Amandeep kaur
@nitdelhi.ac.in
Assistant Professor, National Institute of Technology, Delhi
National Institute of Technology, Delhi
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Science Applications, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Amandeep Kaur and Karanjeet Singh Kahlon
MDPI AG
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopment disorder that affects millions of children and typically persists into adulthood. It must be diagnosed efficiently and consistently to receive adequate treatment, otherwise, it can have a detrimental impact on the patient’s professional performance, mental health, and relationships. In this work, motor activity data of adults suffering from ADHD and clinical controls has been preprocessed to obtain 788 activity-related statistical features. Afterwards, principal component analysis has been carried out to obtain significant features for accurate classification. These features are then fed into six different machine learning algorithms for classification, which include C4.5, kNN, Random Forest, LogitBoost, SVM, and Naive Bayes. The detailed evaluation of the results through 10-fold cross-validation reveals that SVM outperforms other classifiers with an accuracy of 98.43%, F-measure of 98.42%, sensitivity of 98.33%, specificity of 98.56% and AUC of 0.983. Thus, a PCA-based SVM approach appears to be an effective choice for accurate identification of ADHD patients among other clinical controls using real-time analysis of activity data.
Amandeep Kaur, Sahil, and Sandeep Kumar Sood
Elsevier BV
Aarti Yadav and Amandeep Kaur
IEEE
That both type and amount of food demanded have increased, necessitating farming technology through expansion. Adopted a new strategy is booming thanks to social media and Items (IoT), a prospective technique. Universities and climate scientists are creating Smart goods and resolve a range of agronomic concerns. This research offers a thorough literature assessment by looking at IIoT and their present use in a wide range of industries. The whole review conducted for this study was predicated on a question of research published in recognized papers over the last ten years. A broad range of articles were carefully chosen or sorted among courses. Its report's main goal will be to compile every pertinent data about IoT crop monitoring, electronic systems, tcp / ip, & public types. Secondly, it is dealing with major issues and obstacles that are now explored there in agricultural industry.
Amandeep Kaur and Sandeep K. Sood
Elsevier BV
Sandeep K. Sood, Amandeep Kaur, and Vaishali Sood
Elsevier BV
Amandeep Kaur and Karanjeet Singh Kahlon
IEEE
Learning Analytics (LA) is a research subject that entails the development of methods and strategies for evaluating, quantifying, and interpreting trends in data gathered from various educational environments and activities to aid in the development of learning systems. This article provides an in-depth look into the field of Learning Analytics. It also examines the most widely referenced papers on the topic. The focus of this paper is on a thorough list of measures that have an impact on the Learning Analytics Framework. The parametric evaluation of LA is based on its intimate contact with all stakeholders, including educators, students, researchers, caretakers, and regulatory organizations. It also highlights the future of learning analytics.
Amandeep Kaur and Sandeep K Sood
Oxford University Press (OUP)
Abstract Drought is considered as one of the most extremely destructive natural disasters with catastrophic impact on hydrological balance, agriculture outcome, wildlife habitat and financial budget. Therefore, there is a need for an efficient system to predict and forecast drought situations. There are a number of drought indices to assess the severity of droughts considering different causing factors. Most of them does not take important factors into consideration. Internet of Things (IoT) has demonstrated phenomenal growth and has successfully worked in monitoring environmental conditions. This paper proposes an IoT-enabled fog-based framework for the prediction and forecasting of droughts. At the fog layer, the dimensions of the data are decreased using singular vector decomposition. Artificial neural network with genetic algorithm classifier is used to assess drought severity category to the given event and Holt-Winters method is used to predict the future drought conditions. The proposed system is implemented using datasets from government agencies and it proves its effectiveness in assessing drought severity level.
Amandeep Kaur and Sandeep K. Sood
Elsevier BV
Amandeep Kaur and Sandeep K. Sood
Informa UK Limited
ABSTRACT Drought is one of the most recurrent natural disasters with cataclysmic effects on water budget, crop production, economic progression and public health. These consequences are magnified by the climate change leading to more intense drought conditions. A number of drought indices have been presented to calibrate the drought severity with its own strengths and limitations. Many of them are region-specific and unable to exhibit the alterations in significant drought inducing elements. Internet of Things (IoT) is well-suited for continuous monitoring, collection and analysis of different environmental phenomena. The dimensionality of the data collected about drought inducing attributes temperature, humidity, precipitation, evapotranspiration, groundwater, soil moisture at different depths, streamflow and season is reduced using PCA (Principal Component Analysis) at fog layer. Cloud layer estimates the drought severity level using Artificial Neural Network (ANN) whose parameters are optimised with Genetic Algorithm (GA) to get more accurate system and ARIMA method is used to forecast the drought for different time frames. Experimentation done on data collected from government websites shows that proposed system performs well in terms of accuracy, sensitivity, specificity, precision and F-measure with values 95.03%, 90.6%, 96.73%, 91.42% and 91.01%.
Amandeep Kaur and Sandeep K. Sood
Springer Science and Business Media LLC
Amandeep Kaur and Sandeep K. Sood
Institute of Electrical and Electronics Engineers (IEEE)
Drought is a catastrophic natural disaster with significant impact on financial stability, hydrological budget, public health, and agricultural productivity. Numerous drought indices have been introduced to quantify the severity of droughts, but the majority of them are incapable of demonstrating the changes in crucial drought causing elements. Internet of Things (IoT) is appropriate to monitor time-critical environmental parameters. This article proposes an energy-efficient cloud-centric system to assess the drought for the current situation and predict for the future time frame. The architecture determines the active and sleep interval of IoT sensors based on the analysis of data variability using the Bartlett test. The dimensionality of the data about drought causing elements is reduced using kernel principal component analysis (KPCA) at the fog layer. The intensity of drought is determined at the cloud layer using naïve-Bayes classifier, and drought severity for different time periods is predicted using the seasonal autoregressive integrated moving average model. Experimentation and performance analysis prove the efficiency of the proposed system in assessing and predicting the drought with better correlations with drought-causing attributes. Furthermore, it shows significant energy savings as compared to other schemes.
Amandeep Kaur and Sandeep K. Sood
Informa UK Limited
ASBTRACT Catastrophic disasters like earthquake and flood cause widespread destruction and financial devastation. This has brought disaster management into limelight making it a burgeoning academic research field. The remarkable rise of ICT (Information and Communication Technology) has instigated the scientific world to incorporate these technologies in disaster management. This study presents scientometric analysis to identify the status quo of research on the management of various disasters and role of ICT in it. This paper uses bibliographic data retrieved from Scopus for the observation period from 2011 to 2018. We provide extensive insights into growth of publications, citation pattern and their connectedness with other subject disciplines. Furthermore, we identify most productive and influential countries, institutes and journals. Our study analyses co-occurrence of keywords using Visualization of Similarities (VOS) Viewer. This structured overview will enhance the understanding of this field leading to more focussed and purposeful research.