Phaselet Transform,” IET Generation, Transmission & Distribution, vol. 17, no. 1, pp. 469–485, January 2023.

A. HN. Tajani, A. Bamshad, N. Ghaffarzadeh, “A novel differential protection scheme for AC microgrids based on discrete wavelet transform,” Electric Power Systems Research, vol. 220, pp. 1-12, July 2023.

A. Zarei, N. Ghaffarzadeh, “Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM,” Journal of Solar Energy Research, vol. 8, no. 2, pp. 1367-1379, April 2023.

M. Dodangeh, N. Ghaffarzadeh, “A New Protection Method for MTDC Solar Microgrids using on-line Phaselet, Mathematical Morphology, and Signal Energy Analysis,” Energy Engineering & Management, vol. 13, no. 1, pp. 40-53, March 2023 (in Persian).

M. Dodangeh, N. Ghaffarzadeh, “An Intelligent Protection Method for Multi-terminal DC Microgrids Using On-line Phaselet, Mathematical Morphology, and Fuzzy Inference Systems,” Energy Engineering & Management, vol. 12, no. 2, pp. 12-25, August 2022 (in Persian).

M. Dodangeh, N. Ghaffarzadeh, “Optimal Location of HTS-FCLs Considering Security, Stability, and Coordination of Overcurrent Relays and Intelligent Selection of Overcurrent Relay Characteristics in DFIG Connected Networks Using Differential Evolution Algorithm,” Energy Engineering & Management, vol. 10, no. 2, pp. 14-25, May 2020 (in Persian).

A. Inanloo Salehi, N. Ghaffarzadeh, “Fault detection and classification of VSC-HVDC transmission lines using a deep intelligent algorithm,” International Journal of Research and Technology in Electricity Industry, vol. 1, no. 2, pp. 161-170, September 2022.

N. Ghaffarzadeh, H. Faramarzi, “Optimal Solar plant placement using holomorphic embedded power flow considering the clustering technique in uncertainty analysis,” Journal of Solar Energy Research, vol. 7, no. 1, pp. 997-1007, Winter 2022.

N. Ghaffarzadeh, A. Bamshad, “A new approach to AC microgrids protection using a bi-level multi-agent system,” International Journal of Research and Technology in Electricity Industry, vol. 1, no. 1, pp. 66-74, March 2022.

Amel SAHLI | Computer Science | Best Researcher Award

Posted on by Shen Awards

MS. Amel SAHLI | Computer Science | Best Researcher Award

École Nationale des Sciences de l’Informatique , Tunisia

Amel Sahli is a dedicated researcher pursuing her PhD in computer science at the École Nationale des Sciences de l’Informatique in Tunisia, focusing on optimizing e-learning processes through AI and key performance indicators. She holds a Master’s degree in information systems and has published significant work on performance measurement in education. Sahli’s diverse professional background includes roles as a contract lecturer and various internships, providing her with practical insights and teaching experience. Her technical skills in programming and web development, coupled with her proficiency in Arabic, French, and English, enhance her ability to engage with the international research community. Amel Sahli’s commitment to advancing educational methodologies through her research makes her a strong candidate for the Best Researcher Award, highlighting her potential to contribute meaningfully to the field of education technology.

 

Profile:

Education

Amel Sahli is currently pursuing her PhD in computer science at the École Nationale des Sciences de l’Informatique (ENSI) in Tunisia. Her doctoral research focuses on developing an integrated approach that leverages artificial intelligence (AI) and key performance indicators (KPIs) to optimize e-learning processes. Prior to her PhD, she earned a Master’s degree in information systems and web technologies, where she studied performance measurement in educational settings. This followed her Bachelor’s degree in computer science, during which she designed and implemented web applications for educational management. Sahli’s academic journey has been marked by consistent excellence, earning distinctions in her studies and developing a strong foundation in both theoretical and practical aspects of computer science. Her educational background not only highlights her technical competencies but also underscores her commitment to advancing the field of education through innovative research.

