Browsing by Author "Gungor, Vehbi Cagri"

Now showing 1 - 20 of 48

Citation - WoS: 1
Citation - Scopus: 2
Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems
(Wiley, 2025) Hacilar, Hilal; Dedeturk, Bilge Kagan; Ozmen, Mihrimah; Celik, Mehlika Eraslan; Gungor, Vehbi Cagri; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Metaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.
Citation - WoS: 17
Citation - Scopus: 25
Analysis of Compressive Sensing and Energy Harvesting for Wireless Multimedia Sensor Networks
(Elsevier, 2020) Tekin, Nazli; Gungor, Vehbi Cagri; 01. Abdullah Gül University
One of the main concerns of Wireless Multimedia Sensor Networks (WMSNs) is the huge data size causing the higher energy consumption in transmission. The high energy consumption is a critical problem for lifetime of network includes sensor nodes with limited battery. The data size reduction and Energy Harvesting (EH) methods are the promising solutions to improve the network lifetime. The main objective of this paper is to evaluate the impact of the different data size reduction methods, such as image compression and Compressive s Sensing (CS), and EH methods, such as vibration, thermal and indoor solar, on WMSNs lifetime in industrial environments. In addition, a novel Mixed Integer Programming (MIP) framework is proposed to maximize the network lifetime when EH, CS, and Error Control (EC) approaches are utilized together. Comparative performance results show that utilizing Binary Compressive Sensing (BCS) and Indoor Solar Harvester (ISH) extends industrial network lifetime significantly. (C) 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 33
Citation - Scopus: 40
Big Data Acquired by Internet of Things-Enabled Industrial Multichannel Wireless Sensors Networks for Active Monitoring and Control in the Smart Grid Industry 4.0
(Elsevier, 2021) Faheem, Muhammad; Fizza, Ghulam; Ashraf, Muhammad Waqar; Butt, Rizwan Aslam; Ngadi, Md. Asri; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Smart Grid Industry 4.0 (SGI4.0) defines a new paradigm to provide high-quality electricity at a low cost by reacting quickly and effectively to changing energy demands in the highly volatile global markets. However, in SGI4.0, the reliable and efficient gathering and transmission of the observed information from the Internet of Things (IoT)-enabled Cyberphysical systems, such as sensors located in remote places to the control center is the biggest challenge for the Industrial Multichannel Wireless Sensors Networks (IMWSNs). This is due to the harsh nature of the smart grid environment that causes high noise, signal fading, multipath effects, heat, and electromagnetic interference, which reduces the transmission quality and trigger errors in the IMWSNs. Thus, an efficient monitoring and real-time control of unexpected changes in the power generation and distribution processes is essential to guarantee the quality of service (QoS) re-quirements in the smart grid. In this context, this paper de-scribes the dataset contains measurements acquired by the IMWSNs during events monitoring and control in the smart grid. This work provides an updated detail comparison of our proposed work, including channel detection, channel assign-ment, and packets forwarding algorithms, collectively called CARP [1] with existing G-RPL [2] and EQSHC [3] schemes in the smart grid. The experimental outcomes show that the dataset and is useful for the design, development, testing, and validation of algorithms for real-time events monitoring and control applications in the smart grid. (C) 2021 The Authors. Published by Elsevier Inc.
Breast Cancer Detection Using a New Parallel Hybrid Logistic Regression Model Trained by Particle Swarm Optimization and Clonal Selection Algorithms
(Wiley, 2025) Etcil, Mustafa; Dedeturk, Bilge Kagan; Kolukisa, Burak; Bakir-Gungor, Burcu; Gungor, Vehbi Cagri; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Breast cancer is one of the most widespread kinds of cancer, especially in women, and it has a high mortality rate. With the help of technology, it is possible to develop a computer-aided method for the diagnosis of breast cancer, which is crucial for effective treatment. Recent breast cancer diagnosis studies utilizing numerous machine learning models were efficient and innovative. However, it has been observed that they may have problems such as long training times and low accuracy rates. To this end, in this study, we present a new classifier that utilizes a hybrid of the clonal selection algorithm (CSA) and the particle swarm optimization (PSO) algorithm for the training of the logistic regression (LR) model, which is named CSA-PSO-LR. The proposed method is evaluated using two publicly accessible breast cancer datasets, that is, the Wisconsin Diagnostic Breast Cancer (WDBC) database and the Wisconsin Breast Cancer Database (WBCD), with 10-fold cross-validation and Bayesian hyperparameter optimization techniques. Additionally, a CPU parallelization method is applied, which substantially shortens the training time of the model. The efficacy of the CSA-PSO-LR classifier is compared with state-of-the-art machine learning algorithms and related studies in the literature. Performance analysis indicates that the proposed method achieves 98.75% accuracy and 98.27% F1-score on the WDBC dataset, and 97.94% accuracy and 97.35% F1-score on the WBCD dataset. These results demonstrate the potential of the proposed method as an effective approach for improving breast cancer diagnosis.
