Inzamam Mashood Nasir
@hitecuni.edu.pk
Lecturer, Computer Science Department
HITEC University Taxila
RESEARCH INTERESTS
My research interests include Machine Learning, Computer Vision and Medical and Agricultural Imaging, Blockchain and IoTs.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Inzamam Mashood Nasir, Mudassar Raza, Siti Maghfirotul Ulyah, Jamal Hussain Shah, Norma Latif Fitriyani, and Muhammad Syafrudin
Elsevier BV
Inzamam Mashood Nasir, Mudassar Raza, Jamal Hussain Shah, Muhammad Attique Khan, Yun-Cheol Nam, and Yunyoung Nam
Computers, Materials and Continua (Tech Science Press)
Human Action Recognition (HAR) in uncontrolled environments targets to recognition of different actions from a video. An effective HAR model can be employed for an application like human-computer interaction, health care, person tracking, and video surveillance. Machine Learning (ML) approaches, specifically, Convolutional Neural Network (CNN) models had been widely used and achieved impressive results through feature fusion. The accuracy and effectiveness of these models continue to be the biggest challenge in this field. In this article, a novel feature optimization algorithm, called improved Shark Smell Optimization (iSSO) is proposed to reduce the redundancy of extracted features. This proposed technique is inspired by the behavior of white sharks, and how they find the best prey in the whole search space. The proposed iSSO algorithm divides the Feature Vector (FV) into subparts, where a search is conducted to find optimal local features from each subpart of FV. Once local optimal features are selected, a global search is conducted to further optimize these features. The proposed iSSO algorithm is employed on nine (9) selected CNN models. These CNN models are selected based on their top-1 and top-5 accuracy in ImageNet competition. To evaluate the model, two publicly available datasets UCF-Sports and Hollywood2 are selected.
Inzamam Mashood Nasir, Mudassar Raza, Jamal Hussain Shah, Shui-Hua Wang, Usman Tariq, and Muhammad Attique Khan
Elsevier BV
Inzamam Mashood Nasir, Mudassar Raza, Jamal Hussain Shah, Muhammad Attique Khan, and Amjad Rehman
IEEE
Video based Human Action Recognition (HAR) is an active research field of Machine Learning (ML) and human detection in videos is the most important step in action recognition. Recently, several techniques and algorithms have been proposed to increase the accuracy of HAR process, but margin of improvement still exists. Detection and classification of human actions is a challenging task due to random changes in human appearance, clothes, illumination, and background. In this article, an efficient technique to classify human actions by utilizing steps like removing redundant frames from videos, extracting Segments of Interest (SoIs), feature descriptor mining through Geodesic Distance (GD), 3D Cartesian-plane Features (3D-CF), Joints MOCAP (JMOCAP) and n-way Point Trajectory Generation (nPTG). A Neuro Fuzzy Classifier (NFC) is used at the end for the classification purpose. The proposed technique is tested on two publicly available datasets including HMDB-51 and Hollywood2, and achieved an accuracy of 82.55% and 91.99% respectively. These efficient results prove the validity of proposed model.
Iram Mushtaq, Muhammad Umer, Muhammad Imran, Inzamam Mashood Nasir, Ghulam Muhammad, and Mohammad Shorfuzzaman
Computers, Materials and Continua (Tech Science Press)
During COVID-19, the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand. This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals, pharmacies, and retail stores as its customers. Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers. A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customer-selection criteria. These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts' domain-related knowledge using Analytical Hierarchy Process. Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty, commitment, brand awareness, brand image, sustainable behavior, and risk. Subsequently, Multi Criteria Decision Analysis has been performed to prioritize the customer-selection criteria and customers with respect to selection criteria. Three experts with seven and three and ten years of experience have participated in the study. Findings of the study suggest large hospitals, large pharmacies, and small retail stores are the highly preferred customers. Moreover, findings of prioritization of customer-selection criteria from both Principal Component Analysis and Analytical Hierarchy Process are consistent. Furthermore, this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision. Unlike traditional supply chain problems of supplier selection, this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria. © 2021 Tech Science Press. All rights reserved.
