tausifa saleem | NIT Srinagar (original) (raw)
Papers by tausifa saleem
International Journal of Computing and Digital Systems
The efficiency of handwritten digit recognition models greatly relies on the classification techn... more The efficiency of handwritten digit recognition models greatly relies on the classification technique used and the optimization technique involved. Motivated to explore the efficacy of machine learning for handwritten digit recognition, this study assesses the performance of three machine learning techniques, logistic regression, multilayer perceptron, and convolutional neural network for recognition of handwritten digits. The experimental results reveal that convolutional neural network outperforms logistic regression and multilayer perceptron in terms of accuracy. This study also evaluates the performance of three optimizers, namely stochastic gradient descent, adadelta, and adam for handwritten digit recognition. The experiments conducted in the study demonstrate that adam performs better than stochastic gradient descent and adadelta. It is concluded that convolutional neural network with adam is the best choice for handwritten digit recognition in terms of accuracy. However, the convolutional neural network is quite expensive in terms of training time and execution time. To this purpose, this paper proposes a methodology for the design of a lightweight convolutional neural network model.
Procedia Computer Science
International Journal of Sensors, Wireless Communications and Control
Background: The rapid progress in domains like machine learning, and big data has created plenty ... more Background: The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. Objective and conclusion: The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk pr...
Scalable Computing: Practice and Experience
The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented ... more The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proficient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identified key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use...
International Journal of Computing and Digital Systems
The efficiency of handwritten digit recognition models greatly relies on the classification techn... more The efficiency of handwritten digit recognition models greatly relies on the classification technique used and the optimization technique involved. Motivated to explore the efficacy of machine learning for handwritten digit recognition, this study assesses the performance of three machine learning techniques, logistic regression, multilayer perceptron, and convolutional neural network for recognition of handwritten digits. The experimental results reveal that convolutional neural network outperforms logistic regression and multilayer perceptron in terms of accuracy. This study also evaluates the performance of three optimizers, namely stochastic gradient descent, adadelta, and adam for handwritten digit recognition. The experiments conducted in the study demonstrate that adam performs better than stochastic gradient descent and adadelta. It is concluded that convolutional neural network with adam is the best choice for handwritten digit recognition in terms of accuracy. However, the convolutional neural network is quite expensive in terms of training time and execution time. To this purpose, this paper proposes a methodology for the design of a lightweight convolutional neural network model.
Procedia Computer Science
International Journal of Sensors, Wireless Communications and Control
Background: The rapid progress in domains like machine learning, and big data has created plenty ... more Background: The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. Objective and conclusion: The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk pr...
Scalable Computing: Practice and Experience
The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented ... more The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proficient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identified key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use...