Citation - WoS: 28
Citation - Scopus: 31
Capacity and Spectrum-Aware Communication Framework for Wireless Sensor Network-Based Smart Grid Applications
(Elsevier Science Bv, 2017) Faheem, Muhammad; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Recently, wireless sensor networks (WSNs) have been widely recognized as a promising technology for enhancing various aspects of smart grid and realizing the vision of next-generation electric power system in a cost-effective and efficient manner. However, recent field tests show that wireless links in smart grid environments have higher packet error rates and variable link capacity because of dynamic topology changes, obstructions, electromagnetic interference, equipment noise, multipath effects, and fading. To overcome these communication challenges, in this paper, we propose a data capacity-aware channel assignment (DCA) and fish bone routing (FBR) algorithm for WSN-based smart grid applications. The proposed DCA framework deals with the channel scarcities by dynamically switching between different spectrum bands and employs a network for organizing WSN into a highly stable connected hierarchy. In addition, the proposed FBR mechanism provides robust loop free data paths and avoids high transmission cost, excessive end-to-end delay and restricts unnecessary multi-hop data transmission from the source to destination in the network. Thus, it significantly reduces the probability of data packet loss and preserves stable link qualities among sensor nodes for load balancing and prolonging the lifetime of wireless sensor networks in harsh smart grid environments. Comparative performance evaluations show that our proposed schemes outperform the existing communication architectures in terms of data packet delivery, communication delay and energy consumption.
Citation - WoS: 42
Citation - Scopus: 51
CBI4.0: A Cross-Layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0
(Elsevier, 2021) Faheem, Muhammad; Butt, Rizwan Aslam; Ali, Rashid; Raza, Basit; Ngadi, Md Asri; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Faheem, Muhammad; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.
Citation - WoS: 1
Cognitive Radio Networks for Smart Grid Communications Potential Applications, Protocols, and Research Challenges
(CRC Press-Taylor & Francis Group, 2016) Kogias, Dimitris; Tuna, Gurkan; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Citation - WoS: 12
Citation - Scopus: 13
Collecting Smart Meter Data via Public Transportation Buses
(Inst Engineering Technology-IET, 2016) Bilgin, Bilal Erman; Baktir, Selcuk; Gungor, Vehbi Cagri; 01. Abdullah Gül University
With advances in technology, wireless sensor networks (WSNs) have found new applications and their popularity has increased dramatically. In several applications, WSNs have the potential to replace wired data communication systems, e.g. in vehicular ad hoc networks (VANETs) they are the natural option for data communication. WSNs are also proposed for data communication in the emerging smart grid. In this study, the authors merge these two application domains, i.e. VANET and smart grids, and propose a novel solution for effective smart grid data communication. The authors' proposed scheme achieves the task of collecting data from smart meters by utilising VANETs. Using network simulator-2 and with different routing protocols, the authors have performed simulations and shown the efficacy of their scheme in terms of average end-to-end delay and delivery ratio.
Citation - WoS: 2
Credit Card Fraud Detection With Machine Learning Methods
(IEEE, 2019) Goy, Gokhan; Gezer, Cengiz; Gungor, Vehbi Cagri; 01. Abdullah Gül University
With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric.
Citation - WoS: 5
Citation - Scopus: 10
Deep Learning Approaches for Vehicle Type Classification With 3D Magnetic Sensor
(Elsevier, 2022) Kolukisa, Burak; Yildirim, Veli Can; Elmas, Bahadir; Ayyildiz, Cem; Gungor, Vehbi Cagri; 01. Abdullah Gül University
In the Intelligent Transportation Systems, it is crucial to determine the type of vehicles to improve traffic management, increase human comfort, and enable future development of transport infrastructures. This paper presents a deep learning-based vehicle type classification approach for intermediate road traffic. Specifically, a low-cost, easy-to-install, battery-operated 3-D traffic sensor is designed and developed. In addition, a total of 376 vehicle samples are collected, and the vehicles are identified into three different classes according to their structures: light, medium, and heavy. Firstly, an oversampling method is applied to increase the number of samples in the training set. Then, the signals are converted into time series for LSTM and GRU and 2-D images for transfer learning models. Finally, soft voting is proposed using the LSTM, GRU, and VGG16, which is the best transfer learning method for vehicle type classification. The developed system is portable, power-limited, battery-operated, and reliable. Comparative performance results show that the soft voting ensemble method using a deep learning classifier improves the accuracy and f-measure performances by 92.92% and 93.42%, respectively. Additionally, the battery lifetime of the developed magnetic sensor node can work for up to 2 years.