Muhammad Attique Khan, Inzamam Mashood Nasir, Muhammad Sharif, Majed Alhaisoni, Seifedine Kadry, Syed Ahmad Chan Bukhari, and Yunyoung Nam
Computers, Materials and Continua (Tech Science Press)
Wireless Capsule Endoscopy (WCE) is an imaging technology, widely used in medical imaging for stomach infection recognition. However, a one patient procedure takes almost seven to eight minutes and approximately 57,000 frames are captured. The privacy of patients is very important and manual inspection is time consuming and costly. Therefore, an automated system for recognition of stomach infections from WCE frames is always needed. An existing block chain-based approach is employed in a convolutional neural network model to secure the network for accurate recognition of stomach infections such as ulcer and bleeding. Initially, images are normalized in fixed dimension and passed in pre-trained deep models. These architectures are modified at each layer, to make them safer and more secure. Each layer contains an extra block, which stores certain information to avoid possible tempering, modification attacks and layer deletions. Information is stored in multiple blocks, i.e., block attached to each layer, a ledger block attached with the network, and a cloud ledger block stored in the cloud storage. After that, features are extracted and fused using aMode value-based approach and optimized using a Genetic Algorithm along with an entropy function. The Softmax classifier is applied at the end for final classification. Experiments are performed on a private collected dataset and achieve an accuracy of 96.8%. The statistical analysis and individual model comparison show the proposed method’s authenticity.
Inzamam Mashood Nasir, Muhammad Rashid, Jamal Hussain Shah, Muhammad Sharif, Muhammad Yahiya Haider Awan, and Monagi H. Alkinani
Bentham Science Publishers Ltd.
Background: Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.
Inzamam Mashood Nasir, Muhammad Attique Khan, Mussarat Yasmin, Jamal Hussain Shah, Marcin Gabryel, Rafał Scherer, and Robertas Damaševičius
MDPI AG
Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.
Inzamam Mashood Nasir, Muhammad Attique Khan, Ammar Armghan, and Muhammad Younus Javed
IEEE
Real-time applications like object detection, fire detection, face recognition and cancer detection are solely or partially relying on deep learning algorithms. Any tempering in these models can cause huge damages in many ways, therefore an utter need to secure these deep learning models is critically required. Blockchain technology has gained a wide popularity in tractability and security. In this article, the properties of blockchain are applied on the CNN models to produce secure CNN models. Each layer of a CNN model relates to a block, which contains the hash keys, public and private keys of their neighbors, while there exists a ledger block, which contains the detailed information about each layer of the model. The proposed SCNN model is tested using SVGG19 and SInceptionV3 models on publicly available datasets, which provides satisfactory results.
Inzamam Mashood Nasir, Asima Bibi, Jamal Hussain Shah, Muhammad Attique Khan, Muhammad Sharif, Khalid Iqbal, Yunyoung Nam, and Seifedine Kadry
Computers, Materials and Continua (Tech Science Press)
Inzamam Mashood Nasir, Muhammad Attique Khan, Majed Alhaisoni, Tanzila Saba, Amjad Rehman, and Tassawar Iqbal
Computers, Materials and Continua (Tech Science Press)
: Comic character detection is becoming an exciting and growing research area in the domain of machine learning. In this regard, recently, many methods are proposed to provide adequate performance. However, most of these methods utilized the custom datasets, containing a few hundred images and fewer classes, to evaluate the performances of their models without comparing it, with some standard datasets. This article takes advantage of utilizing a standard pub-licly dataset taken from a competition, and proposes a generic data balancing technique for imbalanced dataset to enhance and enable the in-depth training of the CNN. In addition, to classify the superheroes ef fi ciently, a custom 17-layer deep convolutional neural network is also proposed. The computed results achieved overall classi fi cation accuracy of 97.9% which is signi fi cantly superior to the accuracy of competition ’ s winner.