Dr. Humera Shaziya has been working as Assistant Professor in the department of Informatics, Nizam College, an autonomous and constituent college of Osmania University since July 2004. She has served as the head of the department from July 2018 to July 2020 and as chairperson board of studies for BCA program from 2019 to 2020. She has been teaching for over 19 years to PG programme. She has been handling courses on algorithms, artificial intelligence, machine learning, deep learning, data science and several other core courses of computer sciences. She has been guiding students for carrying out major projects. Furthermore, she has been mentoring students on their academic and personal aspects. She has delivered 10 extension lectures and seminars in various colleges. Additionally, she has obtained 8 certificates from Coursera platform. She was the member of several committees including IQAC, NAAC criterion committee, Women Empowerment Cell (WEC), and syllabus revision committee.
EDUCATION
She has received M.Sc(IS), M.Tech(CSE) and BCA each with distinction from Osmania University and has qualified UGC-NET and AP-SET eligibility tests for Lecturership in the year 2012. She has been awarded the PhD(CSE) on the topic “Automatic Detection and Classification of Lung Cancer in Pulmonary CT Images using Deep Learning” during March 2023 under Visvesvaraya PhD scheme for Electronics and IT, Ministry of Electronics and IT, Government of India from department of CSE, University College of Engineering, Osmania University.
A Comprehensive Introduction to Cyber Threat Detection Through Quantum Computing and Comparative Study of Classical and Quantum- Enhanced Convolutional Neural Networks Humera Shaziya, Saif Ali Alsaidi Advancing Cyber Threat Detection Through Quantum and Edge Computing, 2025 This chapter explores the potential of integrating quantum computing and edge computing technologies to enhance cyber threat detection and response capabilities. It also discusses theoretical foundations, current research, practical implementations, and future prospects of combining quantum and edge computing for cybersecurity. Further, this work investigates quantum computing concepts infused in traditional convolutional neural networks (CNNs) for image classification. We present the discussion of traditional versus quantum convolution practices when applied to the MNIST database. Our findings show that the quantum-enhanced model has a highest validation accuracy of 82.67%, which is higher than the 74.33% of the classical model. In addition, the quantum model displays greater confidence in accurate predictions (90.09%) than the 76.87% confidence of the classical model. These results indicate the promise of quantum-enhanced convolutional networks for enhancing image classification.
Lung Cancer Classification Using CNN: Addressing Class Imbalance and Model Performance Analysis L K Suresh Kumar, Humera Shaziya, Raniah Zaheer Sustainability in Industry 5 0 Theory and Applications, 2024 This chapter presents Lung Cancer Classification (LCC) model, a Convolutional Neural Networks (CNN) approach developed to process the lung cancer dataset comprising Computed Tomography (CT) images from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD). The dataset consists of 1097 CT images categorized into 120 benign, 561 malignant and 416 normal cases. To tackle the issue of class imbalance, Synthetic Minority Oversampling Technique (SMOTE), Weighted Class (WC) approach and Data Augmentation (DA) techniques are applied. The LCC model achieves a validation accuracy of 98.78% which is further improved to 99.39% with SMOTE and WC methods, outperforming DA. Pretrained models are also compared with the developed model, and manual and automatic hyperparameter tuning is explored. Additionally, various train-test dataset split ratios are extensively experimented. This chapter provides valuable insights into LCC, addressing class imbalance, and analyzing model performance.
LungNodNet-The CNN architecture for Detection and Classification of Lung Nodules in Pulmonary CT Images Humera Shaziya, Shyamala Kattula Indicon 2022 2022 IEEE 19th India Council International Conference, 2022 Lung cancer detection at an early stage would be life saving. Usually it is diagnosed at a later stage which leads to increase in the mortalities. Detection of malignant lung nodules from CT images is a challenging task, given several factors that impact the detection and classification. In this work, we are proposing a convolutional neural network (CNN) based deep learning model that improves the accuracy of the nodules classification into benign and malignant types. Lung imaging database consortium-image database resource initiative (LIDC-IDRI), a publicly available lung CT scans dataset have been chosen for experiments. The proposed method come up with an approach to patchify the image to include the nodules segments of the image thus reducing the size of CT image drastically by extracting the nodule patches. Computational overhead is decreased due to the presented strategy. 6691 images containing both nodules and non-nodules are subsequently loaded into a 4-layered 2D CNN. Apparently two convolutional and two dense layers form the four layered CNN. Twenty filters having size of 5x5 is employed with relu activation function for first convolutional layer and 40 filters with size 3x3 has been specified for the second one. The model has been trained and validated on 70% and 10% respectively and tested on 20% of dataset. The verification performed on evaluation data resulted in 93.58% accuracy, 95.61% sensitivity and 90.14% specificity.
Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning Humera Shaziya, K. Shyamala 2020 IEEE Pune Section International Conference Punecon 2020, 2020 Image Segmentation performs segregation of distinct segments of an image. Lung segmentation separate different elements of thoracic region. It is an essential prerequisite to several analysis tasks performed on the Computed Tomography (CT) images of lungs. Computational complexity is greatly reduced only when the required area is segregated from the entire CT image. Automated segmentation facilitates quick processing since it requires relatively less time to process more images. Conventional computer based segmentation methods require extensive support for determining the features. Users develop the features and provide to the system which then utilize those features to delineate the required regions. Recent advancements in deep learning showed optimal results in solving numerous image recognition and segmentation problems. The significant characteristic of deep learning is that the model itself learns the features from the input images and then apply the learned features to process new images. The most successful model of deep learning is Convolutional Neural Network (CNN) has outperformed earlier techniques for image recognition, object and face detection and is considered to be the most successful architecture of deep learning. CNN has also been applied for segmentation tasks. In this proposed work, CNN and UNet models have been implemented to evaluate the processing of medical images. The focus of the work is on CT images of lungs. Results obtained on the lungs dataset of 267 images on CNN is 81.34% and UNet is 82.61%. Thus U-Net has improved the dice coefficient by 1.27%. The experiments show that UNet model outperforms CNN model to segment the lung fields in CT images.
A Study of the Optimization Algorithms in Deep Learning Raniah Zaheer, Humera Shaziya Proceedings of the 3rd International Conference on Inventive Systems and Control Icisc 2019, 2019 Training the deep learning models involves learning of the parameters to meet the objective function. Typically the objective is to minimize the loss incurred during the learning process. In a supervised mode of learning, a model is given the data samples and their respective outcomes. When a model generates an output, it compares it with the desired output and then takes the difference of generated and desired outputs and then attempts to bring the generated output close to the desired output. This is achieved through optimization algorithms. An optimization algorithm goes through several cycles until convergence to improve the accuracy of the model. There are several types of optimization methods developed to address the challenges associated with the learning process. Six of these have been taken up to be examined in this study to gain insights about their intricacies. The methods investigated are stochastic gradient descent, nesterov momentum, rmsprop, adam, adagrad, adadelta. Four datasets have been selected to perform the experiments which are mnist, fashionmnist, cifar10 and cifar100. The optimal training results obtained for mnist is 1.00 with RMSProp and adam at epoch 200, fashionmnist is 1.00 with rmsprop and adam at epoch 400, cifar10 is 1.00 with rmsprop at epoch 200, cifar100 is 1.00 with adam at epoch 100. The highest testing results are achieved with adam for mnist, fashionmnist, cifar10 and cifar100 are 0.9826, 0.9853, 0.9855, 0.9842 respectively. The analysis of results shows that adam optimization algorithm performs better than others at testing phase and rmsprop and adam at training phase.
Comprehensive Review of Automatic Lung Segmentation Techniques on Pulmonary CT Images Humera Shaziya, K. Shyamala, Raniah Zaheer Proceedings of the 3rd International Conference on Inventive Systems and Control Icisc 2019, 2019 Segmentation is the process of partitioning an image into distinctive subsets that share similar characteristics. Segmentation is an important prerequisite to semantic image analysis. Segmentation in general is useful in many different applications such as object and face detection and recognition. Particularly in medical image analysis segmentation plays a vital role in efficient processing of images. Segmentation is used to determine the volume of mass, planning of radiotherapy, and detection of artifacts in various organs. In lung cancer diagnosis, segmentation of lungs is the crucial step. Segmenting lungs from nearby structures significantly reduce the execution time of nodule detection and helps improve its efficiency. Lung segmentation is challenging and difficult task considering the heterogeneous nature of lung fields, closeness in gray level of different soft tissues, anatomical variability, and differences in scanners and scanning protocols and dose of radiation. Various automatic and semi-automatic approaches are presented for lung or nodule segmentation. The proposed study is a review of numerous techniques for lung segmentation. The present work investigated lung segmentation methods starting with conventional methods to machine learning techniques and finally the most remarkable methods of deep learning.
Design and Implementation of ConvNet for Handwritten Digits Classification on Graphical Processing Unit Humera Shaziya, K. Shyamala, Raniah Zaheer Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing Iccsp 2018, 2018 Convolutional Neural Network (CNN) or ConvNet is the leading-edge deep learning model that has achieved phenomenal successes in the tasks of image classification, object recognition, speech recognition and natural language processing. ConvNets are inherently complex architectures and training ConvNets requires significant amount of computation. There is a need to determine whether GPU or CPU provide the effective method for implementation of ConvNets. Very few studies have come up with the comparison of the implementation of ConvNets on both CPU and GPU. The present work examines the impact of GPU on the implementation of ConvNets. ConvNet is trained on MNIST dataset to perform classification of handwritten digits. The experiments have been performed on both CPU and GPU and observed that there is a performance improvement of 5 times in terms of training time speedup on GPU. The proposed work also investigates the effect of regularization and the results show that regularization indeed reduces the problem of overfitting.
