Muhammad Umer Khan

Khan, Muhammad Umer

Profile URL

https://hdl.handle.net/20.500.14411/6656

Name Variants

Khan, Muhammad Umer
K.,Muhammad Umer
Muhammad Umer, Khan
Khan,Muhammad Umer
M.U.Khan
M., Khan
M.,Khan
Khan U.
Khan M.
Khan,M.U.
M. U. Khan
Umer Khan M.
K., Muhammad Umer
Muhammad Umer Khan
Khan, Umer
Khan, Muhammed Umer
Khan, M. U.
Khan, M.U

Job Title

Yardımcı Doçent

Email Address

umer.khan@atilim.edu.tr

Main Affiliation

Mechatronics Engineering

Sustainable Development Goals

2

ZERO HUNGER

4

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES

0

Research Products

14

LIFE BELOW WATER

0

Research Products

6

CLEAN WATER AND SANITATION

0

Research Products

1

NO POVERTY

0

Research Products

5

GENDER EQUALITY

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE

1

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS

0

Research Products

17

PARTNERSHIPS FOR THE GOALS

1

Research Products

15

LIFE ON LAND

0

Research Products

10

REDUCED INEQUALITIES

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY

4

Research Products

8

DECENT WORK AND ECONOMIC GROWTH

0

Research Products

4

QUALITY EDUCATION

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION

0

Research Products

3

GOOD HEALTH AND WELL-BEING

1

Research Products

13

CLIMATE ACTION

0

Research Products

Documents

37

Citations

625

h-index

13

Go to Scopus profile

Documents

30

Citations

463

Go to WoS profile

Scholarly Output

36

Articles

14

Views / Downloads

178/1580

Supervised MSc Theses

10

Supervised PhD Theses

0

WoS Citation Count

240

Scopus Citation Count

358

WoS h-index

7

Scopus h-index

8

Patents

0

Projects

0

WoS Citations per Publication

6.67

Scopus Citations per Publication

9.94

Open Access Source

10

Supervised Theses

10

Google Analytics Visitor Traffic

Journal	Count
2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018 -- 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2018 -- 2 July 2018 through 4 July 2018 -- Oulu -- 139111	2
Applied Sciences	2
2019 2nd International Conference on Communication, Computing and Digital Systems, C-CODE 2019 -- 2nd International Conference on Communication, Computing and Digital Systems, C-CODE 2019 -- 6 March 2019 through 7 March 2019 -- Islamabad -- 146997	1
2020 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 -- 7th International Conference on Electrical and Electronics Engineering, ICEEE 2020 -- 14 April 2020 through 16 April 2020 -- Antalya -- 160450	1
2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021 -- 2021 IEEE International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2021 -- 21 April 2021 through 23 April 2021 -- Virtual, Online -- 176794	1

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Scholarly Output Search Results

