Dr. Mani shekhar Gupta | AI in Network System | Excellence in Research

Dr. Mani shekhar Gupta | AI in Network System | Excellence in Research Award

Dr. Mani shekhar Gupta | Adani University, Ahmedabad | India

πŸ“š Dr. Mani Shekhar Gupta is an Assistant Professor at Adani University, Ahmedabad, with a Ph.D. in Electronics and Communication Engineering from NIT Hamirpur. πŸš€ His research spans cognitive radio networks, vehicular networks, resource allocation, AI, and next-gen wireless technologies. πŸ“‘ With over 11 years of academic and research experience, he has contributed significantly through projects at IIT Delhi and NIT Hamirpur. πŸ‘¨β€πŸ« A passionate educator and innovator, Dr. Gupta excels in machine learning, green networks, and intelligent transportation systems. πŸ’‘ His dynamic approach blends technical expertise with a love for teaching and discovery. 🌟

Professional Profile:

Google Scholar

Suitability for the Excellence in Research Award

Dr. Mani Shekhar Gupta is highly suitable for the Excellence in Research Award due to his extensive academic background, impactful research contributions, and innovative approaches in the fields of cognitive radio networks, vehicular networks, resource allocation, artificial intelligence, and next-generation wireless technologies. His career, spanning over 11 years, reflects a deep commitment to advancing technological frontiers and fostering academic excellence.

Education πŸ“– & Experience πŸ‘¨β€πŸ’ΌΒ 

  • πŸ“œ Ph.D. in Electronics & Communication Engineering, NIT Hamirpur (2017–2021) – CGPI 9.5
  • 🎯 M.Tech. in Electronics & Communication Engineering, NIT Hamirpur (2009–2011) – CGPI 8.49
  • πŸ… B.Tech. in Electronics & Communication, UPTU, Lucknow (2005–2009) – 74.42%
  • πŸ‘¨β€πŸ« Assistant Professor, Adani University (2022–Present)
  • πŸ”¬ Postdoctoral Researcher, IIT Delhi (2021–2022)
  • πŸŽ“ Ph.D. Research Scholar, NIT Hamirpur (2017–2021)
  • πŸ‘¨β€πŸ’Ό Assistant Professor, PSIT Kanpur (2011–2017)

Professional DevelopmentΒ 

🌐 Dr. Gupta actively engages in continuous professional growth through memberships in global organizations like IEEE πŸ“‘, EAI πŸ‡ͺπŸ‡Ί, IACSIT πŸ‡ΈπŸ‡¬, IAENG πŸ‡­πŸ‡°, and IAAM 🌍. His participation spans technical communities focusing on e-Government, IoT 🌐, Smart Cities πŸ™οΈ, and Autonomous Driving πŸš—. He’s also a member of humanitarian groups like IEEE SIGHT 🀝. Through conferences, workshops, and collaborative projects, Dr. Gupta refines his expertise in wireless networks, machine learning πŸ€–, and green technologies 🌱, ensuring he stays at the forefront of innovation and academic excellence. πŸš€

Research FocusΒ 

πŸ” Dr. Gupta’s research focuses on cognitive radio networks πŸ“‘, vehicular networks πŸš—, and resource allocation strategies for next-generation wireless systems πŸ“Ά. His work integrates AI πŸ€– and machine learning to enhance spectrum management, optimize network efficiency, and support intelligent transportation systems 🚦. He explores green network technologies 🌱, aiming to reduce environmental impact while improving connectivity. His contributions to 5G and beyond involve proactive spectrum sharing, game theory applications 🎯, and cooperative uplink-downlink strategies, making his research pivotal for smart cities πŸ™οΈ and sustainable communication infrastructures. 🌍

Awards & Honors πŸ†Β 

(No specific awards or honors mentioned in the provided details. If you have any, please share for accurate updates.)

  • πŸ… IEEE Membership & Active Roles in Multiple Societies
  • 🌟 Recognized Contributor in DST-SERB & BASF Research Projects
  • πŸ“œ International Memberships: IACSIT πŸ‡ΈπŸ‡¬, IAENG πŸ‡­πŸ‡°, IAAM 🌍

Publication Top Notes:

πŸ“‘ Progression on Spectrum Sensing for Cognitive Radio Networks: A Survey, Classification, Challenges, and Future Research Issues Β πŸ“‘ Cited by 164
🌿 Energy Efficient Transmission Trends Towards Future Green Cognitive Radio Networks (5G): Progress, Taxonomy, and Open Challenges πŸ“‘ Cited by 114
πŸš— Application Aware Networks’ Resource Selection Decision Making Technique Using Group Mobility in Vehicular Cognitive Radio Networks Β πŸ“‘ Cited by 32
πŸ“Ά A Survey on NOMA Techniques for 5G Scenario Β πŸ“‘ Cited by 24
πŸ”€ Seamless Vertical Handover for Efficient Mobility Management in Cooperative Heterogeneous Networks πŸ“‘ Cited by 20

 

 

 

