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The Internet of Things (IoT) is an emerging technology and provides connectivity among physical objects with the support of 5G communication. In recent decades, there have been a lot of applications based on IoT technology for the sustainability of smart cities, such as farming, e-healthcare, education, smart homes, weather monitoring, etc. These applications communicate in a collaborative manner between embedded IoT devices and systematize daily routine tasks. In the literature, many solutions facilitate remote users to gather the observed data by accessing the stored information on the cloud network and lead to smart systems. However, most of the solutions raise significant research challenges regarding information sharing in mobile IoT networks and must be able to stabilize the performance of smart operations in terms of security and intelligence. Many solutions are based on 5G communication to support high user mobility and increase the connectivity among a huge number of IoT devices. However, such approaches lack user and data privacy against anonymous threats and incur resource costs. In this paper, we present a mobility support 5G architecture with real-time routing for sustainable smart cities that aims to decrease the loss of data against network disconnectivity and increase the reliability for 5G-based public healthcare networks. The proposed architecture firstly establishes a mutual relationship among the nodes and mobile sink with shared secret information and lightweight processing. Secondly, multi-secured levels are proposed to protect the interaction with smart transmission systems by increasing the trust threshold over the insecure channels. The conducted experiments are analyzed, and it is concluded that their performance significantly increases the information sustainability for mobile networks in terms of security and routing.
Amjad Rehman; Khalid Haseeb; Tanzila Saba; Jaime Lloret; Zara Ahmed. Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities. Sustainability 2021, 13, 9092 .
AMA StyleAmjad Rehman, Khalid Haseeb, Tanzila Saba, Jaime Lloret, Zara Ahmed. Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities. Sustainability. 2021; 13 (16):9092.
Chicago/Turabian StyleAmjad Rehman; Khalid Haseeb; Tanzila Saba; Jaime Lloret; Zara Ahmed. 2021. "Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities." Sustainability 13, no. 16: 9092.
In recent years, the Green Internet of Things (G-IoT) has gained a lot of attention to developing energy-efficient communication systems. It consists of electronic devices and is integrated with numerous tight constraint sensors for observing the real world and provide communication services to end-users. However, optimal data collection and its management among the heterogeneous G-IoT objects are one of the main challenges. Many researchers are still proposing different solutions to cope with such problems and offering IoT-cloud paradigms for processing, storage, and scalability services However, the data of smart cities is forwarding among connected users using the open-source IoT platform, and sensitive information may be compromised. Therefore, this research aims to propose a model of security measures using the Green Internet of Things with Cloud Integrated Data Management (M-SMDM) for Smart Cities. Firstly, it forms a long-run and energy-efficient connectivity using self-balancing trees and distributing load factors uniformly in green communication systems. Secondly, it addresses the secret key distribution problem between peer nodes and attained trust for both partial and direct communication. In the end, it securing the transmission system from mobile gateways to cloud infrastructure against threats with improved data latency. The security analysis of the proposed M-SMDM model is done along with simulation-based experiments. The attained results disclose the importance of the proposed model in terms of network parameters compared to existing work.
Amjad Rehman; Khalid Haseeb; Tanzila Saba; Hoshang Kolivand. M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities. Environmental Technology & Innovation 2021, 24, 101802 .
AMA StyleAmjad Rehman, Khalid Haseeb, Tanzila Saba, Hoshang Kolivand. M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities. Environmental Technology & Innovation. 2021; 24 ():101802.
Chicago/Turabian StyleAmjad Rehman; Khalid Haseeb; Tanzila Saba; Hoshang Kolivand. 2021. "M-SMDM: A model of security measures using Green Internet of Things with Cloud Integrated Data Management for Smart Cities." Environmental Technology & Innovation 24, no. : 101802.
With the evolution of deep learning technologies, computer vision-related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out-perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three-stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal-to-noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.
Muhammad Sajjad; Farheen Ramzan; Muhammad Usman Ghani Khan; Amjad Rehman; Mahyar Kolivand; Suliman Mohamed Fati; Saeed Ali Bahaj. Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography ( PET ) and synthetic data augmentation. Microscopy Research and Technique 2021, 1 .
