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| 1 | Unintentional falls mortality among elderly in the United States: Time for action显示文摘 | Hasanat Alamgir Sana Muazzam Muazzam Nasrullah | 2011 | Injury2011，，： | 1 |
| 2 | Toll-like receptor pathways in the immune responses to mycobacteria 显示文摘 | Valerie Q Cecile F Muazzam J | 2004 | Microbes and Infection2004，6，8： | 1 |
| 3 | Ureteric Injury due to the Use of LigaSure显示文摘 | Muazzam Tahir William Gilkison M. Perachino F. Ramezanzadeh | 2013 | Case Reports in Urology2013，，： | 1 |
| 4 | Unintentional falls mortality among elderly in the United States: time for action 显示文摘 | Alamgir H Muazzam S Nasrullah M | 2012 | Injury2012，43，12： | 1 |
| 5 | An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment显示文摘Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model. | Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 6 | A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos显示文摘The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)etc.Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way.Instead of the classical random walk,this paper proposes the quantum walk to achieve high image security.Classical random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave functions.The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity etc.All experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally efficient.In the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively. | Muhammad Islam Kamran Muazzam A.Khan Suliman A.Alsuhibany Yazeed Yasin Ghadi Arshad Jameel Arif Jawad Ahmad | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 7 | An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3显示文摘Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect videos.However,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant information.Hence,to automate this manual procedure of video assessment,an accurate and efficient automated system is a greater necessity.From this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy(PME)method tofirst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to thefield of underwater archaeology,stock assessment,and surveillance. | Mubashir Javaid Muazzam Maqsood Farhan Aadil Jibran Safdar Yongsung Kim | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 8 | An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities显示文摘Innovations on the Internet of Everything(IoE)enabled systems are driving a change in the settings where we interact in smart units,recognized globally as smart city environments.However,intelligent video-surveillance systems are critical to increasing the security of these smart cities.More precisely,in today’s world of smart video surveillance,person re-identification(Re-ID)has gained increased consideration by researchers.Various researchers have designed deep learningbased algorithms for person Re-ID because they have achieved substantial breakthroughs in computer vision problems.In this line of research,we designed an adaptive feature refinementbased deep learning architecture to conduct person Re-ID.In the proposed architecture,the inter-channel and inter-spatial relationship of features between the images of the same individual taken from nonidentical camera viewpoints are focused on learning spatial and channel attention.In addition,the spatial pyramid pooling layer is inserted to extract the multiscale and fixed-dimension feature vectors irrespective of the size of the feature maps.Furthermore,the model’s effectiveness is validated on the CUHK01 and CUHK02 datasets.When compared with existing approaches,the approach presented in this paper achieves encouraging Rank 1 and 5 scores of 24.6% and 54.8%,respectively. | Muazzam MAQSOOD Sadaf YASMIN Saira GILLANI Maryam BUKHARI Seungmin RHO Sang-Soo YEO | 2023 | Frontiers of Computer Science2023，17，4： | 0 |
| 9 | An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection显示文摘Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain vessels.CMBs can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain injury.Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke.The CMBs seriously impact individuals’life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life.The existing work report good results but often ignores false-positive’s perspective for this research area.In this paper,an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images(SWI).The proposed framework consists of four main phases(i)making clusters of brain Magnetic Resonance Imaging(MRI)using k-mean classifier(ii)reduce false positives for better classification results(iii)discriminative feature extraction specific to CMBs(iv)classification using a five layers convolutional neural network(CNN).The proposed method is evaluated on a public dataset available for 20 subjects.The proposed system shows an accuracy of 98.9%and a 1.1%false-positive rate value.The results show the superiority of the proposed work as compared to existing states of the art methods. | Sitara Afzal Muazzam Maqsood Irfan Mehmood Muhammad Tabish Niaz Sanghyun Seo | 2021 | Computers, Materials & Continua2021，，3： | 0 |
| 10 | An Immutable Framework for Smart Healthcare Using Blockchain Technology显示文摘The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters. | Faneela Muazzam A.Khan Suliman A.Alsuhibany Walid El-Shafai Mujeeb Ur Rehman Jawad Ahmad | 2023 | Computer Systems Science & Engineering2023，46，7： | 0 |
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