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Zuhaib Nishtar

Bio: Zuhaib Nishtar is an academic researcher. The author has contributed to research in topics: Mobile robot & Population. The author has an hindex of 1, co-authored 2 publications receiving 4 citations.

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Journal ArticleDOI
TL;DR: In this paper, the authors proposed a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and non-focal signals.
Abstract: For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

23 citations

Journal ArticleDOI
01 Jun 2021
TL;DR: An automated irrigation system has been developed which irrigates the field in acres and a solar-powered robot is attached with various sensors and with a highresolution camera that tests crop conditions and senses the soil state.
Abstract: Water plays a significant role among other existing natural resources. The daily demand for water supplies is increasingly on the rise as the population grows. To minimize the consumption of water in irrigation, several proposals were suggested. The currently existing system known as the automated irrigation system for effective water resource use with the prediction of the weather (AISWP) functions with a single farm that lacks the reliability in the precision of weather forecasting. So, a robot-based irrigation system has been proposed to improve the performance of the system. To minimize the water usage for crops, an automated irrigation system has been developed which irrigates the field in acres. An additional characteristic of the system has also been given for the soil pH measurement to allow the use of fertilizers accordingly. The solar-powered robot is managed wirelessly by a designated application. The robot is attached with various sensors and with a high-resolution camera that tests crop conditions and senses the soil state. The application has been created to provide information about the soil’s condition such as temperature level, humidity level, water level, and level of nutrients to the PC/Laptop with the real-time values via the GSM module.

7 citations


Cited by
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Journal ArticleDOI
TL;DR: Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design as mentioned in this paper, which has emerged as a technology of choice due to the availability of high computational resources.
Abstract: Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

41 citations

Journal ArticleDOI
TL;DR: In this paper , the authors examined the factors influencing the willingness of Bangladeshi farmers to adopt and pay for the Internet of Things in the agricultural sector by applying the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2).
Abstract: This paper aims to examine the factors influencing the willingness of Bangladeshi farmers to adopt and pay for the Internet of Things (IoT) in the agricultural sector by applying the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). To this end, the study employed a quantitative research methodology and obtained data from 345 farmers from the northern districts of Bangladesh. Using a cross-sectional survey design and convenience sampling method, a study of premium fruit growers was undertaken to assess IoT use in agriculture, and the primary survey data were analyzed using the Structural Equation Modeling (SEM) approach via AMOS 26. The study confirmed that effort expectancy, performance expectancy, facilitating condition, hedonic motivation, government support, price value, personal innovativeness, and trust influence the willingness of Bangladeshi farmers to adopt the IoT. Additionally, predictors such as trust and willingness to adopt were observed to influence the willingness to pay for the IoT, while the construct ‘performance expectancy’ produced no effect. The study also revealed that the willingness to adopt moderates the association between performance expectancy, price value, and willingness to pay for the IoT. This research has novel implications because it investigates the behavior of rural customers with respect to innovation adoption, which in this case is the IoT in agriculture. It outlines precise reasons for the willing adoption of the IoT in agriculture, which will, in turn, assist marketers of IoT technology in the design of appropriate marketing strategies to increase acceptance in rural areas. Using the proposed model that incorporates farmers’ willingness to pay, this empirical study takes the first step in examining whether farmers in a developing economy such as Bangladesh will adopt and pay for the IoT.

15 citations

Journal ArticleDOI
01 Jul 2022-Sensors
TL;DR: A modified deep neural algorithm is proposed to classify untapped pathological and suspicious CTG recordings with the desired time complexity in a newly developed classification algorithm that outperforms the leading architectures with respect to real-time accuracies, sensitivities, and specificities.
Abstract: Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient’s outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.

11 citations

Journal ArticleDOI
TL;DR: In this paper , the authors present an overview of current design trends in construction, current development technology for controlling and monitoring greenhouse microclimates, and the various systems available for managing greenhouse environments.

10 citations