AMA StyleMuhammad Sajjad, Farheen Ramzan, Muhammad Usman Ghani Khan, Amjad Rehman, Mahyar Kolivand, Suliman Mohamed Fati, Saeed Ali Bahaj. Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography ( PET ) and synthetic data augmentation. Microscopy Research and Technique. 2021; ():1.
Chicago/Turabian StyleMuhammad Sajjad; Farheen Ramzan; Muhammad Usman Ghani Khan; Amjad Rehman; Mahyar Kolivand; Suliman Mohamed Fati; Saeed Ali Bahaj. 2021. "Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography ( PET ) and synthetic data augmentation." Microscopy Research and Technique , no. : 1.
Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.
Muhammad Zeeshan Khan; Tanzila Saba; Imran Razzak; Amjad Rehman; Saeed Ali Bahaj. Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision. IEEE Access 2021, 9, 100040 -100049.
AMA StyleMuhammad Zeeshan Khan, Tanzila Saba, Imran Razzak, Amjad Rehman, Saeed Ali Bahaj. Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision. IEEE Access. 2021; 9 ():100040-100049.
Chicago/Turabian StyleMuhammad Zeeshan Khan; Tanzila Saba; Imran Razzak; Amjad Rehman; Saeed Ali Bahaj. 2021. "Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision." IEEE Access 9, no. : 100040-100049.
Violence is a critical social problem and demands to evaluate through computer vision approaches. At present, the incidences of violent actions get grown in the community, particularly in public places due to several economic and social causes. Moreover, our society’s populations are increasing day by day and it is challenging to keep citizens within limits as well as monitoring human activities in crowd is too hard. Thus, government organizations including local bodies, require examining such occurrences through smart surveillance. In this research, a lightweight computational architecture has been presented to classify non-violent and violent activities. A model has been proposed to extract time-based features using smart devices, high-speed wireless networks and cloud servers to classify real-time human activities. For this purpose, a deep learning-based model is employed to detect violent activities and assist the stakeholders in exposing such activities in real-time. Convolutional long short-term memory (Conv-LSTM) is employed to extend fully connected LSTM (FC-LSTM) to capture the frame and detect violent actions. The proposed model accomplished 95.16% validation accuracy using a standard crowd anomaly dataset.
Tanzila Saba. Real time anomalies detection in crowd using convolutional long short-term memory network. Journal of Information Science 2021, 1 .
AMA StyleTanzila Saba. Real time anomalies detection in crowd using convolutional long short-term memory network. Journal of Information Science. 2021; ():1.
Chicago/Turabian StyleTanzila Saba. 2021. "Real time anomalies detection in crowd using convolutional long short-term memory network." Journal of Information Science , no. : 1.
Machine learning techniques are proven valuable for the Internet of things (IoT) due to intelligent and cost-effective computing processes. In recent decades, wireless sensor network (WSN) and machine learning are integrated to give significant improvements for energy-based systems. However, resourceful routes analytic with nominal energy consumption are some demanding challenges. Moreover, WSN operates in an unpredictable space and a lot of network threats can be harmful to smart and secure data gathering. Consequently, security against such threats is another major concern for low-power sensors. Therefore, we aim to present a machine learning-based approach for autonomous IoT Security to achieve optimal energy efficiency and reliable transmissions. First, the proposed protocol optimizes network performance using a model-free Q-learning algorithm and achieves fault-tolerant data transmission. Second, it accomplishes data confidentiality against adversaries using a cryptography-based deterministic algorithm. The proposed protocol demonstrates better conclusions than other existing solutions.
Tanzila Saba; Khalid Haseeb; Asghar Ali Shah; Amjad Rehman; Usman Tariq; Zahid Mehmood. A Machine-Learning-Based Approach for Autonomous IoT Security. IT Professional 2021, 23, 69 -75.
AMA StyleTanzila Saba, Khalid Haseeb, Asghar Ali Shah, Amjad Rehman, Usman Tariq, Zahid Mehmood. A Machine-Learning-Based Approach for Autonomous IoT Security. IT Professional. 2021; 23 (3):69-75.
