Sandeep Belidhe | Engineering | Best Innovation Award

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Mr. Sandeep Belidhe | Engineering | Best Innovation Award

DevSecOps Engineer at Sparksoft Corp, United States

Sandeep Belidhe is a highly experienced IT professional with over 10.5 years of expertise in DevSecOps, DevOps Cloud Engineering, Release Engineering, and Middleware Administration. His career has been dedicated to integrating AI, machine learning (ML), and security automation within cloud environments to enhance operational efficiency and risk mitigation. Through his extensive research and development, he has significantly contributed to AI-driven DevSecOps, leading to multiple scholarly publications, two patents, and an authored book on AI/ML. His research has focused on bridging the gap between artificial intelligence, deep learning, and IT automation, revolutionizing the way security and efficiency are managed in cloud computing. By successfully deploying intelligent, scalable, and secure IT solutions, he has influenced industry best practices and innovation. Additionally, his role as a mentor and thought leader has allowed him to guide professionals in adopting cutting-edge AI solutions in DevOps. With a track record of innovation, leadership, and technical excellence, Sandeep continues to push the boundaries of AI-driven IT automation and security. His contributions make him a strong candidate for recognition as a top researcher in the field, further solidifying his impact on DevSecOps and AI integration in cloud computing.

Professional Profile

Education

Sandeep Belidhe has built a strong academic foundation in computer science, artificial intelligence, and cloud security, enabling him to contribute extensively to AI-integrated DevSecOps solutions. His educational journey has equipped him with advanced knowledge in software development, deep learning, cybersecurity, and automation, shaping his research and professional expertise. He holds a Bachelor’s Degree in Computer Science & Engineering, which provided him with essential skills in programming, system architecture, and IT infrastructure management. To further enhance his expertise, he pursued a Master’s Degree in Artificial Intelligence & Machine Learning, focusing on deep learning, neural networks, and AI-driven security frameworks. In addition to his formal education, he has acquired multiple industry-recognized certifications in DevSecOps, Cloud Computing, AI/ML, and Security, keeping him at the forefront of technological advancements. His continuous learning approach ensures that he stays updated with emerging trends and best practices, further enhancing his ability to drive research and innovation in AI-powered DevOps security.

Professional Experience

Sandeep Belidhe has amassed over a decade of experience in DevSecOps, Cloud Engineering, AI/ML, and Middleware Administration, working with leading technology firms and research institutions. His expertise in security automation, AI-driven DevOps, and scalable cloud architectures has allowed him to deliver innovative and high-impact IT solutions. Throughout his career, he has held various key positions, including DevSecOps Engineer, AI & ML Researcher, Middleware & Release Engineer, and Patent Innovator. As a DevSecOps and Cloud Engineer, he has played a critical role in ensuring secure, automated, and scalable IT environments. His work in AI and ML research has led to the development of intelligent security automation frameworks, contributing significantly to the field. He has also been instrumental in optimizing middleware solutions, release management, and application security, ensuring seamless CI/CD integration and operational efficiency. His pioneering research, combined with real-world applications, positions him as a leading expert in AI-driven DevSecOps, making substantial contributions to cloud security, automation, and IT infrastructure advancements.

Research Interest

Sandeep Belidhe’s research focuses on AI-driven automation, security, and scalability in cloud computing and DevSecOps. His primary goal is to develop intelligent and adaptive security solutions that enhance cloud infrastructure protection, automation, and operational efficiency. His key research areas include AI-driven DevOps security, where he integrates machine learning algorithms to predict security threats, automate compliance checks, and optimize CI/CD workflows. He is also deeply involved in deep learning and neural network applications, exploring their role in enhancing IT performance monitoring, cybersecurity, and anomaly detection. Additionally, he specializes in cloud engineering and automation, developing strategies for securing cloud-based infrastructures through AI-powered insights. His research has led to published papers, patents, and contributions to industry best practices, reinforcing his position as an innovative thought leader in AI-driven IT automation and security.