Automatic Lung Segmentation on Thoracic CT Scans Using U-Net Convolutional Network Humera Shaziya, K. Shyamala, Raniah Zaheer Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing Iccsp 2018, 2018 Lung Cancer is the most perilous cancer. Early detection of the disease can improve survival rate. Automation of detection of lung nodules aid radiologists in quickly and accurately diagnosing the disease. Developing computer aided diagnosis (CADx) systems for lung cancer is a challenging task. Several components make up CADx and one of the most significant components is lung segmentation. Segmentation of lungs is an essential prerequisite to efficiently detect and classify lung nodules. Lung segmentation is the process of segregating lungs region from other tissues in the CT image. Conventional methods for lung segmentation either do not accurately segments normal and abnormal lungs or rely heavily on user generated features for the lungs. Deep learning has outperformed other methods in image processing and computer vision tasks. An architecture called U-Net convolutional network has been proposed and implemented exclusively for the segmentation of biomedical images. In this study U-Net ConvNet has been implemented on lungs dataset to perform lungs segmentation. The lungs dataset consists of 267 CT images of lungs and their corresponding segmentation maps. The accuracy and loss achieved is 0.9678 and 0.0871 respectively. Hence U-Net ConvNet can be used for the segmentation of lungs in CT scans.
The Future of Intelligent, Connected Healthcare through Industry 6.0 HS al The Transformation of the Metaverse: Blockchain and Healthcare in Industry 6.0 , 2026 2026
Connecting Supply Chains in Real-Time though the Internet of Things HS al Next-Gen Supply Chains: Technologies, Trends, and the Future of Global … , 2026 2026
A Comprehensive Framework for DDoS Intrusion Detection Using CICIDS 2017 Through Preprocessing, Modeling and Explainability with XGBoost and CICIDS 2017 H Shaziya Converging Intelligence Multidisciplinary Advances in AI-Driven Cybersecurity , 2026 2026
Enhancing CNN Performance on MNIST through Metaheuristic Hyperparameter Optimization HS al International Journal of Recent Development in Engineering and Technology 15 … , 2026 2026
Enhancing CNN Performance on MNIST through Metaheuristic Hyperparameter Optimization DHS Raniah Zaheer International Journal of Recent Development in Engineering and Technology 15 (1) , 2026 2026
A Comprehensive Introduction to Cyber Threat Detection Through Quantum Computing and Comparative Study of Classical and Quantum-Enhanced Convolutional Neural Networks H Shaziya, SA Alsaidi Advancing Cyber Threat Detection Through Quantum and Edge Computing, 1-30 , 2026 2026
Cyberbullying Detection in Arabic Text Using Different Deep Learning Approaches HS al Wasit Journal of Computer and Mathematics Sciences 4 (2), 45-55 , 2025 2025
Lung Cancer Classification Using CNN: Addressing Class Imbalance and Model Performance Analysis LKS Kumar, H Shaziya, R Zaheer Sustainability in Industry 5.0 1, 177-205 , 2024 2024
Automatic Detection and Classification of Lung Cancer in Pulmonary CT Images using Deep Learning H Shaziya Osmania University , 2023 2023 Citations: 2
Lungnodnet-the cnn architecture for detection and classification of lung nodules in pulmonary ct images H Shaziya, S Kattula 2022 IEEE 19th India Council International Conference (INDICON), 1-6 , 2022 2022 Citations: 4
Fully Convolutional Network and UNet for Lung Segmentation PSK Humera Shaziya International Journal for Research Trends and Innovation 7 (7) , 2022 2022
Explainable Deep Learning Through Grad-CAM and Feature Visualization for the Detection of COVID-19 in Chest X-ray Images H Shaziya Advanced Technologies and Societal Change book series (ATSC), pp 27-34 , 2021 2021 Citations: 6
Impact of hyperparameters on model development in deep learning H Shaziya, R Zaheer Proceedings of International Conference on Computational Intelligence and … , 2020 2020 Citations: 22
Strategies to effectively integrate visualization with active learning in computer science class H Shaziya, R Zaheer Proceedings of International Conference on Computational Intelligence and … , 2020 2020 Citations: 2
Pulmonary CT images segmentation using CNN and UNet models of deep learning H Shaziya, K Shyamala 2020 IEEE Pune section international conference (PuneCon), 195-201 , 2020 2020 Citations: 28
Comprehensive review of automatic lung segmentation techniques on pulmonary CT images H Shaziya, K Shyamala, R Zaheer 2019 Third International Conference on Inventive Systems and Control (ICISC … , 2019 2019 Citations: 7
