Mumtaz Ahmed

Jamia Millia Islamia

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Sarfaraz Masood

Jamia Millia Islamia

Danish Raza Rizvi

Jamia Millia Islamia

Himanshu Sundriyal

Jamia Millia Islamia

Kseniya Zablotskaya

Universität Ulm

Shahriyar Mahmud Mamun

Daffodil International University

Ravi Pratap Singh

National Institute of Technology Kurukshetra

Jameel Ahamed

Maulana Azad National Urdu University

Fatima Munawwar

Jamia Millia Islamia

Asif Uz Zaman Asif

Colorado State University

Wolfgang Minker

Universität Ulm

Cooperative Institutions

Jamia Millia Islamia

Universität Ulm

National Institute of Technology Kurukshetra

Technische Hochschule Ulm

Information Technology University

Daffodil International University

Aligarh Muslim University

Jawaharlal Nehru Medical College Hospital

Indian Institute of Technology Delhi

Central University of Punjab

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

DCNN-Based Vegetable Image Classification Using Transfer Learning: A Comparative Study

Mumtaz Ahmed Shahriyar Mahmud Mamun Asif Uz Zaman Asif

In this paper, an attempt is addressed towards accurate vegetable image classification. A dataset consisting of 21,000 images of 15 classes is used for this classification. Convolutional neural network, a deep learning algorithm is the most efficient tool in the machine learning field for classification problems. But CNN requires large datasets so that it performs well in natural image classification problems. Here, we conduct an experiment on the performance of CNN for vegetable image classification by developing a CNN model from the ground. Additionally, several pre-trained CNN architectures using transfer learning are employed to compare the accuracy with the typical CNN. This work proposes the study between such typical CNN and its architectures(VGG16, MobileNet, InceptionV3, ResNet etc.) to build up which technique would work best regarding accuracy and effectiveness with new image datasets. Experimental results are presented for all the proposed architectures of CNN. Besides, a comparative study is done between developed CNN models and pre-trained CNN architectures. And the study shows that by utilizing previous information gained from related large-scale work, the transfer learning technique can achieve better classification results over traditional CNN with a small dataset. And one more enrichment in this paper is that we build up a vegetable images dataset of 15 categories consisting of a total of 21,000 images.

Transfer of learning

Contextual image classification

10.1109/icccsp52374.2021.9465499

Cite

Citations (35)

Non-parametric Regression and Random Balance Method Modification for Determination of the Most Informative Features

Kseniya Zablotskaya Mumtaz Ahmed Sergey Zablotskiy Wolfgang Minker

In this paper we present a new method which allows us to detect the most informative features out of all data extracted from a certain data corpus. Widely used Pearson's coefficient is not reliable if the dependency between extracted features (input variables) and the objective function (output) is not linear. This approach is based on a modified random balance method (RBM) combined with non-parametric kernel regression for modeling the dependency between output and input variables. The standard random balance method stochastically determines the most important features of a process, but it requires the values of the objective function at the certain assigned points. If there is no possibility to calculate these values, it is necessary to approximate them. Since we assume that the dependency between stochastic variables can be non-linear, it is necessary to take an appropriate model. We used non-parametric kernel regression because knowledge about the parametric structure of the dependency is not needed. Moreover, we modified the random balance method to handle the non-linearity of the data.

Kernel (algebra)

Kernel regression

10.1109/ie.2010.19

Cite

Citations (1)

Scene Recognition from Image Using Convolutional Neural Network

Procedia Computer Science (2020)

Sarfaraz Masood Umer Ahsan Fatima Munawwar Danish Raza Rizvi Mumtaz Ahmed

This work is an attempt to solve one of the most common problems of scene recognition. The dataset called PLACES2 was used for the purpose of experimentation which contains nearly 7 million labelled pictures of various scenes which is a sufficiently exhaustive dataset to train and test a model to get the desired output. The Convolutional Neural Network was used to design a neural architecture for our machine to store and classify the images as desired for the recognition of scene in the dataset. Results strongly suggest that the trained model were able to perform successful scene recognition in the given data sample

Sample (material)

