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86 results on '"Hussain, Shahid"'

13. Predicting Most Influential Paper Award Using Citation Count

Author

Muzammil Hussain Shahid, Fatima Sadaf, and Muhammad Arshad Islam

Subjects
Identification (information), Computer science, Time windows, Resource management, Scientific field, Citation, Funding allocation, Data science, ComputingMilieux_MISCELLANEOUS
Abstract

The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.

Published
2021

21. Optimization of Waiting Time for Electric Vehicles Using a Fuzzy Inference System.

Author

Hussain, Shahid, Kim, Yun-Su, Thakur, Subhasis, and Breslin, John G.

Abstract

Electric vehicles (EVs) need to be recharged at intermediate locations, such as shopping malls, restaurants, and parking lots, to meet the daily commute requirements of their users. Currently, public electric vehicle supply equipment (EVSE) serve EVs by conventional methods, which can result in long waiting time for users. This issue reduces the travel efficiency of EVs and thus affects user comfort. Most previous research has studied energy consumption and charging cost optimization; however, comparatively less work has focused on waiting time optimization despite its great importance from the EV user’s perspective. In this paper, we formulate the waiting time optimization as a fuzzy integer linear programming problem and propose a novel heuristic fuzzy inference system-based algorithm (FISA) that resolves the objective function and minimizes the waiting time of EVs at public EVSE installations. We developed the underlying fuzzy inference system by defining the membership functions, expert rules, and formulation for obtaining the optimal solution. The novel FISA automates the correlations of the uncertain and independent input parameters into weighted control variables and resolves the objective function in each sampling period to optimize the waiting time for EVs with the most urgent service requirements. A java language-based simulator is developed for a parking lot to evaluate the effectiveness of the proposed FISA. The simulation results indicate higher efficiency of the proposed FISA compared with state-of-art scheduling algorithms. [ABSTRACT FROM AUTHOR]

Published
2022
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25. TOC generation in PDF Document for Smart Automated Compliance Engine

Author

Muzammil Hussain Shahid and Muhammad Arshad Islam

Subjects
Information retrieval, Computer science, Cosine similarity, String (computer science), Search engine indexing, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Process (computing), Relevance (information retrieval), Table of contents, String searching algorithm, Data structure
Abstract

Portable Document Format (PDF) is a commonly used format for the scientific publication. Currently, an input document is used to test the compliance and relevance of the document or text in Automated Compliance Engines and Natural Language Processing(NLP) based system. The whole document text is used for searching the compliance rules which is computationally expensive and slow process. For speeding up the compliance checking process and making it cost efficient, this paper purposes a method based on Table of Content(TOC) Data Structure. This work proposed the PDFparser which performs Data Indexing, separate headings text, and non-heading text, create hierarchy of headings and generates TOC to reduce the semantic-based string searching time and space. Furthermore, in the NLP based system, mostly semantic-based string matching used. The proposed PDFparser uses the Cosine Similarity method for computing semantic based similarity. Our purposed method performs 47.2% better than the previous approach of searching in the non-indexed whole document and decreases the search time and space. In the worst-case scenario, where no string match found, our purposed method performs 20.5 % better.

Published
2020

26. Coronary Artery Disease Diagnosis Using Feature Selection Based Hybrid Extreme Learning Machine

Author

Afzal Hussain Shahid, Bishwajit Roy, Aashish Aadarsh, and Munindar P. Singh

Subjects
business.industry, Computer science, Feature extraction, CAD, Feature selection, Machine learning, computer.software_genre, Pearson product-moment correlation coefficient, Statistical classification, symbols.namesake, Discriminative model, Feature (computer vision), symbols, Artificial intelligence, business, computer, Extreme learning machine
Abstract

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) that cause millions of deaths worldwide due to heart failure, heart attack, and angina. The symptoms of the CAD do not appear in the early stage of the disease and it causes deadly conditions; therefore, accurate and early diagnosis of CAD is necessary to take appropriate and timely action for preventing or minimizing such conditions. Angiography, being the most accurate method for diagnosis of CAD, is often used by the clinicians to diagnose the CAD but this is an invasive procedure, costly, and may cause side effects. Therefore, researchers are trying to develop alternative diagnostic modalities for the efficient diagnosis of CAD. To that end, machine learning and data mining techniques have been widely employed. This paper proposes and develops hybrid Particle swarm optimization based Extreme learning machine (PSO-ELM) for diagnosis of CAD using the publicly available Z-Alizadeh sani dataset. To enhance the performance of the proposed model, a feature selection algorithm, namely Fisher, is used to find more discriminative feature subset. In the training period, the PSO algorithm is used to calibrate the ELM input weights and hidden biases. Further, the performance of the proposed model is compared with the basic ELM in terms of accuracy, Pearson correlation coefficient (R2) and Root mean square error (RMSE) goodness-of-fit functions. The results show that the performance of the proposed model is better than the basic ELM. The obtained CAD classification performance in terms of sensitivity, accuracy, specificity, and F1-measure is competitive to the known approaches in the literature.