Now showing 1 - 10 of 13

Citation - WoS: 31
Citation - Scopus: 38
A Computationally Efficient Method for Hybrid Eeg-Fnirs Bci Based on the Pearson Correlation
(Hindawi Ltd, 2020) Hasan, Mustafa A. H.; Khan, Muhammad U.; Mishra, Deepti
A hybrid brain computer interface (BCI) system considered here is a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). EEG-fNIRS signals are simultaneously recorded to achieve high motor imagery task classification. This integration helps to achieve better system performance, but at the cost of an increase in system complexity and computational time. In hybrid BCI studies, channel selection is recognized as the key element that directly affects the system's performance. In this paper, we propose a novel channel selection approach using the Pearson product-moment correlation coefficient, where only highly correlated channels are selected from each hemisphere. Then, four different statistical features are extracted, and their different combinations are used for the classification through KNN and Tree classifiers. As far as we know, there is no report available that explored the Pearson product-moment correlation coefficient for hybrid EEG-fNIRS BCI channel selection. The results demonstrate that our hybrid system significantly reduces computational burden while achieving a classification accuracy with high reliability comparable to the existing literature.
Citation - WoS: 21
Citation - Scopus: 35
Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets
(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, Alok
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.
Citation - WoS: 5
Citation - Scopus: 4
Avoiding Contingent Incidents by Quadrotors Due To One or Two Propellers Failure
(Public Library Science, 2023) Altinuc, Kemal Orcun; Khan, Muhammad Umer; Iqbal, Jamshed
With the increasing impact of drones in our daily lives, safety issues have become a primary concern. In this study, a novel supervisor-based active fault-tolerant (FT) control system is presented for a rotary-wing quadrotor to maintain its pose in 3D space upon losing one or two propellers. Our approach allows the quadrotor to make controlled movements about a primary axis attached to the body-fixed frame. A multi-loop cascaded control architecture is designed to ensure robustness, stability, reference tracking, and safe landing. The altitude control is performed using a proportional-integral-derivative (PID) controller, whereas linear-quadratic-integral (LQI) and model-predictive-control (MPC) have been investigated for reduced attitude control and their performance is compared based on absolute and mean-squared error. The simulation results affirm that the quadrotor remains in a stable region, successfully performs the reference tracking, and ensures a safe landing while counteracting the effects of propeller(s) failures.
Citation - Scopus: 1
Investigation of Harmonic Losses To Reduce Rotor Copper Loss in Induction Motors for Traction Applications
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Siddique, M.S.; Ertan, H.B.; Alam, M.S.; Khan, M.U.
The focus of this paper is to seek means of increasing induction motor efficiency to a comparable level to a permanent magnet motor. Harmonic and high-frequency losses increase the rotor core and copper loss, often limiting IM efficiency. The research in this study focuses on reducing rotor core and copper losses for this purpose. An accurate finite element model of a prototype motor is developed. The accuracy of this model in predicting the performance and losses of the prototype motor is verified with experiments over a 32 Hz–125 Hz supply frequency range. The verified model of the motor is used to identify the causes of the rotor core and copper losses of the motor. It is found that the air gap flux density of the motor contains many harmonics, and the slot harmonics are dominant. The distribution of the core loss and the copper loss is investigated on the rotor side. It is discovered that up to 35% of the rotor copper losses and 90% rotor core losses occur in the regions up to 4 mm from the airgap where the harmonics penetrate. To reduce these losses, one solution is to reduce the magnitude of the air gap flux density harmonics. For this purpose, placing a sleeve to cover the slot openings is investigated. The FEA indicates that this measure reduces the harmonic magnitudes and reduces the core and bar losses. However, its effect on efficiency is observed to be limited. This is attributed to the penetration depth of flux density harmonics inside the rotor conductors. To remedy this problem, several FEA-based modifications to the rotor slot shape are investigated to place rotor bars deeper than the harmonic penetration. It is found that placing the bars further away from the rotor surface is very effective. Using a 1 mm sleeve across the stator’s open slots combined with a rotor tapered slot lip positions the bars slightly deeper than the major harmonic penetration depth, making it the optimal solution. This reduces the bar loss by 70% and increases the motor efficiency by 1%. Similar loss reduction is observed over the tested supply frequency range. © 2025 by the authors.
Citation - WoS: 25
Citation - Scopus: 33
Hybrid Eeg-Fnirs Bci Fusion Using Multi-Resolution Singular Value Decomposition (msvd)
(Frontiers Media Sa, 2020) Khan, Muhammad Umer; Hasan, Mustafa A. H.
Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.
Utilization of Robotics for Solar Panel Cleaning and Maintenance
(2019) Park, Haon; Öztürk, Abdullah; Park, Hajun; Khan, Muhammed Umer
In this study, a portable and low-cost solar panel cleaning robot with a functioning to blowaway dust was designed and built to incur labor costs.The robot is service effective, environmentallyfriendly, energy independent, self-automated, long durable, and cost-effective. After cleaning operation,output voltage and current in solar panel increased by 8.02% and 18.78%, respectively. Moreover, theimage processing with the photos taken by a camera fixed on the robot made classification according tocolor changes in the solar panel. Therefore, it can be concluded that the automated and multifunctionalrobot may facilitate the solar panel surface cleaning and be an applicable maintenance method throughremote monitoring of the surface conditions.
Citation - WoS: 6
Citation - Scopus: 6
Ensemble Transfer Learning Using Maizeset: a Dataset for Weed and Maize Crop Recognition at Different Growth Stages
(Elsevier Sci Ltd, 2024) Das, Zeynep Dilan; Alam, Muhammad Shahab; Khan, Muhammad Umer
