Worku Chekol | Cybersecurity And Cryptography | Best Researcher Award

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Mr. Worku Chekol, Cybersecurity And Cryptography, Best Researcher Award

Worku Chekol at Teda Health Science College, Ethiopia

Mr. Worku Chekol is a dedicated academic and researcher in the field of medical nursing, currently serving as a Lecturer and Research Coordinator at Teda Health Science College in Ethiopia. He holds a Master of Medical Nursing degree from the University of Gondar, where he graduated with a perfect CGPA of 4.00 in 2012. He also earned his Bachelor of Science in Nursing from Aksum University in 2007, achieving a CGPA of 3.81. Since 2008, Mr. Chekol has been imparting knowledge and skills to students at Teda Health Science College, where he teaches various clinical and health-related courses. In addition to his teaching responsibilities, he has been the Research Coordinator at the same institution since September 2014. His research focuses on critical health issues, with publications addressing acute kidney injury in diabetes patients, herbal medicine use, hyperglycemic emergencies, self-medication practices among pregnant women, hygienic practices in complementary food preparation, eye care service utilization among diabetic patients, and precancerous cervical lesions in HIV-infected women. Mr. Chekol is fluent in both Amharic and English and possesses strong skills in working under pressure, multitasking, and effective communication. He is proficient in several computer applications, including MS Word, MS PowerPoint, MS Excel, SPSS, STATA, EPI INFO, EpiData, and Open Code. His commitment to serving the community and his ability to adapt to challenging environments highlight his dedication to the field of healthcare and education.

Profile:

Education:

Mr. Worku Chekol holds a Master of Medical Nursing degree from the University of Gondar, Ethiopia, which he completed in 2012 with an outstanding Cumulative Grade Point Average (CGPA) of 4.00. Prior to this, he earned a Bachelor of Science degree in Nursing from Aksum University, Ethiopia, in 2007, achieving a CGPA of 3.81. His academic excellence has been a cornerstone of his professional career.

Professional Experience:

Mr. Worku Chekol has been serving as a lecturer at Teda Health Science College since 2008, where he delivers various clinical and health-related courses to students. Since September 2014, he has also taken on the role of Research Coordinator at the same institution, actively engaging in research and academic duties. His dual role as a lecturer and researcher allows him to contribute significantly to both the educational and research fields within the college.

Research Interest:

Mr. Worku Chekol’s research interests encompass a wide range of health-related topics, with a particular focus on the incidence and predictors of acute kidney injury among newly diagnosed type 2 diabetes patients, the prevalence and associated factors of herbal medicine use among patients with chronic diseases, and the outcomes of hyperglycemic emergencies among diabetic patients in Ethiopia. He also investigates self-medication practices among pregnant women, hygienic practices in complementary food preparation for children aged 6-24 months, eye care service utilization among diabetic patients in Africa, and precancerous cervical lesions among HIV-infected women. His work involves systematic reviews and meta-analyses to provide comprehensive insights into these critical health issues.

SKILLS:

Mr. Worku Chekol is fluent in both Amharic and English, which allows him to effectively communicate and collaborate in diverse environments. He excels in working in complex and demanding settings, managing multiple tasks with short deadlines, and performing under intense pressure. His strong communication skills, eagerness to learn, and willingness to collaborate with others make him an effective team player. Mr. Chekol is committed to serving the community and is capable of working in challenging conditions. Additionally, he possesses basic computer skills and is proficient in various software programs, including MS Word, MS PowerPoint, MS Excel, SPSS, STATA, EPI INFO, epi data, and Open Code.

 

 

 

Agerie Mengistie | Intrusion Detection | Best Researcher Award

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Ms. Agerie Mengistie | Intrusion Detection | Best Researcher Award

Intrusion Detection at College of Medicine and Health Sciences, University of Gondar, Ethiopia

Agerie Mengistie is a dedicated professional in the field of midwifery and maternal health, based in Gondar, Ethiopia. With a Bachelor of Science in Midwifery from the University of Gondar and a Master’s degree in Clinical Midwifery from Bahir Dar University, she has honed her expertise in providing comprehensive maternity healthcare services. Currently serving as a Lecturer and Researcher at the University College of Tedda Health Sciences, she plays a pivotal role in monitoring and enhancing the quality of public health research activities. Agerie has a passion for research and has successfully conducted thesis work at both undergraduate and postgraduate levels, demonstrating her commitment to advancing knowledge in her field. With a strong foundation in research methodology and proficiency in statistical software, she is well-equipped to contribute to evidence-based practices and improve maternal health outcomes in Ethiopia.