Chicago/Turabian StyleTanzila Saba; Khalid Haseeb; Asghar Ali Shah; Amjad Rehman; Usman Tariq; Zahid Mehmood. 2021. "A Machine-Learning-Based Approach for Autonomous IoT Security." IT Professional 23, no. 3: 69-75.
Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.
Amjad Rehman; Tanzila Saba; Usman Tariq; Noor Ayesha. Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons. IT Professional 2021, 23, 63 -68.
AMA StyleAmjad Rehman, Tanzila Saba, Usman Tariq, Noor Ayesha. Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons. IT Professional. 2021; 23 (3):63-68.
Chicago/Turabian StyleAmjad Rehman; Tanzila Saba; Usman Tariq; Noor Ayesha. 2021. "Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons." IT Professional 23, no. 3: 63-68.
Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named “L4-Branched-ActionNet”. The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in order of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work’s soundness.
Tanzila Saba; Amjad Rehman; Rabia Latif; Suliman Mohamed Fati; Mudassar Raza; Muhammad Sharif. Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization. IEEE Access 2021, 9, 1 -1.
AMA StyleTanzila Saba, Amjad Rehman, Rabia Latif, Suliman Mohamed Fati, Mudassar Raza, Muhammad Sharif. Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleTanzila Saba; Amjad Rehman; Rabia Latif; Suliman Mohamed Fati; Mudassar Raza; Muhammad Sharif. 2021. "Suspicious Activity Recognition Using Proposed Deep L4-Branched-ActionNet with Entropy Coded Ant Colony System Optimization." IEEE Access 9, no. : 1-1.
The Internet of Medical Things (IoMT) has shown incredible development with the growth of medical systems using wireless information technologies. Medical devices are biosensors that can integrate with physical things to make smarter healthcare applications that are collaborated on the Internet. In recent decades, many applications have been designed to monitor the physical health of patients and support expert teams for appropriate treatment. The medical devices are attached to patients’ bodies and connected with a cloud computing system for obtaining and analyzing healthcare data. However, such medical devices operate on battery powered sensors with limiting constraints in terms of memory, transmission, and processing resources. Many healthcare solutions are helping the community with the efficient monitoring of patients’ conditions using cloud computing, however, mostly incur latency in data collection and storage. Therefore, this paper presents a model for the Secured Big Data analytics using Edge–Cloud architecture (SBD-EC), which aims to provide distributed and timely computation of a decision-oriented medical system. Moreover, the mobile edges cooperate with the cloud level to present a secure algorithm, achieving reliable availability of medical data with privacy and security against malicious actions. The performance of the proposed model is evaluated in simulations and the results obtained demonstrate significant improvement over other solutions.
Amjad Rehman; Khalid Haseeb; Tanzila Saba; Jaime Lloret; Usman Tariq. Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things. Electronics 2021, 10, 1273 .
AMA StyleAmjad Rehman, Khalid Haseeb, Tanzila Saba, Jaime Lloret, Usman Tariq. Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things. Electronics. 2021; 10 (11):1273.
Chicago/Turabian StyleAmjad Rehman; Khalid Haseeb; Tanzila Saba; Jaime Lloret; Usman Tariq. 2021. "Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things." Electronics 10, no. 11: 1273.
The protection of digital content is increasingly becoming a significant issue for researchers and engineers. In this context, nonlinear dynamic systems play a vital role in information security through their chaotic behavior and susceptibility to initial conditions. This research presents a 3D chaotic map-based symmetric algorithm for multiple images to improve encryption efficiency and encourage secure transmission. The proposed scheme comprises the following four modules: the combination (the images are combined into a single image by merging their color channels); the permutation (using the suggested 3D chaotic map); the S-box generation; and the substitution through the AES substitution method. The proposed algorithm’s encryption strength was determined through Entropy, Correlation coefficient, NPCR, and UACI analyses, which were then compared to the past techniques. Furthermore, the proposed method is assessed in terms of its computation time. Results demonstrate that it is highly efficient and secure for real-time communication.