A study of the optimization algorithms in deep learning R Zaheer, H Shaziya 2019 third international conference on inventive systems and control (ICISC … , 2019 2019 Citations: 258
A study of the optimization algorithms in deep learning. In2019 third international conference on inventive systems and control (ICISC)(pp. 536-539) R Zaheer, H Shaziya IEEE , 2019 2019 Citations: 4
Automatic Lung Segmentation on Thoracic CT Scans Using U-Net Convolutional Network H Shaziya, K Shyamala, R Zaheer IEEE , 2018 2018 Citations: 98
Design and Implementation of ConvNet for Handwritten Digits Classification on Graphical Processing Unit H Shaziya, K Shyamala, R Zaheer IEEE Xplore Digital Library, 0485-0490 , 2018 2018
MOST CITED SCHOLAR PUBLICATIONS
A study of the optimization algorithms in deep learning R Zaheer, H Shaziya 2019 third international conference on inventive systems and control (ICISC … , 2019 2019 Citations: 258
Automatic Lung Segmentation on Thoracic CT Scans Using U-Net Convolutional Network H Shaziya, K Shyamala, R Zaheer IEEE , 2018 2018 Citations: 98
GPU-based empirical evaluation of activation functions in convolutional neural networks R Zaheer, H Shaziya 2018 2nd international conference on inventive systems and control (ICISC … , 2018 2018 Citations: 46
Prediction of students performance in semester exams using a naïve bayes classifier H Shaziya, R Zaheer, G Kavitha International Journal of Innovative Research in Science, Engineering and … , 2015 2015 Citations: 34
Pulmonary CT images segmentation using CNN and UNet models of deep learning H Shaziya, K Shyamala 2020 IEEE Pune section international conference (PuneCon), 195-201 , 2020 2020 Citations: 28
Text categorization of movie reviews for sentiment analysis H Shaziya, G Kavitha, R Zaheer International Journal of Innovative Research in Science, Engineering and … , 2015 2015 Citations: 27
A survey of natural language interface to database management system B Sujatha, DSV Raju, H Shaziya International Journal of Science and Advance Technology 2 (6), 56-61 , 2012 2012 Citations: 23
Impact of hyperparameters on model development in deep learning H Shaziya, R Zaheer Proceedings of International Conference on Computational Intelligence and … , 2020 2020 Citations: 22
Comprehensive review of automatic lung segmentation techniques on pulmonary CT images H Shaziya, K Shyamala, R Zaheer 2019 Third International Conference on Inventive Systems and Control (ICISC … , 2019 2019 Citations: 7
Explainable Deep Learning Through Grad-CAM and Feature Visualization for the Detection of COVID-19 in Chest X-ray Images H Shaziya Advanced Technologies and Societal Change book series (ATSC), pp 27-34 , 2021 2021 Citations: 6
Lungnodnet-the cnn architecture for detection and classification of lung nodules in pulmonary ct images H Shaziya, S Kattula 2022 IEEE 19th India Council International Conference (INDICON), 1-6 , 2022 2022 Citations: 4
A study of the optimization algorithms in deep learning. In2019 third international conference on inventive systems and control (ICISC)(pp. 536-539) R Zaheer, H Shaziya IEEE , 2019 2019 Citations: 4
Automatic Detection and Classification of Lung Cancer in Pulmonary CT Images using Deep Learning H Shaziya Osmania University , 2023 2023 Citations: 2
Strategies to effectively integrate visualization with active learning in computer science class H Shaziya, R Zaheer Proceedings of International Conference on Computational Intelligence and … , 2020 2020 Citations: 2
A Study of the Various Architectures for Natural Language Interface to DBs B Sujatha, DSV Raju, H Shaziya International Journal of Computer Science and Network (IJCSN) , 2012 2012 Citations: 2
The Future of Intelligent, Connected Healthcare through Industry 6.0 HS al The Transformation of the Metaverse: Blockchain and Healthcare in Industry 6.0 , 2026 2026
Connecting Supply Chains in Real-Time though the Internet of Things HS al Next-Gen Supply Chains: Technologies, Trends, and the Future of Global … , 2026 2026
A Comprehensive Framework for DDoS Intrusion Detection Using CICIDS 2017 Through Preprocessing, Modeling and Explainability with XGBoost and CICIDS 2017 H Shaziya Converging Intelligence Multidisciplinary Advances in AI-Driven Cybersecurity , 2026 2026
Enhancing CNN Performance on MNIST through Metaheuristic Hyperparameter Optimization HS al International Journal of Recent Development in Engineering and Technology 15 … , 2026 2026
Enhancing CNN Performance on MNIST through Metaheuristic Hyperparameter Optimization DHS Raniah Zaheer International Journal of Recent Development in Engineering and Technology 15 (1) , 2026 2026
