ORCID Profile
0000-0003-2653-9541
Current Organisation
National Skills University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: International Journal of Advanced and Applied Sciences
Date: 2022
DOI: 10.21833/IJAAS.2022.01.004
Abstract: Coronavirus (COVID-19) has turned to be an alarm for the whole world both in terms of health and economics. It is striking the global economy and increasing the unpredictability of the financial market in several ways. Significantly, the pandemic spread stimulated the social distancing which led to the lockdown of the countries’ businesses, financial markets, and daily life events. International oil markets have accommodated the crude oil prices during the early COVID-19 period. However, after the first 50 days, Saudi Arabia has surged the market with oil, which caused a certain decrease in crude oil prices, internationally. Saudi Arabia is one of the biggest oil reserves in the world. International trade is based on oil reservoirs which in turn, have been significantly dislodged by the pandemic. Therefore, it is crucial to study the impact of COVID-19 on the international oil market. The purpose of this study is to investigate the short-term and long-term impact of COVID-19 on the international oil market. The daily crude oil price data is used to analyze the impact of daily price fluctuation over COVID-19 surveillance variables. The correlation between surveillance variables and international crude oil prices is calculated and analyzed. Consequently, the project will help in stabilizing the expected world economic crises and particularly will provide the implications for the policymakers in the oil market.
Publisher: Springer Science and Business Media LLC
Date: 10-04-2020
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Hindawi Limited
Date: 15-03-2021
DOI: 10.1155/2021/6621622
Abstract: Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority overs ling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.
Publisher: Hindawi Limited
Date: 17-08-2020
DOI: 10.1155/2020/8846033
Abstract: Tone mapping operators are designed to display high dynamic range (HDR) images on low dynamic range devices. Clustering-based content and color adaptive tone mapping algorithm aims to maintain the color information and local texture. However, fine details can still be lost in low dynamic range images. This paper presents an effective way of clustering-based content and color adaptive tone mapping algorithm by using fast search and find of density peak clustering. The suggested clustering method reduces the loss of local structure and allows better adaption of color in images. The experiments are carried out to evaluate the effectiveness and performance of proposed technique with state-of-the-art clustering techniques. The objective and subjective evaluation results reveal that fast search and find of density peak preserves more textural information. Therefore, it is most suitable to be used for clustering-based content and color adaptive tone mapping algorithm.
Publisher: Hindawi Limited
Date: 29-11-2018
DOI: 10.1155/2018/7586417
Abstract: The availability of wearable cameras in the consumer market has motivated the users to record their daily life activities and post them on the social media. This exponential growth of egocentric videos demand to develop automated techniques to effectively summarizes the first-person video data. Egocentric videos are commonly used to record lifelogs these days due to the availability of low cost wearable cameras. However, egocentric videos are challenging to process due to the fact that placement of camera results in a video which presents great deal of variation in object appearance, illumination conditions, and movement. This paper presents an egocentric video summarization framework based on detecting important people in the video. The proposed method generates a compact summary of egocentric videos that contains information of the people whom the camera wearer interacts with. Our proposed approach focuses on identifying the interaction of camera wearer with important people. We have used AlexNet convolutional neural network to filter the key-frames (frames where camera wearer interacts closely with the people). We used five convolutional layers and two completely connected hidden layers and an output layer. Dropout regularization method is used to reduce the overfitting problem in completely connected layers. Performance of the proposed method is evaluated on UT Ego standard dataset. Experimental results signify the effectiveness of the proposed method in terms of summarizing the egocentric videos.
Publisher: Elsevier BV
Date: 04-2022
Publisher: SPIE-Intl Soc Optical Eng
Date: 13-10-2018
No related grants have been discovered for Hussain Dawood.