Muhammad Tanveer; Tariq Shah; Amjad Rehman; Asif Ali; Ghazanfar Farooq Siddiqui; Tanzila Saba; Usman Tariq. Multi-Images Encryption Scheme Based on 3D Chaotic Map and Substitution Box. IEEE Access 2021, 9, 73924 -73937.
AMA StyleMuhammad Tanveer, Tariq Shah, Amjad Rehman, Asif Ali, Ghazanfar Farooq Siddiqui, Tanzila Saba, Usman Tariq. Multi-Images Encryption Scheme Based on 3D Chaotic Map and Substitution Box. IEEE Access. 2021; 9 ():73924-73937.
Chicago/Turabian StyleMuhammad Tanveer; Tariq Shah; Amjad Rehman; Asif Ali; Ghazanfar Farooq Siddiqui; Tanzila Saba; Usman Tariq. 2021. "Multi-Images Encryption Scheme Based on 3D Chaotic Map and Substitution Box." IEEE Access 9, no. : 73924-73937.
Regression techniques are generally used to predict a response variable using one or more predictor variables. In many fields of study, the regressors can be highly intercorrelated, which leads to the problem of multicollinearity. Consequently, the ordinary least squares estimates become inconsistent and lead to wrong inferences. To handle the problem, machine learning techniques particularly, the ridge regression approach, are commonly used. In this paper, we revisit the problem of estimating the ridge parameter “ ${k}$ ” by proposing some new estimators using the Jackknife method and compare them with some existing estimators. The performance of the proposed estimators compared to the existing ones is evaluated using extensive Monte Carlo simulations as well as two real data sets. The results suggested that the proposed estimators outperform the existing estimators.
Ismail Shah; Faiza Sajid; Sajid Ali; Amjad Rehman; Saeed Ali Bahaj; Suliman Mohamed Fati. On the Performance of Jackknife Based Estimators for Ridge Regression. IEEE Access 2021, 9, 68044 -68053.
AMA StyleIsmail Shah, Faiza Sajid, Sajid Ali, Amjad Rehman, Saeed Ali Bahaj, Suliman Mohamed Fati. On the Performance of Jackknife Based Estimators for Ridge Regression. IEEE Access. 2021; 9 ():68044-68053.
Chicago/Turabian StyleIsmail Shah; Faiza Sajid; Sajid Ali; Amjad Rehman; Saeed Ali Bahaj; Suliman Mohamed Fati. 2021. "On the Performance of Jackknife Based Estimators for Ridge Regression." IEEE Access 9, no. : 68044-68053.
This research presents a reverse engineering approach to discover the patterns and evolution behavior of SARS-CoV-2 using AI and big data. Accordingly, we have studied five viral families (Orthomyxoviridae, Retroviridae, Filoviridae, Flaviviridae, and Coronaviridae) that happened in the era of the past one hundred years. To capture the similarities, common characteristics, and evolution behavior for prediction concerning SARS-CoV-2. And how reverse engineering using Artificial intelligence (AI) and big data is efficient and provides wide horizons. The results show that SARS-CoV-2 shares the same highest active amino acids (S, L, and T) with the mentioned viral families. As known, that affects the building function of the proteins. We have also devised a mathematical formula representing how we calculate the evolution difference percentage between each virus concerning its phylogenic tree. It shows that SARS-CoV-2 has fast mutation evolution concerning its time of arising. Artificial Intelligence (AI) is used to predict the next evolved instance of SARS-CoV-2 by utilizing the phylogenic tree data as a corpus using Long Short-term Memory (LSTM). This paper has shown the evolved viral instance prediction process on ORF7a protein from SARS-CoV-2 as the first stage to predict the complete mutant virus. Finally, in this research, we have focused on analyzing the virus to its primary factors by reverse engineering using AI and big data to understand the viral similarities, patterns, and evolution behavior to predict future viral mutations of the virus artificially in a systematic and logical way.
Ahmad M. Abu Haimed; Tanzila Saba; Ayman Albasha; Amjad Rehman; Mahyar Kolivand. Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM). Environmental Technology & Innovation 2021, 22, 101531 -101531.