Maize holds significant importance as a staple food source globally. Increasing maize yield requires the effective removal of weeds from maize fields, as they pose a detrimental threat to the growth of maize plants. In recent years, there has been a drive towards Precision Agriculture (PA), involving the integration of farming methods with artificial intelligence and advanced automation techniques. In the realm of PA, deep learning techniques present a promising solution for addressing the complex challenge of classifying maize plants and weeds. In this work, a deep learning method based on transfer learning and ensemble techniques is developed. The proposed method is implementable on any number of existing CNN models irrespective of their architecture and complexity. The developed ensemble model is trained and tested on our custom-built dataset, namely MaizeSet, comprising 3330 images of maize plants and weeds under varying environmental conditions. The performance of the ensemble model is compared against individual pre-trained VGG16 and InceptionV3 models using two experiments: the identification of weeds and maize plants, and the identification of the various vegetative growth stages of maize plants. VGG16 attained an accuracy of 83% in Experiment 1 and 71% in Experiment 2, while InceptionV3 showcased improved performance, boasting an accuracy of 98% in Experiment 1 and 81% in Experiment 2. With the proposed ensemble approach, VGG16 when combined with InceptionV3, achieved an accuracy of 90% for Experiment 1 and 80% for Experiment 2. The findings demonstrate that integrating a suboptimal pre-defined classifier, specifically VGG16, with a more proficient model like InceptionV3, yields enhanced performance across various analytical metrics. This underscores the efficacy of ensemble techniques in the context of maize classification and analogous applications within the agricultural domain.
Strawberries Maturity Level Detection Using Convolutional Neural Network (cnn) and Ensemble Method
(Computer Vision and Machine Learning in Agriculture, 2023) Daşkın, Zeynep Dilan; Khan, Muhammad Umer; İrfanoğlu, Bülent; Alam, Muhammad Shahab
Harvesting high-quality products at an affordable expense has been the prime incentive for the agriculture industry. Automation and intelligent software technology is playing a pivotal role in achieving both practical and effective solutions. In this study, we developed a robust deep learning-based vision framework to detect and classify strawberries according to their maturity levels. Due to the unavailability of the relevant dataset, we built up a novel dataset comprising 900 strawberry images to evaluate the performance of existing convolutional neural network (CNN) models under complex background conditions. The overall dataset is categorized into three classes: mature, semi-mature, and immature. The existing classifiers evaluated during this study are AlexNet, GoogleNet, SqueezeNet, DenseNet, and VGG-16. To further improve the overall prediction accuracy, two Ensemble methods are proposed based on SqueezeNet, GoogleNet, and VGG-16. Based on the considered performance matrices, SqueezeNet is recommended as the most effective model among all the classifiers and networks for detecting and classifying the maturity levels of strawberries.
A Systematic Review of Social Robots in Shopping Environments
(Taylor and Francis Ltd., 2025) Khan, M.U.; Erden, Z.
Social robots, driven by cutting-edge technologies are designed to cater to various societal needs, facilitating complex human interactions involving multiple users and stakeholders. Their integration into daily life is anticipated to increase significantly. Within this context, the domain of shopping robots, which play a crucial role in enhancing and diversifying shopping experiences where human interaction is paramount, holds immense potential for development. This article aims to provide an overview of the current landscape of shopping robots and explore future research directions in this evolving field. Through this systematic review, key trends and insights in the field of shopping robots are identified, while also offering a categorization in the form of a 3D conceptual scheme, called the Public Space Robot (PSR) framework. The outcomes reveal significant developments over the past two decades (2002–2024), with the main concentration on developing and deploying mobile robots that offer functional or autonomous interaction for navigation assistance and customer service in shopping malls and retail stores. © 2024 Taylor & Francis Group, LLC.
Mobile Robot Navigation Using Reinforcement Learning in Unknown Environments
(2019) Khan, M. U.
In mobile robotics, navigation is considered as one of the most primary tasks, which becomes more challenging during local navigation when the environment is unknown. Therefore, the robot has to explore utilizing the sensory information. Reinforcement learning (RL), a biologically-inspired learning paradigm, has caught the attention of many as it has the capability to learn autonomously in an unknown environment. However, the randomized behavior of exploration, common in RL, increases computation time and cost, hence making it less appealing for real-world scenarios. This paper proposes an informed-biased softmax regression (iBSR) learning process that introduce a heuristic-based cost function to ensure faster convergence. Here, the action-selection is not considered as a random process, rather, is based on the maximum probability function calculated using softmax regression. Through experimental simulation scenarios for navigation, the strength of the proposed approach is tested and, for comparison and analysis purposes, the iBSR learning process is evaluated against two benchmark algorithms.

Khan, Muhammad Umer

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WoS Researcher ID

Sustainable Development Goals

2

ZERO HUNGER

4

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES

0

Research Products

14

LIFE BELOW WATER

0

Research Products

6

CLEAN WATER AND SANITATION

0

Research Products

1

NO POVERTY

0

Research Products

5

GENDER EQUALITY

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE

1

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS

0

Research Products

17

PARTNERSHIPS FOR THE GOALS

1

Research Products

15

LIFE ON LAND

0

Research Products

10

REDUCED INEQUALITIES

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY

4

Research Products

8

DECENT WORK AND ECONOMIC GROWTH

0

Research Products

4

QUALITY EDUCATION

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION

0

Research Products

3

GOOD HEALTH AND WELL-BEING

1

Research Products

13

CLIMATE ACTION