Profile:

Education

Ms. Agerie Mengistie holds a Master’s degree in Clinical Midwifery (MSc) from Bahir Dar University, Ethiopia, which she completed between February 2014 and June 2016. Prior to her Master’s degree, she obtained a Bachelor of Science in Midwifery (BSc) from the University of Gondar, Ethiopia, from May 2009 to March 2012. These academic qualifications have equipped her with a solid foundation in midwifery and clinical practices, enabling her to pursue a career focused on maternal and reproductive health.

Professional Experience

Ms. Agerie Mengistie has accumulated valuable professional experience in the field of midwifery and healthcare. Since May 2016, she has served as a Lecturer and Researcher in Clinical Midwifery at the University College of Tedda Health Sciences in Gondar, Ethiopia. In this role, she has undertaken various responsibilities, including monitoring and coordinating quality public health research activities, conducting and supervising thesis work for both undergraduate and postgraduate students, assisting senior professors in teaching and research, and contributing to manuscript preparation for peer-reviewed journals. Additionally, Ms. Mengistie has been actively involved in community diagnosis research as part of a team training program, providing research methodology training, and monitoring and evaluating public health services. Her commitment to education and research has significantly contributed to the advancement of midwifery education and healthcare services in her community.

Research Interest

Ms. Agerie Mengistie’s research interests lie in the intersection of midwifery, maternal healthcare, and public health. Specifically, she is passionate about exploring topics related to improving maternal and neonatal health outcomes, enhancing the quality of midwifery care, and advancing evidence-based practices in midwifery education and clinical practice. She is keen on conducting research that addresses key challenges in maternal healthcare delivery, such as reducing maternal mortality and morbidity rates, promoting safe childbirth practices, and ensuring access to comprehensive reproductive health services. Additionally, Ms. Mengistie is interested in investigating innovative approaches to midwifery education and training, as well as exploring the impact of cultural, social, and economic factors on maternal and child health outcomes. Through her research endeavors, she aims to contribute to the development of effective strategies and policies aimed at improving maternal and neonatal health outcomes in Ethiopia and beyond.

Award and Honors

Ms. Agerie Mengistie has been recognized for her dedication and contributions in the field of midwifery and maternal healthcare. She was granted the Induced Abortion Service award by the Ethiopian Midwifery Association in 2018 for her outstanding service in this area. This recognition highlights her commitment to providing quality healthcare services to women in need and acknowledges her significant contributions to the field of midwifery.

Research Skills

Ms. Agerie Mengistie possesses a diverse set of research skills, enabling her to contribute effectively to the advancement of knowledge in the field of midwifery and maternal health. She is proficient in various statistical software including Stata, SPSS, Epi Info, and Epi data software, which enables her to analyze data with precision and accuracy. Additionally, she is skilled in conducting systematic reviews and meta-analyses, demonstrating her ability to synthesize and evaluate existing research literature comprehensively. Moreover, Ms. Mengistie has received training in research methodology, enhancing her capacity to design and execute high-quality research studies. Her proficiency in these research skills equips her to address complex research questions and contribute meaningfully to the evidence-based practice of midwifery.

 

Oluwafemi Oke | Cybersecurity And Cryptography | Best Researcher Award

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Mr. Oluwafemi Oke, Cybersecurity And Cryptography, Best Researcher Award

Oluwafemi Oke at Near East University, Cypus

Oke Oluwafemi is a highly motivated and skilled Ph.D. candidate in Artificial Intelligence with a strong desire for a remote position in machine learning. He has demonstrated success in leading research projects, developing AI algorithms, and implementing AI solutions across various industries. Oke possesses expertise in machine learning, natural language processing, and computer vision. His proficiency extends to languages such as Python, and frameworks including TensorFlow and PyTorch. He holds a Bachelor’s degree in Computer Engineering, a Master’s degree in Computer Science with a focus on Software Engineering, and is currently pursuing his Doctor of Philosophy in Computer Information Systems with a concentration in Artificial Intelligence.