Research Skills

Sandeep Belidhe possesses a diverse set of technical and analytical skills that enable him to conduct cutting-edge research in AI, DevSecOps, and cloud security. His expertise includes AI and ML algorithm development, where he applies deep learning techniques to cybersecurity challenges, improving threat detection and automated security solutions. His knowledge in cloud security and DevSecOps allows him to build scalable and automated security infrastructures, integrating AI-driven analytics for proactive threat management. He has also mastered big data analytics and predictive security, leveraging data-driven insights to enhance IT automation and risk mitigation. Additionally, he excels in software development, middleware engineering, and automation scripting, providing the technical foundation for deploying high-performance, secure, and efficient systems. His ability to translate research into real-world applications makes him an industry leader in AI-powered DevSecOps innovations.

Awards and Honors

Sandeep Belidhe has been recognized for his groundbreaking contributions to AI, ML, DevSecOps, and cloud security, earning prestigious awards, patents, and professional honors. His ability to innovate and push the boundaries of AI-driven automation and security has positioned him as a leading researcher and industry expert. One of his most significant achievements is holding two patents in AI-integrated security solutions, which highlight his pioneering work in intelligent automation frameworks. Additionally, he has been awarded for research excellence, receiving Best Research Paper Awards for his contributions to AI-driven DevOps security. As an author, he has published a comprehensive book on AI/ML, serving as a valuable educational resource for researchers, professionals, and students. His industry certifications and recognitions further emphasize his expertise and commitment to advancing AI and DevSecOps research.

Conclusion

Sandeep Belidhe is a distinguished researcher and IT professional, with a strong background in AI, ML, DevSecOps, and cloud security. His 10.5 years of experience, combined with his patents, scholarly publications, and industry contributions, make him a key innovator in AI-driven IT automation. His commitment to research, innovation, and knowledge sharing has not only led to high-impact technological advancements but has also influenced industry best practices. By continuously mentoring professionals, collaborating with research institutions, and developing AI-powered security solutions, he has played a transformative role in DevSecOps and cloud computing. Sandeep’s ability to integrate AI-driven automation with security frameworks sets him apart as a leader in the IT industry. His dedication to continuous learning, technical excellence, and real-world applications makes him a strong candidate for recognition as a top researcher in AI-integrated DevSecOps and cloud security.

Publications Top Notes

  1. Title: Deep Fake Detection with Hybrid Activation Function Enabled Adaptive Milvus Optimization-Based Deep Convolutional Neural Network
    Authors: H. Mashetty, N. Erukulla, S. Belidhe, N. Jella, V. Reddy Pishati, B.K. Enesheti
    Year: 2025

  2. Title: Explainable AI and Deep Neural Networks for Continuous PCI DSS Compliance Monitoring
    Authors: S.K.D. Sandeep Belidhe, Phani Monogya Katikireddi
    Year: 2024

  3. Title: Applying Deep Q-Learning for Optimized Resource Management in Secure Multi-Cloud DevOps
    Authors: S. Belidhe
    Year: 2022

  4. Title: AI-Driven Governance for DevOps Compliance
    Authors: S. Belidhe
    Year: 2022

  5. Title: Transparent Compliance Management in DevOps Using Explainable AI for Risk Assessment
    Authors: S. Belidhe
    Year: 2022

  6. Title: Using Deep Reinforcement Learning to Defend Conversational AI Against Adversarial Threats
    Authors: S.K.D. Phani Monogya Katikireddi, Sandeep Belidhe
    Year: 2021

  7. Title: Machine Learning Approaches for Optimal Resource Allocation in Kubernetes Environments
    Authors: S.B. Sandeep Kumar Dasa, Phani Monogya Katikireddi
    Year: 2021

  8. Title: Intelligent Cybersecurity: Enhancing Threat Detection through Hybrid Anomaly Detection Techniques
    Authors: S.B. Phani Monogya Katikireddi, Sandeep Kumar Dasa
    Year: 2021

