Institution
University of Wah
Education•Rawalpindi, Pakistan•
About: University of Wah is a education organization based out in Rawalpindi, Pakistan. It is known for research contribution in the topics: Per capita income & Rhizobacteria. The organization has 258 authors who have published 466 publications receiving 4719 citations.
Papers
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TL;DR: An anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN is proposed and achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.
Abstract: Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless and real-time transfer of data, are imperative infrastructural advancements to realize the emerging smart world. The sensor-generated data are, however, vulnerable to anomalies caused due to faults, errors, and/or cyberattacks, which may cause accidents resulting in fatal casualties. To help in avoiding such situations by timely detecting anomalies, this study proposes an anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN. The data streams, in the proposed method, are converted into vectors and then processed for anomaly detection. We also designed a method, namely, weight-adjusted fine-tuned ensemble: WAVED, which works on the principle of average predicted probability of multiple classifiers to detect anomalies in CAVs and benchmark the performance of the MSALSTM-CNN method. The MSALSTM-CNN method effectively enhances the anomaly detection rate in both low and high magnitude cases of anomalous instances in the dataset with the gain of up to 2.54% in F-score for detecting different single anomaly types. The method achieves the gain of up to 3.24% in F-score in the case of detecting mixed anomaly types. The experiment results show that the MSALSTM-CNN method achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.
106 citations
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TL;DR: This research work presents a new technique for the detection of tumor that accurately segments and classifies the benign and malignant tumor cases and evaluated on top medical image computing and computer-assisted intervention datasets.
Abstract: Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.
104 citations
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TL;DR: In this article, the most important minerals required for plant growth occupying a strong position among soil macro nutrients, such as P, are often fulfilled by phosphate fertilizers, which is one of the most essential minerals required by plant growth.
Abstract: Phosphorus (P) is one of the most important minerals required for plant growth occupying a strong position among soil macro nutrients. Soil P deficiency is often fulfilled by phosphate fertilizers....
103 citations
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TL;DR: In this paper, the causal relationship among technological innovation, environment pollution, energy consumption, and sustainable economic growth from selected South Asian economies was examined using the premises of the EKC framework in order to identify the causal association between energy growth and nexus of CO2 emissions, and found bidirectional causality between economic growth and energy use.
100 citations
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TL;DR: In this article, the authors investigate consumers' perceptions regarding attributes of online shopping websites that influence their cognitive and affective attitudes and also online purchase intentions and show that consumers' perception of utilitarian attributes and hedonic attributes are significant and positive predictors of cognitive, affective, and purchase intentions.
87 citations
Authors
Showing all 266 results
Name | H-index | Papers | Citations |
---|---|---|---|
Khalid Zaman | 42 | 324 | 6710 |
Asghari Bano | 38 | 169 | 4831 |
Amjad Farooq | 35 | 153 | 4421 |
Naeem Khan | 27 | 146 | 2709 |
Muhammad Ajmal | 20 | 47 | 1094 |
Sohail Hameed | 19 | 39 | 1334 |
Muhammad Usman | 18 | 110 | 1208 |
Asghari Bano | 17 | 45 | 919 |
Anwar Khitab | 13 | 46 | 556 |
Jameel-Un Nabi | 13 | 121 | 950 |
Saira Shahzadi | 12 | 44 | 406 |
Syed Irfan Raza | 12 | 25 | 505 |
Javeria Amin | 12 | 18 | 595 |
Shahab Khushnood | 12 | 67 | 882 |
Muhammad Jahangir | 11 | 37 | 408 |