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    AI in Maintenance Forecasting

    Watch: AI-Based Predictive Maintenance in 4 Steps by Ronald van Loon AI in maintenance forecasting refers to the application of artificial intelligence technologies to analyze historical and real-time data, enabling the prediction of equipment maintenance needs such as labor, costs, and resource…
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    Image of Author Dr. Dipen
    Dr. Dipen
    I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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      AdapterFusion vs LoRA‑QLoRA for AI Applications

      Watch: LoRA & QLoRA Fine-tuning Explained In-Depth by Mark Hennings AdapterFusion and LoRA-QLoRA represent two prominent parameter-efficient fine-tuning (PEFT) methodologies for optimizing large language models (LLMs) in AI applications. These approaches address the computational and memory…
      Thumbnail Image of Tutorial AdapterFusion vs LoRA‑QLoRA for AI Applications
      Image of Author Dr. Dipen
      Dr. Dipen
      I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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        AI Applications with LoRA‑QLoRA Hybrid

        The LoRA-QLoRA hybrid represents a convergence of parameter-efficient fine-tuning techniques designed to optimize large language model (LLM) training and deployment. LoRA (Low-Rank Adaptation) introduces low-rank matrices to capture new knowledge without modifying the original model weights, while…
        Thumbnail Image of Tutorial AI Applications with LoRA‑QLoRA Hybrid
        Image of Author Dr. Dipen
        Dr. Dipen
        I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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          How To Implement AI with MCP Server

          The Model Context Protocol (MCP) servers act as intermediaries that enable AI systems to interact with structured data sources, providing contextual information to improve decision-making and task execution . These servers are critical for applications requiring real-time data integration, such as…
          Thumbnail Image of Tutorial How To Implement AI with MCP Server
          Image of Author Dr. Dipen
          Dr. Dipen
          I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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            How to Implement LoRA-QLoRA in AI for Drug Discovery

            LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) are parameter-efficient fine-tuning techniques that enable resource-constrained adaptation of large foundation models without retraining the entire architecture. These methods introduce low-rank matrices to existing model weights,…
            Thumbnail Image of Tutorial How to Implement LoRA-QLoRA in AI for Drug Discovery
            Image of Author Dr. Dipen
            Dr. Dipen
            I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
            •Last Updated:
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