  9. Title: Optimizing Object Detection in Dynamic Environments with Low-Visibility Conditions
    Authors: S. Belidhe, S.K. Dasa, S. Jaini

Saeed Mohsen Abosreea | Artificial Intelligence Engineering | Best Researcher Award

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Dr. Saeed Mohsen Abosreea | Artificial Intelligence Engineering | Best Researcher Award

Clinical Associate Professor at Department of Otorhinolaryngology-Head and Neck Surgery Yongin Severance Hospital, Yonsei University College of Medicine, South Korea

Dr. Saeed Mohsen Abosreea Hassan is an Assistant Professor of Electronics and Communications Engineering with a Ph.D. from Ain Shams University, Cairo. He has extensive academic experience, currently serving at King Salman International University. His research focuses on cutting-edge areas like deep learning, IoT, and wearable devices, with applications in healthcare and smart systems. Dr. Saeed has published 24 papers in reputable journals such as IEEE Access and Multimedia Tools and Applications, achieving an h-index of 10. His work spans various interdisciplinary fields, including Industry 4.0, human activity recognition, and energy harvesting systems. In addition to his research, he has supervised numerous student projects and contributed significantly to teaching advanced courses in electronics and AI. His contributions to both academia and industry make him a versatile researcher poised for continued impact in technological innovation and healthcare systems.

Profile

Education

Saeed Mohsen Abosreea Hassan holds a Ph.D. in Electronics and Communications Engineering from Ain Shams University, Cairo, Egypt, completed between 2017 and 2020. His doctoral research focused on the design and implementation of hybrid energy harvesting systems for medical wearable sensor nodes, demonstrating his expertise in cutting-edge healthcare technology. Prior to this, Saeed earned his Master’s degree in Electronics and Communications Engineering from the same institution, where he worked on the development of an electroencephalogram (EEG) system, further advancing his specialization in medical applications of electronics. He completed his undergraduate studies at Thebes Higher Institute of Engineering, Cairo, from 2008 to 2013, graduating with honors, earning an overall grade of “Excellent” and a GPA of 3.6/4.0. His strong educational background has provided him with a solid foundation in both theoretical and practical aspects of electronics, communications, and their applications in healthcare and industry.

Professional Experience

Saeed Mohsen Abosreea Hassan is an accomplished Assistant Professor in Electronics and Communications Engineering, currently serving at King Salman International University since September 2022. Prior to this role, he held a full-time Assistant Professor position at Al-Madinah Higher Institute for Engineering and Technology from April 2021 to August 2022. He also served as a part-time Assistant Professor at Ain Shams University from July 2021 to September 2021, where he contributed to cutting-edge research and advanced teaching methodologies. Before transitioning to academia, Saeed gained extensive experience as a Teaching Assistant at Thebes Academy from September 2013 to March 2021. Throughout his career, he has demonstrated expertise in various fields such as deep learning, IoT systems, and medical wearable sensor technologies. His diverse academic roles, combined with his active involvement in research, student supervision, and curriculum development, highlight his commitment to advancing education and innovation in engineering.

Research Interest

Saeed Mohsen Abosreea Hassan’s research interests focus on cutting-edge technologies in electronics, communications, and artificial intelligence. His work spans deep learning models, machine learning algorithms, and their applications in human activity recognition, smart healthcare systems, and Internet of Things (IoT) technologies. A significant portion of his research is dedicated to the development of energy harvesting systems for wearable medical sensor nodes, which has the potential to revolutionize real-time healthcare monitoring. He is also passionate about the use of neural networks and convolutional neural networks (CNNs) for the detection of brain tumors, Alzheimer’s disease, and other medical conditions through medical imaging techniques. His focus on Industry 4.0 and smart city networks highlights his commitment to advancing technologies that enhance both industrial automation and urban living. Saeed’s research integrates theoretical advancements with practical applications, particularly in healthcare and smart environments.

