Papers by prachiti pimple
Journal of Information Systems Engineering and Management, 2025
Brain tumor detection is the identification and categorization of aberrant brain tissues using me... more Brain tumor detection is the identification and categorization of aberrant brain tissues using methods
like MRI for tumor diagnosis and tracking. This sophisticated technique uses deep learning to analyze images,
resulting in precise early detection and treatment. This study uses hybrid architectures for different deep-
learning applications to offer a comprehensive hybridization strategy with promising prospects for improving
the diagnostic precision of images obtained for medical diagnostics. In this study, it employs three separate
datasets, previously known as Brain MRI images, Br35H and BraTS, to assess several architectures, including
ResNet, VGG, Inception, EfficientNet, DenseNet121, MobileNetV2, Xception, NASNetMobile, and
InceptionResNetV2. For the Brain MRI dataset, the findings demonstrated that the VGG16 model had a training
accuracy of 99.93% with the lowest train loss of 0.0238; on all three datasets, the InceptionV3
showed exceptional robustness, with an accuracy of 99.78%. Although hybrid models that combined
architectures such as Xception, NASNetMobile, and InceptionResNetV2 performed effectively, they also
appeared to overfit, with validation and test losses being comparatively larger than training accuracy. The hybrid
model hybrid model (EfficientNet, DenseNet121, MobileNetV2) achieved 99.87% training accuracy using the
BraTS dataset. These findings indicate the possibility of applying deep learning architectures more effectively to
better diagnose brain tumors, in addition to rigorous model optimization and selection to reduce the tendency for
overfitting. This study encourages hybrid DL models' application in the medical area.
HELIX
Many measures have been proposed to represent the status of traffic conditions on arterial roadwa... more Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. Traffic congestion is rising nowadays and to understand its nature, a systematic mechanism is required. A new approach is presented in this research work to measure the congestion index first and then the congestion level. In this research work, a twophase fuzzy controller is applied wherein in first phase the traffic congestion index is measured by using travel speed rate and very-low speed rate followed by congestion level measurement by using density state and congestion index in next phase. The application of the proposed approach is demonstrated using realworld data of small area segment of Nagpur city, India. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition.
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Papers by prachiti pimple
like MRI for tumor diagnosis and tracking. This sophisticated technique uses deep learning to analyze images,
resulting in precise early detection and treatment. This study uses hybrid architectures for different deep-
learning applications to offer a comprehensive hybridization strategy with promising prospects for improving
the diagnostic precision of images obtained for medical diagnostics. In this study, it employs three separate
datasets, previously known as Brain MRI images, Br35H and BraTS, to assess several architectures, including
ResNet, VGG, Inception, EfficientNet, DenseNet121, MobileNetV2, Xception, NASNetMobile, and
InceptionResNetV2. For the Brain MRI dataset, the findings demonstrated that the VGG16 model had a training
accuracy of 99.93% with the lowest train loss of 0.0238; on all three datasets, the InceptionV3
showed exceptional robustness, with an accuracy of 99.78%. Although hybrid models that combined
architectures such as Xception, NASNetMobile, and InceptionResNetV2 performed effectively, they also
appeared to overfit, with validation and test losses being comparatively larger than training accuracy. The hybrid
model hybrid model (EfficientNet, DenseNet121, MobileNetV2) achieved 99.87% training accuracy using the
BraTS dataset. These findings indicate the possibility of applying deep learning architectures more effectively to
better diagnose brain tumors, in addition to rigorous model optimization and selection to reduce the tendency for
overfitting. This study encourages hybrid DL models' application in the medical area.
like MRI for tumor diagnosis and tracking. This sophisticated technique uses deep learning to analyze images,
resulting in precise early detection and treatment. This study uses hybrid architectures for different deep-
learning applications to offer a comprehensive hybridization strategy with promising prospects for improving
the diagnostic precision of images obtained for medical diagnostics. In this study, it employs three separate
datasets, previously known as Brain MRI images, Br35H and BraTS, to assess several architectures, including
ResNet, VGG, Inception, EfficientNet, DenseNet121, MobileNetV2, Xception, NASNetMobile, and
InceptionResNetV2. For the Brain MRI dataset, the findings demonstrated that the VGG16 model had a training
accuracy of 99.93% with the lowest train loss of 0.0238; on all three datasets, the InceptionV3
showed exceptional robustness, with an accuracy of 99.78%. Although hybrid models that combined
architectures such as Xception, NASNetMobile, and InceptionResNetV2 performed effectively, they also
appeared to overfit, with validation and test losses being comparatively larger than training accuracy. The hybrid
model hybrid model (EfficientNet, DenseNet121, MobileNetV2) achieved 99.87% training accuracy using the
BraTS dataset. These findings indicate the possibility of applying deep learning architectures more effectively to
better diagnose brain tumors, in addition to rigorous model optimization and selection to reduce the tendency for
overfitting. This study encourages hybrid DL models' application in the medical area.