SANDEEP BHAD

@rungta.ac.in

Asst Professor Electronics and Telecommunication
RCET Bhilai, Rungta International Skill University Bhilai CG India

SANDEEP BHAD


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https://researchid.co/sandeepbhad

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Multidisciplinary, Biomedical Engineering, Information Systems
8

Scopus Publications

Scopus Publications

  • Hybrid Deep Learning Framework for Sleep Quality Prediction: Integrating Metaheuristic Optimization and Statistical Features
    Ayodele Lasisi, Nitasha Rathore, Lalita Gupta, Kanika Thakur, Shrikant Burje, Madhumathi Ramasamy, Sandeep Bhad, Anurag Sinha, Amar Jeet, Shakti Singh, Quadri Noorulhasan Naveed, Raman Kumar, Syed Abid Hussain
    Brain and Behavior, 2026
    Assessing sleep quality is essential to preserving optimum health and well‐being, with consequences ranging from preventing chronic diseases to improving cognitive function. This paper introduces a sophisticated hybrid deep learning architecture that far outperforms current techniques for actigraphy data‐based sleep quality prediction. Our method uses two metaheuristic optimization approaches (genetic algorithms and particle swarm optimization (PSO)) for feature selection and combines statistical characteristics with complex features retrieved using long short‐term memory (LSTM) networks. support vector machines (SVMs) are then used to classify the optimized feature set. Our model outperforms baseline LSTM and other cutting‐edge methods when tested on the benchmark MESA Actigraphy dataset. It achieves remarkable accuracy (84.64% for weekly sleep quality and 68.99% for sleep consistency), F1‐scores (0.847 and 0.69, respectively), and AUC values (0.909 and 0.839, respectively). Furthermore, we close a significant gap in black‐box deep learning techniques by introducing a unique feature significance analysis that gives the model's predictions interpretability. Our results emphasize the potential of hybrid deep learning frameworks for individualized sleep health management and early diagnosis of sleep disorders by demonstrating the efficacy of integrating metaheuristic optimization with multimodal data in sleep quality prediction.
  • Intelligent Biodegradable Materials Composite Packaging (IBCP): An Integrated IoT-ML-LLM Approach for Sustainable Design, Tracking, and Waste Management
    Anurag Sinha, Pronaya Bhattacharya, Aasif Aftab, Imran Mohd Khan, Mohammad Nadeem Ahmed, Sandeep Bhad, Amarjeet, Biresh Kumar
    Aip Conference Proceedings, 2026
  • A Hybrid DL with Battle Royal Optimisation Algorithm for Accurate Tree Counting Using Satellite Images
    Himanshu Bansal, Anurag Sinha, Garvit Agarwal, Shantanu Kumar Mishra, Shelly Gupta, Parul Chaudhary, Patil Rahul Ashokrao, Ajay Kushwaha, Mukesh Kumar Bagaria, Md.Sazid Reza, Anupam Agrawal, Sandeep Bhad, Saifullah Khalid, Ayodele Lasisi, Ali M. Aseere
    International Journal of Computational Intelligence Systems, 2025
    Tree enumeration is a fundamental task in environmental monitoring, sustainable forestry management, and urban planning, yet manual methods remain prohibitively time-consuming and labor-intensive. This study presents an innovative approach, named BRO (Briefly Optimized Recognition with Deep Learning (DL) for accurate and efficient tree enumeration utilizing high-resolution satellite imagery and advanced machine learning techniques, specifically leveraging DL and transfer learning for robust tree detection and counting in complex environments. Experimental results demonstrate the significant effectiveness of the BRO approach compared to baseline methods, achieving a high accuracy of 97.8%. Furthermore, BRO shows substantial improvements in counting precision, resulting in a 5% reduction in Root Mean Squared Error (RMSE) and a 7% decrease in Mean Absolute Error (MAE) over existing techniques. Beyond performance metrics, execution time benchmarks highlight BRO’s computational efficiency, processing large datasets significantly faster than conventional optimization methods, which is crucial for large-scale applications. This research provides a robust and efficient system critical for various real-world applications, including large-scale deforestation monitoring, afforestation project planning and evaluation, and detailed urban forest inventories, thereby facilitating informed decision-making for environmental conservation and resource management.
  • Bullying and Cyber Bullying Challenges: Toward Sustainable Education
    Toward Sustainable Environmental Education Trends Challenges and Opportunities, 2025
  • Performance Investigation of Mimo-OFDM System in Next Generation 5G Communication: A Review
    Computational Optimization Modeling and Simulation for Engineering Applications, 2024
  • Health Disease Prediction Using Machine Learning and Internet of Things
    Patil Rahul Ashokrao, Derle Deepak Radhakrishna, Mukesh Sharma, Sandeep Bhad
    4th International Conference on Intelligent Engineering and Management Iciem 2023, 2023
    The Analysis of Disease Forecast using Machine Learning may be a framework that makes an exact prediction based on the data or side effects that the user enters into the framework and the infection it will cause. If the understanding isn't very accurate and the client really needs to know the type of illness he or she has experienced, it could be a framework that offers the client the tips and tricks to maintain the client's wellbeing framework and it provides a way to discover the illness using this forecast. The health industry plays a crucial role in treating patients' infections, so there are typically a few types of offer assistance for the health industry. By just getting the client's indications and using the framework, the health business can also profit from it. They will be able to tell in a reasonable amount of time. With the help of machine learning and the Python programming language with Tkinter interface, this Malady Expectation Utilizing Machine Learning has been completed in full. It also uses the dataset that the medical facilities already have access to and uses that to predict the spread of infection. IoT, cloud, and fog computing, which are popular issues in the field of illness detection and prediction, are, on the other hand, attracting the attention of the academic community. The Internet of Things (IoT), cloud computing, and fog computing have all been used to develop a variety of healthcare solutions.
  • Performance analysis of MIMO- Space-Time trellis code system over fading channels
    Sandeep Bhad, A. S. Hiwale
    Communications in Computer and Information Science, 2011
  • Performance analysis of space-time trellis codes with receive antenna selection
    A.S. Hiwale, A. A. Ghatol, S. D. Bhad
    Proceedings of the 4th International Conference on Wireless Communication and Sensor Networks Wcsn 2008, 2008
    This paper discusses the performance analysis of multi-antennas systems with receive antenna selection over quasistatic fading channels. In performing the analysis with antenna selection, the selection criterion adopted is based on selecting best N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> antennas out of the available N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> receive antennas such that the instantaneous signal-to-noise ratio (SNR) at the receiver is maximized. It is assumed that the total number of transmit antennas, N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sub> = 2, are equal to or greater than the total number of receive antennas. From the analysis it is shown that the resulting diversity order with antenna selection is maintained as that of full complexity system, however the coding gain deteriorates in proportion with reduced number of receive antennas. The results obtained with antenna selection with N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> = 2 and N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> = 3 multi-antenna systems are compared.