10.1016/j.procs.2020.03.400

Cite

Citations (13)

Computer Vision to Enhance Healthcare Domain: An Overview of Features, Implementation, and Opportunities

Intelligent Pharmacy (2024)

Mohd Javaid Abid Haleem Ravi Pratap Singh Mumtaz Ahmed

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one of the growing modern AI technologies. The distribution and administration of medications are about to change by using CV for medication management. This system scans pharmaceutical labels and keeps track of the process from delivery to administration using cameras, sensors, and computer algorithms. In order to assure accuracy in medicine delivery and dose, the system also makes it easier for doctors, nurses, and chemists to communicate. The computer vision-driven medication management system can significantly lower the number of medical mistakes that result from inaccurate or missing prescriptions, improper doses, or simply forgetting to take a particular drug. An exhaustive literature review has been done to identify work related to the research objectives. This paper is about CV and their need in healthcare. Various tasks associated with CV in the healthcare domain are discussed. Targeted healthcare goals through CV traits are briefed. Finally, the significant applications of CVs in healthcare were identified and discussed. Nowadays, CV has practical uses in healthcare. Its methods are widely used since they have shown excellent utility in several medical contexts, including medical imaging and surgical planning. The CV is used to study how to program computers to comprehend digital pictures. Numerous medical applications utilise this technology, such as automated abnormality identification, illness diagnosis, and surgical procedure guiding. CV is expanding quickly and has enormous promise to enhance healthcare. Some of the many CV applications in the healthcare sector include patient identification systems, medical picture analysis, surgical simulation and illness diagnosis.

10.1016/j.ipha.2024.05.007

Cite

Citations (3)

A Hybrid Approach for Fake News Detection using Convolution and Multilayer Perceptron

International Journal of Computer Sciences and Engineering (2019)

Mohd Zeeshan Ansari Mumtaz Ahmed

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Publication

Bridge (graph theory)

10.26438/ijcse/v7i4.11811187

Cite

Citations (1)

Identification of diabetic retinopathy in eye images using transfer learning

Sarfaraz Masood Tarun Luthra Himanshu Sundriyal Mumtaz Ahmed

Convolutional Neural Networks have been performing exceptionally well for the recognition of worldly objects and there are various trained models available which can give you the class of an object if an image of the object is fed as their input. But, we can also retrain an already trained model for anotherset of classes of our interest and we have retrained such a model to check the severity of Diabetic Retinopathy in eye images (data has been provided by eyePacs as color fundus images) on the scale of 0-5. A convolutional neural network classifier engineered from Inception V3 network (trained for ImageNet) for 5-class severity classification performed best with an accuracy of 48.2%.

Transfer of learning

Contextual image classification

Fundus (uterus)

10.1109/ccaa.2017.8229977

Cite

Citations (65)

Parkinson’s Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection

Lecture notes in networks and systems (2024)

Aqib Nazir Mir Iqra Nissar Mumtaz Ahmed Sarfaraz Masood Danish Raza Rizvi

10.1007/978-981-97-3523-5_16

Cite

Citations (0)

An Inception V3 Approach for Malware Classification Using Machine Learning and Transfer Learning

SSRN Electronic Journal (2022)

Jameel Ahamed Mumtaz Ahmed Neda Afreen Muneeb Ahmed Mustafa Sameer

Malware instances have been extremely used for illegitimate purposes, and new variants of malware are observed every day. Machine learning in network security is one of the prime areas of research today because of its performance and has shown tremendous growth in the last decade. In this paper, we formulate the malware signature as a 2D image representation and leverage deep learning approaches to characterize the signature of malware contained in BIG15 dataset across nine classes. The current research compares the performance of various machine learning and deep learning technologies towards malware classification such as Logistic Regression (LR), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), transfer learning on CNN and Long Short Term Memory (LSTM). The transfer learning approach using InceptionV3 resulted in a good performance with respect to the compared models like LSTM with a classification accuracy of 98.76% on the test dataset and 99.6% on the train dataset.

Transfer of learning

10.2139/ssrn.4233090

Cite

Citations (2)