AMA StyleAhmad M. Abu Haimed, Tanzila Saba, Ayman Albasha, Amjad Rehman, Mahyar Kolivand. Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM). Environmental Technology & Innovation. 2021; 22 ():101531-101531.
Chicago/Turabian StyleAhmad M. Abu Haimed; Tanzila Saba; Ayman Albasha; Amjad Rehman; Mahyar Kolivand. 2021. "Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM)." Environmental Technology & Innovation 22, no. : 101531-101531.
Exam proctoring is a hectic task i.e., the monitoring of students’ activities becomes difficult for supervisors in the examination rooms. It is a costly approach that requires much labor. Also, it is a difficult task for supervisors to keep an eye on all students at a time. Automatic exam activities recognition is therefore necessitating and a demanding field of research. In this research work, categorization of students’ activities during the exam is performed using a deep learning approach. A new deep CNN architecture with 46 layers is proposed which contains the characteristics of deep AlexNet and SqueezeNet. The model is engineered first with slight modifications in AlexNet. After then, the squeezed branch-like structure of SqueezeNet is grafted/embedded at two locations in the modified AlexNet architecture. The model is named as L2-GraftNet because of dual grafting blocks. The proposed model is first converted to a pre-trained model by performing its training with SoftMax classifier on the CIFAR-100 dataset. Afterwards, the features of the dataset prepared for exam activities categorization are extracted from the above-mentioned pre-trained model. The extracted features are then fed to the atom search optimization (ASO) approach for features optimization. The optimized features are passed to different variants of SVM and KNN classifiers. The best performance results attained are on the Fine KNN classifier with an accuracy of 93.88%. The satisfactory results prove the robustness of the proposed framework. Also, the proposed categorization provides a base for automated exam proctoring without the need for proctors in the exam halls.
Tanzila Saba; Amjad Rehman; Nor Shahida Mohd Jamail; Souad Larabi Marie-Sainte; Mudassar Raza; Muhammad Sharif. Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach. IEEE Access 2021, 9, 47639 -47656.
AMA StyleTanzila Saba, Amjad Rehman, Nor Shahida Mohd Jamail, Souad Larabi Marie-Sainte, Mudassar Raza, Muhammad Sharif. Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach. IEEE Access. 2021; 9 (99):47639-47656.
Chicago/Turabian StyleTanzila Saba; Amjad Rehman; Nor Shahida Mohd Jamail; Souad Larabi Marie-Sainte; Mudassar Raza; Muhammad Sharif. 2021. "Categorizing the Students’ Activities for Automated Exam Proctoring Using Proposed Deep L2-GraftNet CNN Network and ASO Based Feature Selection Approach." IEEE Access 9, no. 99: 47639-47656.
Prostate cancer (PCa) is a severe type of cancer and causes major deaths among men due to its poor diagnostic system. The images obtained from patients with carcinoma consist of complex and necessary features that cannot be extracted readily by traditional diagnostic techniques. This research employed deep learning long short-term memory $(LSTM)$ and Residual Net $(ResNet-101)$ , independent of hand-crafted features, and is fine-tuned. The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix $(GLCM)$ using non-deep learning classifiers such as support vector machine $(SVM)$ Gaussian Kernel, k-nearest neighbor-Cosine $(KNN-Cosine)$ , kernel naive Bayes, decision tree $(DT)$ and RUSBoost tree. This study reduces the features of carcinoma images, employed machine learning and deep learning approaches. For validation of training and testing data, a jack-knife ten-fold cross-validation method was used. The performance was measured using a confusion matrix such as sensitivity, specificity, positive predictive value $(PPV)$ , negative predictive value $(NPV)$ , accuracy $(AC)$ , Mathews Correlation Coefficient ( $MCC$ ), and area under the curve $(AUC)$ . The most remarkable performance was obtained using non-deep learning methods with $GLCM$ features using KNN-Cosine with sensitivity (98.00%), specificity (99.25%), PPV (98.99%), NPV (99.11%), accuracy (99.07%), and AUC (0.998). The LSTM deep learning method yields performance with sensitivity (98.33%), specificity (100%), PPV (100%), NPV (99.26%), accuracy (99.48%), MCC (0.9879) and AUC (0.9999), where using Deep learning method $ResNet-101$ , we obtained (100%) Accuracy and AUC (1) for Kernel Naive Bayes, SVM Gaussian and RUSBoost Tree. The results show that $ResNet-101$ deep learning outperformed than non-deep learning methods and $LSTM$ . Thus, the deep learning method $ResNet-101$ could be used as a better predictor for the detection of prostate cancer.