Education:

Babcock University

  • Degree: Bachelor of Science in Computer Engineering
  • Duration: July 2016
  • Project: Radio Frequency Identification in Doors

Babcock University

  • Degree: Master of Science in Computer Science (Software Engineering)
  • Duration: August 2020
  • Thesis: Hybrid Intelligent Internet of Things (IOT) Systems for Automated Homes

Near East University

  • Program: Doctor of Philosophy (PhD)
  • Duration: March 2021 – Present

Profile:

Professional  Experience:

AI Engineer, Cadbury Plc, 2015

  • Developed and deployed AI-based solutions for clients in various industries.
  • Implemented machine learning algorithms for image and speech recognition, improving accuracy by 23%.

AI Consultant, Corporate Affairs Commission, 2017

  • Provided expert guidance and consulting services on AI implementation.
  • Conducted workshops on machine learning and deep learning.
  • Built and trained models for natural language processing and computer vision tasks.

Data Scientist, NEU Cardiac Centre, 2020

  • Developed a healthcare diagnostic tool using machine learning and image recognition, achieving 94% accuracy in identifying cancerous cells.
  • Conducted analysis of customer behavior using NLP on social media data, leading to a targeted marketing strategy and a 15% increase in conversions.

Machine Learning Engineer, Harvest, 2022

  • Led the development of a recommendation system using deep learning, increasing user engagement by 30% and sales by 25%.
  • Led a team to implement an autonomous vehicle navigation system, achieving 99.5% accuracy in real-world scenarios.

Research Interests:

Mr. Oluwafemi Oke has amassed a wealth of research experience across various domains of artificial intelligence (AI) and machine learning (ML). As a Research Assistant at Daxlinks in 2020, he focused on deep learning techniques within the realm of machine learning, contributing significantly to user engagement improvements by developing a novel approach that yielded a remarkable 45% increase in interaction accuracy. His tenure at GIFA INC in 2021 saw him collaborating on groundbreaking research projects exploring the intersection of AI and climate science, as well as devising a cutting-edge deep learning model for financial market forecasting, achieving an impressive 85% accuracy rate. Additionally, he played a pivotal role in enhancing language translation models using Transformer architectures, leading to a noteworthy 20% enhancement in translation accuracy, which garnered recognition within the academic community. Subsequently, as a Research Scientist at Near East University in 2022, Mr. Oke spearheaded the development of advanced algorithms for image and video analysis, resulting in the acquisition of several patents. Moreover, his contributions to research in natural language understanding culminated in multiple publications in prestigious conferences and journals. Notably, Mr. Oke led a team of researchers in the creation of an AI-based predictive maintenance system for industrial equipment, achieving a remarkable 67% reduction in downtime and a significant 97% increase in efficiency.

Publications:

Artificial Intelligence for Computer Vision: A Bibliometric Analysis

  • Year: 2023
  • Author(s): Oluwafemi Oke
  • Journal: [Journal Name] (Please insert the name of the journal where the paper was published.)
  • Volume: [Volume Number]
  • Issue: [Issue Number]
  • Pages: [Page Range]

Brain-Computer Interfaces: High-Tech Race to Merge Minds and Machines

  • Year: 2023
  • Author(s): Oluwafemi Oke
  • Journal: [Journal Name] (Please insert the name of the journal where the paper was published.)
  • Volume: [Volume Number]
  • Issue: [Issue Number]
  • Pages: [Page Range]

The Impact of Artificial Intelligence in Foreign Language Learning Using Learning Management Systems: A Systematic Literature Review

  • Year: 2023
  • Author(s): Oluwafemi Oke
  • Journal: [Journal Name] (Please insert the name of the journal where the paper was published.)
  • Volume: [Volume Number]
  • Issue: [Issue Number]
  • Pages: [Page Range]