Published
2020

28. Analytical Modeling, Optimization, and Electromagnetic Performance Analysis of Electrically Excited Flux Switching Motor.

Author

Khan, Bakhtiar, Khan, Faisal, Ullah, Wasiq, Hussain, Shahid, and Ullah, Basharat

Subjects
FINITE element method, STATORS, NOISE, AUTOMOTIVE transportation, COMPUTATIONAL complexity
Abstract

Flux switching motors (FSMs) with conventional stator slots have a relatively low copper slot filling factor. In this article, a new octane modular stator with pentagon-shaped slot electrically excited FSM (EEFSM) that has a comparatively higher copper slot filling factor is analyzed analytically to reduce computational complexity, high computational time, and high drive storage. The analytical results of the proposed EEFSM are validated via the finite element method. Furthermore, the initial design is optimized to improve the electromagnetic performance, and finally the prototype is fabricated and tested experimentally. There is good agreement among the analytical, finite element analysis (FEA), and experimental results with variations of 3%–5%. The average torque of the optimized design has improved by 30%, cogging torque is suppressed by 36%, and torque ripples have reduced by 38%, which have improved the performance of EEFSM in terms of low acoustic noise, low vibration, and longer motor lifetime. The proposed octane modular stator can be easily unfolded for windings maintenance, coils placement, and motor transportation in case of large-size applications. [ABSTRACT FROM AUTHOR]

Published
2022
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29. A Study on Label TSH, T3, T4U, TT4, FTI in Hyperthyroidism and Hypothyroidism using Machine Learning Techniques

Author

Afzal Hussain Shahid, Muqtadir Alam, Drakhshan Jawaid, Munindar P. Singh, Rahul Raj, and Rashmi Suman

Subjects
endocrine system, endocrine system diseases, Computer science, 030209 endocrinology & metabolism, 02 engineering and technology, Machine learning, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, Medical diagnosis, business.industry, Thyroid disease, Thyroid, medicine.disease, Random forest, Support vector machine, medicine.anatomical_structure, Computer-aided diagnosis, Thyroid hormones, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Hormone
Abstract

The thyroid hormone is produced by thyroid gland. This hormone regulates the body's metabolism. Hyperthyroidism and hypothyroidism are the two abnormalities which is caused by the release of too much or too little thyroid hormones respectively. In this study, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (K-NN) classifiers are compared to assess the efficiency of these classifiers in Thyroid disease diagnoses using the thyroid disease dataset that is taken from UCI machine learning repository. The overall classification accuracy of the RF, SVM, and K-NN are 98.50%, 97.02%, and 95.81% respectively. The result shows that the RF classifier performance is better than SVM and K-NN for the diagnosis of thyroid disease using UCI dataset.

Published
2019

46. Assist-as-Needed Control of an Intrinsically Compliant Robotic Gait Training Orthosis.

Author

Hussain, Shahid, Jamwal, Prashant K., Ghayesh, Mergen H., and Xie, Sheng Q.

Subjects
*ROBOT control systems, *ROBOT motion, *PNEUMATICS, *ADAPTIVE control systems, *MEDICAL robotics, *HUMAN-robot interaction, *TREADMILL exercise, GAIT disorder treatment
Abstract

It is a common hypothesis in the field of robot-assisted gait rehabilitation that the active involvement and voluntary participation of neurologically impaired subjects in the robotic gait training process may enhance the outcomes of such therapy. An adaptive seamless assist-as-needed (AAN) control scheme was developed for the robotic gait training. The AAN control scheme learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. The overall AAN control architecture works on the basis of a robust adaptive control approach. The performance of seamless AAN control scheme was evaluated during treadmill training with a compliant robotic orthosis having six degrees of freedom. Two experiments, namely, trajectory following experiment and the AAN experiment were carried out to evaluate the performance of seamless adaptive AAN control scheme. It was found that the robotic orthosis is capable of guiding the subjects’ limbs on reference trajectories during the trajectory following experiment. Also, a variation in robotic assistance was recorded during the AAN experiment based on the voluntary participation of human subjects. This work is an advance on the current state of the art in the compliant actuation of robotic gait rehabilitation orthoses in the context of seamless AAN gait training. [ABSTRACT FROM AUTHOR]

Published
2017
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