Dr. Julius Olaniyan | Machine Learning Award |Best Researcher Award

Dr. Julius Olaniyan | Machine Learning Award |Best Researcher Award

Dr. Julius Olaniyan, Bowen University, NigeriaΒ 

Olaniyan JuliusΒ in Odo-Owa, Kwara State, Nigeria. He is a Lecturer II in the Computer Science Department at Bowen University, Iwo, Osun State, Nigeria. Julius holds a Ph.D. in Computer Science (2023) and has extensive experience in software development, data analysis, and teaching. He has worked in several institutions, including Landmark University, Federal Polytechnic Auchi, and Feghas Solutions Ltd. Over his career, he has developed various applications using programming languages such as C, C++, Java, Python, and PHP. Julius specializes in Artificial Intelligence, Computer Vision, Natural Language Processing, and Machine Translation. A devoted husband and father of three, Julius is passionate about advancing AI and its application in healthcare and education. He has contributed to several innovative research papers in the field of computer science and AI.

Professional Profile:

Google Scholar

Summary of Suitability for Award:

Dr. Olaniyan has demonstrated outstanding proficiency and expertise in the fields of Artificial Intelligence, Computer Vision, Natural Language Processing, and Machine Translation, with a solid academic background in Computer Science. He holds a Ph.D. in Computer Science from Landmark University, and has published extensively in high-impact journals and conferences. His work on cataract detection using deep learning, as well as his innovative contributions in areas like speech refinement and emotion recognition, highlights his commitment to advancing technology for real-world applications. Furthermore, his ability to collaborate across interdisciplinary research teams and contribute to several peer-reviewed articles reflects his academic rigor and leadership.

πŸŽ“Education:Β 

Olaniyan Julius completed his Ph.D. in Computer Science at Landmark University (2023). He also holds a Master’s in Computer Science (M.Tech) from the Federal University of Technology, Akure (2019), where he also earned a Postgraduate Diploma (PGD) in 2012. Julius started his academic journey with a Bachelor’s in Computer Science from the Federal University of Oye Ekiti (2022). His earlier qualifications include a Higher National Diploma (HND) in Computer Science from Auchi Polytechnic (2006), and a National Diploma (ND) in the same field (2000). Julius completed his Secondary Education at Orota Community High School, Odo-Owa (1994) and his Primary Education at St. Thomas Catholic School (1988). His strong educational foundation in Computer Science has shaped his successful academic and professional career.

🏒Work Experience:

Olaniyan Julius has a diverse career in academia and industry. He is currently a Lecturer II at Bowen University, Nigeria. Previously, he served as a Lecturer II at Landmark University (2023-2024) and as a Data Analyst at Federal Polytechnic Auchi (2013-2022). His industry experience includes working as a Software Developer/Business Developer at Feghas Solutions Ltd. (2009-2012) and a Tutor/Application Developer at Pesoka Systems Ltd. (2008). Julius also has teaching experience from his time as a Lecturer during his NYSC service at Maritime Academy of Nigeria (2007-2008). His early career included roles like Data Processing Officer at Ajaokuta Steel Company (2002-2004) and School Database Admin at Sani Bello Secondary School (2001). Julius’s experience spans academic teaching, research, software development, data analysis, and project management.

πŸ…Awards:

Olaniyan Julius has received numerous accolades throughout his academic and professional journey. His Ph.D. dissertation was highly recognized, contributing to his recognition as an emerging scholar in Computer Science. He was awarded a best student award during his time at Landmark University and has been recognized by the Federal Polytechnic Auchi for his outstanding performance as a Data Analyst. Julius’s commitment to education and research has earned him several institutional commendations for his efforts in developing AI-driven solutions in healthcare and education. His research in Artificial Intelligence and Machine Translation has garnered him recognition at international conferences. He is also an active member of several professional organizations in computer science and artificial intelligence. Julius’s leadership and contributions to academic and professional initiatives have cemented his reputation as a passionate educator and researcher.

πŸ”¬Research Focus:

Olaniyan Julius specializes in Artificial Intelligence (AI), with a focus on Computer Vision, Natural Language Processing (NLP), and Machine Translation. His work primarily involves using deep learning techniques to create solutions for healthcare (e.g., cataract detection) and education (e.g., student performance evaluation). Julius is dedicated to developing hybrid AI models that combine traditional methods with transformative learning approaches. His research in audio signal denoising and speech-to-speech translation aims to enhance communication and multilingual interaction. He is passionate about designing AI-powered systems that can automate and optimize processes, improving outcomes in health diagnostics and online learning environments. Julius’s work on emotion detection in virtual classrooms and the integration of CNN models for speech emotion recognition represents a significant contribution to the AI field. His interdisciplinary research approach holds promise for real-world AI applications in various domains.

Publication Top Notes:Β 

  • “Utilizing an Attention-Based LSTM Model for Detecting Sarcasm and Irony in Social Media”
  • “Implementation of Audio Signals Denoising for Perfect Speech-to-Speech Translation Using Principal Component Analysis”
  • “Advancements in Accurate Speech Emotion Recognition Through the Integration of CNN-AM Model”
  • “Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection”
  • “Parallel Attention Driven Model for Student Performance Evaluation”