Professional Experiences

Amel Sahli has gained diverse professional experience that enriches her academic pursuits. She began her career as a bank intern and a counter agent, where she honed her customer service and operational skills. Following these roles, she interned at the Institut Supérieur d’Informatique du Kef, further deepening her understanding of information technology in educational contexts. In 2023, she transitioned into academia as a part-time lecturer, sharing her expertise in computer science with students. Currently, Sahli is engaged in research at the RIADI laboratory at the Université de la Manouba, where she applies her knowledge of artificial intelligence and KPIs to enhance e-learning processes. This combination of practical experience and academic engagement positions her as a well-rounded professional, capable of bridging theory and practice effectively. Sahli’s journey reflects her commitment to continuous learning and development in both research and teaching.

Research Skills

Amel Sahli possesses a robust set of research skills that are essential for her academic pursuits. Her expertise in quantitative and qualitative research methodologies allows her to design comprehensive studies that yield meaningful insights. Proficient in data analysis, Sahli employs statistical tools to interpret complex datasets, ensuring her findings are both reliable and impactful. Additionally, her experience in academic writing and publication equips her to effectively communicate her research outcomes to diverse audiences. Sahli’s ability to critically evaluate existing literature enables her to identify gaps in knowledge, guiding her own research questions. Her strong organizational skills facilitate the management of research projects, from initial conception to final execution. Moreover, her proficiency in various programming languages and web development enhances her capability to create innovative solutions within her research, particularly in optimizing e-learning processes. Overall, Sahli’s comprehensive research skill set positions her as a valuable contributor to the field of computer science and education technology.

Award and Recognition

Amel Sahli has been recognized for her outstanding contributions to the field of computer science and education. Notably, she participated in the “Inspiring Research & Innovation Using IEEE Publications” event, demonstrating her commitment to advancing research practices. Additionally, she attended the “23rd International Conference on Intelligent Systems Design and Applications,” where she engaged with leading experts and shared her insights. Her certifications from prestigious organizations, including Google and Microsoft, further attest to her dedication to continuous learning and professional development. Moreover, Sahli’s article on performance measurement in educational processes has been published in Procedia Computer Science, enhancing her visibility in academic circles. These recognitions not only reflect her hard work and innovation but also position her as a rising star in her field, earning her respect among peers and contributing to her eligibility for the Best Researcher Award.

Conclusion

In conclusion, Amel Sahli exemplifies the qualities sought in a candidate for the Best Researcher Award. Her academic journey, characterized by a robust educational background in computer science and information systems, has equipped her with the necessary tools to conduct meaningful research. Her focus on optimizing e-learning processes through the integration of AI and KPIs showcases her innovative approach to addressing contemporary educational challenges. Furthermore, her contributions to peer-reviewed journals and participation in international conferences illustrate her commitment to advancing knowledge in her field. Sahli’s diverse professional experiences, ranging from teaching to research, highlight her multifaceted skill set and adaptability. With her proficiency in multiple languages and technical expertise, she stands out as a collaborative researcher poised to make a lasting impact in education technology. Thus, Amel Sahli is not only a deserving nominee but also a potential leader in shaping the future of educational practices.

Publication Top Note

  • Conference Paper in Procedia Computer Science
    • Title: Performance Measurement of Reading Teaching-Learning Business Processes: Case of Whole-Word and Syllabic Reading Methods in Primary Schools
    • Authors: Amel Sahli, A. Mejri, A. Louati
    • Year: 2024
    • Citations: 0
  • Conference Paper in Lecture Notes in Networks and Systems
    • Title: Performance Measurement of Reading Teaching-Learning Business Processes: Case of Whole-Word and Syllabic Reading Methods in Primary Schools
    • Authors: Amel Sahli, A. Mejri, A. Louati
    • Year: 2024
    • Citations: 0