Received Bachelor's degree in Information Technology from Benazir Bhutto Shaheed University Lyari, Karachi. I am a Certified Network Security Specialist from International Cyber Security Institute, UK. My academic and research endeavors have encompassed various projects focused on Internet of Things (IoT), Machine Learning, Medical Imaging, and Artificial Intelligence.My research interests in Medical Imaging, Machine Learning, Digital Transformation and Cyber Security. Aim to explore innovation solutions and advancements. Address: Maharaj Mohalla, Near National Bank of Pakistan, Islamkot, Tharparkar, Sindh,Pakistan
The imputation of missing data in healthcare records is a critical task for ensuring the integrit... more The imputation of missing data in healthcare records is a critical task for ensuring the integrity and utility of medical datasets. Traditional methods often rely on simplistic approaches, leading to potential biases and inaccuracies in downstream analyses. In this paper, we propose a novel machine learning approach for imputing missing healthcare data, aiming to enhance the accuracy and robustness of imputation while preserving the underlying patterns and relationships in the data. Our approach leverages advanced machine learning techniques, including ensemble and deep learning architectures, to effectively capture complex dependencies and correlations within healthcare datasets. We demonstrate the efficacy of our method through comprehensive experiments on realworld healthcare datasets, showcasing superior imputation performance compared to conventional techniques. Furthermore, we discuss the interpretability and scalability aspects of our approach, highlighting its potential for practical deployment in healthcare analytics and decision support systems. Our proposed machine learning approach offers a promising solution for addressing missing data challenges in healthcare, paving the way for more accurate and reliable data-driven insights in medical research and practice.
The imputation of missing data in healthcare records is a critical task for ensuring the integrit... more The imputation of missing data in healthcare records is a critical task for ensuring the integrity and utility of medical datasets. Traditional methods often rely on simplistic approaches, leading to potential biases and inaccuracies in downstream analyses. In this paper, we propose a novel machine learning approach for imputing missing healthcare data, aiming to enhance the accuracy and robustness of imputation while preserving the underlying patterns and relationships in the data. Our approach leverages advanced machine learning techniques, including ensemble and deep learning architectures, to effectively capture complex dependencies and correlations within healthcare datasets. We demonstrate the efficacy of our method through comprehensive experiments on real-world healthcare datasets, showcasing superior imputation performance compared to conventional techniques. Furthermore, we discuss the interpretability and scalability aspects of our approach, highlighting its potential for practical deployment in healthcare analytics and decision support systems. Our proposed machine learning approach offers a promising solution for addressing missing data challenges in healthcare, paving the way for more accurate and reliable data-driven insights in medical research and practice.
Industrial water pollution is a significant environmental concern, threatening human health and e... more Industrial water pollution is a significant environmental concern, threatening human health and ecosystems. This paper proposes Water4.0, a machine learning-based system that forecasts industrial water pollution levels using historical data and real-time monitoring. Water4.0 uses the K-Nearest Neighbors (KNN) algorithm, which identifies patterns and relationships based on proximity. The system considers nine water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity. Experimental results show Water4.0's impressive accuracy, outperforming traditional methods. A case study of the textile industry in Pakistan demonstrates its success in accurately predicting water pollution levels and enabling proactive measures to prevent pollution. Water4.0's significance extends beyond technical prowess, offering a proactive approach to addressing industrial water pollution. By predicting pollution levels, industries can take preventative measures, reduce their environmental impact, and ensure compliance with regulations. This helps protect aquatic ecosystems, preserve public health, and promote sustainable development. Water4.0 is a groundbreaking machine learning-based system that offers a proactive solution to industrial water pollution. By combining historical data and real-time monitoring, it can significantly impact various industries and locations.
Industrial water pollution is a significant environmental concern, threatening human health and e... more Industrial water pollution is a significant environmental concern, threatening human health and ecosystems. This paper proposes Water4.0, a machine learning-based system that forecasts industrial water pollution levels using historical data and real-time monitoring. Water4.0 uses the K-Nearest Neighbors (KNN) algorithm, which identifies patterns and relationships based on proximity. The system considers nine water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity. Experimental results show Water4.0's impressive accuracy, outperforming traditional methods. A case study of the textile industry in Pakistan demonstrates its success in accurately predicting water pollution levels and enabling proactive measures to prevent pollution. Water4.0's significance extends beyond technical prowess, offering a proactive approach to addressing industrial water pollution. By predicting pollution levels, industries can take preventative measures, reduce their environmental impact, and ensure compliance with regulations. This helps protect aquatic ecosystems, preserve public health, and promote sustainable development. Water4.0 is a groundbreaking machine learning-based system that offers a proactive solution to industrial water pollution. By combining historical data and real-time monitoring, it can significantly impact various industries and locations.