Saqib Iqbal; Ghazanfar Farooq Siddiqui; Amjad Rehman; Lal Hussain; Tanzila Saba; Usman Tariq; Adeel Ahmed Abbasi. Prostate Cancer Detection Using Deep Learning and Traditional Techniques. IEEE Access 2021, 9, 27085 -27100.
AMA StyleSaqib Iqbal, Ghazanfar Farooq Siddiqui, Amjad Rehman, Lal Hussain, Tanzila Saba, Usman Tariq, Adeel Ahmed Abbasi. Prostate Cancer Detection Using Deep Learning and Traditional Techniques. IEEE Access. 2021; 9 ():27085-27100.
Chicago/Turabian StyleSaqib Iqbal; Ghazanfar Farooq Siddiqui; Amjad Rehman; Lal Hussain; Tanzila Saba; Usman Tariq; Adeel Ahmed Abbasi. 2021. "Prostate Cancer Detection Using Deep Learning and Traditional Techniques." IEEE Access 9, no. : 27085-27100.
COVID‐19 has impacted the world in many ways, including loss of lives, economic downturn and social isolation. COVID‐19 was emerged due to the SARS‐CoV‐2 that is highly infectious pandemic. Every country tried to control the COVID‐19 spread by imposing different types of lockdowns. Therefore, there is an urgent need to forecast the daily confirmed infected cases and deaths in different types of lockdown to select the most appropriate lockdown strategies to control the intensity of this pandemic and reduce the burden in hospitals. Currently are imposed three types of lockdown (partial, herd, complete) in different countries. In this study, three countries from every type of lockdown were studied by applying time‐series and machine learning models, named as random forests, K‐nearest neighbors, SVM, decision trees (DTs), polynomial regression, Holt winter, ARIMA, and SARIMA to forecast daily confirm infected cases and deaths due to COVID‐19. The models' accuracy and effectiveness were evaluated by error based on three performance criteria. Actually, a single forecasting model could not capture all data sets' trends due to the varying nature of data sets and lockdown types. Three top‐ranked models were used to predict the confirmed infected cases and deaths, the outperformed models were also adopted for the out‐of‐sample prediction and obtained very close results to the actual values of cumulative infected cases and deaths due to COVID‐19. This study has proposed the auspicious models for forecasting and the best lockdown strategy to mitigate the causalities of COVID‐19.
Tanzila Saba; Ibrahim Abunadi; Mirza Naveed Shahzad; Amjad Rehman Khan. Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types. Microscopy Research and Technique 2021, 1 .
AMA StyleTanzila Saba, Ibrahim Abunadi, Mirza Naveed Shahzad, Amjad Rehman Khan. Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types. Microscopy Research and Technique. 2021; ():1.
Chicago/Turabian StyleTanzila Saba; Ibrahim Abunadi; Mirza Naveed Shahzad; Amjad Rehman Khan. 2021. "Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types." Microscopy Research and Technique , no. : 1.
Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law‐enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag‐of‐Words model to extract distinct and robust features from the iris image, followed by ensemble multi‐class‐SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi‐class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA‐v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.
Amjad Rehman. Light microscopic iris classification using ensemble multi‐class support vector machine. Microscopy Research and Technique 2021, 84, 982 -991.
AMA StyleAmjad Rehman. Light microscopic iris classification using ensemble multi‐class support vector machine. Microscopy Research and Technique. 2021; 84 (5):982-991.
Chicago/Turabian StyleAmjad Rehman. 2021. "Light microscopic iris classification using ensemble multi‐class support vector machine." Microscopy Research and Technique 84, no. 5: 982-991.
Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is time-consuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlighting the importance of each step have been presented. A detailed discussion and future directions have been provided at the end.
Amna Liaqat; Muhammad Attique Khan; Muhammad Sharif; Mamta Mittal; Tanzila Saba; K. Suresh Manic; Feras Nadhim Hasoon Al Attar. Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Current Medical Imaging Formerly: Current Medical Imaging Reviews 2021, 16, 1229 -1242.
AMA StyleAmna Liaqat, Muhammad Attique Khan, Muhammad Sharif, Mamta Mittal, Tanzila Saba, K. Suresh Manic, Feras Nadhim Hasoon Al Attar. Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Current Medical Imaging Formerly: Current Medical Imaging Reviews. 2021; 16 (10):1229-1242.
Chicago/Turabian StyleAmna Liaqat; Muhammad Attique Khan; Muhammad Sharif; Mamta Mittal; Tanzila Saba; K. Suresh Manic; Feras Nadhim Hasoon Al Attar. 2021. "Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review." Current Medical Imaging Formerly: Current Medical Imaging Reviews 16, no. 10: 1229-1242.
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time‐consuming, and vulnerable to error. Hence, automated computer‐assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi‐classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.
Tariq Sadad; Amjad Rehman; Asim Munir; Tanzila Saba; Usman Tariq; Noor Ayesha; Rashid Abbasi. Brain tumor detection and multi‐classification using advanced deep learning techniques. Microscopy Research and Technique 2021, 84, 1296 -1308.
AMA StyleTariq Sadad, Amjad Rehman, Asim Munir, Tanzila Saba, Usman Tariq, Noor Ayesha, Rashid Abbasi. Brain tumor detection and multi‐classification using advanced deep learning techniques. Microscopy Research and Technique. 2021; 84 (6):1296-1308.
Chicago/Turabian StyleTariq Sadad; Amjad Rehman; Asim Munir; Tanzila Saba; Usman Tariq; Noor Ayesha; Rashid Abbasi. 2021. "Brain tumor detection and multi‐classification using advanced deep learning techniques." Microscopy Research and Technique 84, no. 6: 1296-1308.
Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the riskiest is melanoma, although it starts in a few different ways. The patient is extremely unaware of recognizing skin malignant growth at the initial stage. Literature is evident that various handcrafted and automatic deep learning features are employed to diagnose skin cancer using the traditional machine and deep learning techniques. The current research presents a comparison of skin cancer diagnosis techniques using handcrafted and non‐handcrafted features. Additionally, clinical features such as Menzies method, seven‐point detection, asymmetry, border color and diameter, visual textures (GRC), local binary patterns, Gabor filters, random fields of Markov, fractal dimension, and an oriental histography are also explored in the process of skin cancer detection. Several parameters, such as jacquard index, accuracy, dice efficiency, preciseness, sensitivity, and specificity, are compared on benchmark data sets to assess reported techniques. Finally, publicly available skin cancer data sets are described and the remaining issues are highlighted.
Tanzila Saba. Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features. Microscopy Research and Technique 2021, 84, 1272 -1283.
AMA StyleTanzila Saba. Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features. Microscopy Research and Technique. 2021; 84 (6):1272-1283.
Chicago/Turabian StyleTanzila Saba. 2021. "Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features." Microscopy Research and Technique 84, no. 6: 1272-1283.
Gopi Krishna Durbhaka; Barani Selvaraj; Mamta Mittal; Tanzila Saba; Amjad Rehman; Lalit Mohan Goyal. Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms. Computers, Materials & Continua 2021, 66, 2041 -2059.
AMA StyleGopi Krishna Durbhaka, Barani Selvaraj, Mamta Mittal, Tanzila Saba, Amjad Rehman, Lalit Mohan Goyal. Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms. Computers, Materials & Continua. 2021; 66 (2):2041-2059.
Chicago/Turabian StyleGopi Krishna Durbhaka; Barani Selvaraj; Mamta Mittal; Tanzila Saba; Amjad Rehman; Lalit Mohan Goyal. 2021. "Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms." Computers, Materials & Continua 66, no. 2: 2041-2059.