Research Skills

Saeed Mohsen Abosreea Hassan possesses a diverse and advanced set of research skills that span multiple fields of electronics, communications, and deep learning. He is proficient in AI tools such as TensorFlow, PyTorch, Keras, and Scikit-Learn, which he uses for developing machine learning and deep learning models. His expertise in embedded systems, IoT, and smart healthcare technologies is reflected in his research on wearable sensor nodes and energy harvesting systems. He is skilled in programming languages like Python, MATLAB, and Embedded C, essential for his work in developing algorithms and systems for medical and industrial applications. Additionally, Saeed is experienced in electronic circuit and layout design using tools like Proteus, LT-spice, and NI Multisim. His research extends into data acquisition systems, neural networks, and signal processing, particularly in healthcare applications such as brain tumor detection and human activity recognition, showcasing his multidisciplinary research proficiency.

Award and Recognition

Dr. Saeed Mohsen Abosreea Hassan, an accomplished Assistant Professor in Electronics and Communications Engineering, has made significant strides in the fields of deep learning, IoT, and wearable healthcare technologies. He holds a Ph.D. from Ain Shams University and has published 24 research papers, with an impressive h-index of 10 on Google Scholar. His work has been featured in prestigious journals, including IEEE Access, highlighting his contributions to Industry 4.0, smart healthcare systems, and energy harvesting technologies. Dr. Saeed’s research has been recognized for its practical applications in healthcare, with innovations like self-powered medical wearable sensors. His expertise has also earned him opportunities to present at international conferences and collaborate with top-tier researchers globally. As an emerging leader in his field, Dr. Saeed’s work continues to push the boundaries of technology and healthcare, positioning him as a distinguished researcher dedicated to advancing science and improving lives.

Conclusion

Saeed Mohsen Abosreea Hassan is a well-qualified candidate for the Best Researcher Award. His strong academic foundation, multidisciplinary research, and publication record make him a strong contender. By expanding his international collaborations, focusing on high-impact research, and pursuing more patents or grants, he could significantly increase his research impact and standing in the academic community. His work in healthcare and energy harvesting aligns with global trends, making his contributions both timely and impactful.

Publication Top Notes

  • Title: Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm
    • Authors: S Mohsen, A Elkaseer, SG Scholz
    • Year: 2021
    • Citations: 63
  • Title: Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition
    • Authors: Saeed Mohsen, Ahmed Elkaseer, Steffen G. Scholz
    • Year: 2021
    • Citations: 46
  • Title: A Self-Powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications
    • Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta
    • Year: 2021
    • Citations: 41
  • Title: Machine Learning and Deep Learning Techniques for Driver Fatigue and Drowsiness Detection: A Review
    • Authors: S Abd El-Nabi, W El-Shafai, ES M. El-Rabaie, K F. Ramadan, S Mohsen
    • Year: 2023
    • Citations: 25
  • Title: Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique with ResNext101_32x8d and VGG19 Pre-Trained Models
    • Authors: S Mohsen, AM Ali, ESM El-Rabaie, A Elkaseer, SG Scholz, AMA Hassan
    • Year: 2023
    • Citations: 22
  • Title: Recognition of Human Activity Using GRU Deep Learning Algorithm
    • Authors: S Mohsen
    • Year: 2023
    • Citations: 18
  • Title: An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System
    • Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta
    • Year: 2020
    • Citations: 15
  • Title: On Architecture of Self-Sustainable Wearable Sensor Node for IoT Healthcare Applications
    • Authors: S Mohsen, A Zekry, K Youssef, M Abouelatta
    • Year: 2021
    • Citations: 13
  • Title: EEG-Based Human Emotion Prediction Using an LSTM Model
    • Authors: S Mohsen, AG Alharbi
    • Year: 2021
    • Citations: 12
  • Title: A Self-Powered Wearable Sensor Node for IoT Healthcare Applications
    • Authors: S Mohsen, A Zekry, M Abouelatta, K Youssef
    • Year: 2020
    • Citations: 12