The imputation of missing data in healthcare records is a critical task for ensuring the integrit... more The imputation of missing data in healthcare records is a critical task for ensuring the integrity and utility of medical datasets. Traditional methods often rely on simplistic approaches, leading to potential biases and inaccuracies in downstream analyses. In this paper, we propose a novel machine learning approach for imputing missing healthcare data, aiming to enhance the accuracy and robustness of imputation while preserving the underlying patterns and relationships in the data. Our approach leverages advanced machine learning techniques, including ensemble and deep learning architectures, to effectively capture complex dependencies and correlations within healthcare datasets. We demonstrate the efficacy of our method through comprehensive experiments on realworld healthcare datasets, showcasing superior imputation performance compared to conventional techniques. Furthermore, we discuss the interpretability and scalability aspects of our approach, highlighting its potential for practical deployment in healthcare analytics and decision support systems. Our proposed machine learning approach offers a promising solution for addressing missing data challenges in healthcare, paving the way for more accurate and reliable data-driven insights in medical research and practice.
The imputation of missing data in healthcare records is a critical task for ensuring the integrit... more The imputation of missing data in healthcare records is a critical task for ensuring the integrity and utility of medical datasets. Traditional methods often rely on simplistic approaches, leading to potential biases and inaccuracies in downstream analyses. In this paper, we propose a novel machine learning approach for imputing missing healthcare data, aiming to enhance the accuracy and robustness of imputation while preserving the underlying patterns and relationships in the data. Our approach leverages advanced machine learning techniques, including ensemble and deep learning architectures, to effectively capture complex dependencies and correlations within healthcare datasets. We demonstrate the efficacy of our method through comprehensive experiments on real-world healthcare datasets, showcasing superior imputation performance compared to conventional techniques. Furthermore, we discuss the interpretability and scalability aspects of our approach, highlighting its potential for practical deployment in healthcare analytics and decision support systems. Our proposed machine learning approach offers a promising solution for addressing missing data challenges in healthcare, paving the way for more accurate and reliable data-driven insights in medical research and practice.
Industrial water pollution is a significant environmental concern, threatening human health and e... more Industrial water pollution is a significant environmental concern, threatening human health and ecosystems. This paper proposes Water4.0, a machine learning-based system that forecasts industrial water pollution levels using historical data and real-time monitoring. Water4.0 uses the K-Nearest Neighbors (KNN) algorithm, which identifies patterns and relationships based on proximity. The system considers nine water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity. Experimental results show Water4.0's impressive accuracy, outperforming traditional methods. A case study of the textile industry in Pakistan demonstrates its success in accurately predicting water pollution levels and enabling proactive measures to prevent pollution. Water4.0's significance extends beyond technical prowess, offering a proactive approach to addressing industrial water pollution. By predicting pollution levels, industries can take preventative measures, reduce their environmental impact, and ensure compliance with regulations. This helps protect aquatic ecosystems, preserve public health, and promote sustainable development. Water4.0 is a groundbreaking machine learning-based system that offers a proactive solution to industrial water pollution. By combining historical data and real-time monitoring, it can significantly impact various industries and locations.
Industrial water pollution is a significant environmental concern, threatening human health and e... more Industrial water pollution is a significant environmental concern, threatening human health and ecosystems. This paper proposes Water4.0, a machine learning-based system that forecasts industrial water pollution levels using historical data and real-time monitoring. Water4.0 uses the K-Nearest Neighbors (KNN) algorithm, which identifies patterns and relationships based on proximity. The system considers nine water quality parameters, including pH, Hardness, Solids, Chloramines, Sulfate, Conductivity, Organic Carbon, Trihalomethanes, and Turbidity. Experimental results show Water4.0's impressive accuracy, outperforming traditional methods. A case study of the textile industry in Pakistan demonstrates its success in accurately predicting water pollution levels and enabling proactive measures to prevent pollution. Water4.0's significance extends beyond technical prowess, offering a proactive approach to addressing industrial water pollution. By predicting pollution levels, industries can take preventative measures, reduce their environmental impact, and ensure compliance with regulations. This helps protect aquatic ecosystems, preserve public health, and promote sustainable development. Water4.0 is a groundbreaking machine learning-based system that offers a proactive solution to industrial water pollution. By combining historical data and real-time monitoring, it can significantly impact various industries and locations.
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