Citation - Scopus: 4
Deep Learning Based Employee Attrition Prediction
(Springer International Publishing AG, 2023) Gurler, Kerem; Pak, Burcu Kuleli; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Employee attrition is a critical issue for the business sectors as leaving employees cause various types of difficulties for the company. Some studies exist on examining the reasons for this phenomenon and predicting it with Machine Learning algorithms. In this paper, the causes for employee attrition is explored in three datasets, one of them being our own novel dataset and others obtained from Kaggle. Employee attrition was predicted with multiple Machine Learning and Deep Learning algorithms with feature selection and hyperparameter optimization and their performances are evaluated with multiple metrics. Deep Learning methods showed superior performances in all of the datasets we explored. SMOTE Tomek Links were utilized to oversample minority classes and effectively tackle the problem of class imbalance. Best performing methods were Deep Random Forest on HR Dataset from Kaggle and Neural Network for IBM and Adesso datasets with F1 scores of 0.972, 0.642 and 0.853, respectively.
Citation - WoS: 4
Citation - Scopus: 5
A Deep Neural Network Approach With Hyper-Parameter Optimization for Vehicle Type Classification Using 3D Magnetic Sensor
(Elsevier, 2023) Kolukisa, Burak; Yildirim, Veli Can; Ayyildiz, Cem; Gungor, Vehbi Cagri; 01. Abdullah Gül University
The identification of vehicle types plays a critical role in Intelligent Transportation Systems. In this study, battery-operated, easy-to-install, low-cost 3-D magnetic traffic sensors have been developed for vehicle type classification problems. In addition, a new machine learning approach based on deep neural networks (DNN) with hyper-parameter optimization using feature selection and extraction methods has been proposed for vehicle type classification. A dataset is collected from the field, and vehicles are classified into three different classes, i.e., light: motorcycles, medium: passenger cars, and heavy: buses, based on vehicle structures and sizes. The proposed system is portable, energy-efficient, and reliable. The performance results show that the proposed method, which is based on a DNN classifier, has an accuracy of 91.15%, an f-measure of 91.50%, and a battery life of up to 2 years.
Citation - WoS: 5
Citation - Scopus: 3
Defect Classification of Composite Materials Using Transfer Learning Methods
(Taylor & Francis Ltd, 2025) Gulsen, Abdulkadir; Kolukisa, Burak; Ozdemir, Ahmet Turan; Bakir-Gungor, Burcu; Gungor, Vehbi Cagri; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Nowadays, composite materials have become prevalent across various sectors, particularly finding usage in large-scale applications such as spaceships, automobiles, and aircrafts. The accurate detection of the defects in these materials is crucial, yet traditional methods often rely on human inspection, which is susceptible to errors. Recent advancements in machine learning have enabled defect detection using ultrasonic non-destructive testing methods. This paper introduces a new dataset named UNDT, which is obtained from the scans of 60 different composite materials, generating a total of 1150 images depicting both defective and non-defective areas. Several transfer learning methods are applied on the newly introduced UNDT dataset as well as the publicly available USimgAIST ultrasonic dataset. Comparative performance assessments illustrate the significance of utilising the transfer learning approach for defect classification on ultrasonic inspection images. Furthermore, the research emphasises the substantial benefits of employing these transfer learning methods. Notably, the DenseNet121 and VGG19 models achieve the highest accuracy rates, with 98.8% and 98.6% on the UNDT and USimgAIST datasets, respectively.
Citation - WoS: 64
Citation - Scopus: 76
EDHRP: Energy Efficient Event Driven Hybrid Routing Protocol for Densely Deployed Wireless Sensor Networks
(Academic Press Ltd- Elsevier Science Ltd, 2015) Faheem, Muhammad; Abbas, Muhammad Zahid; Tuna, Gurkan; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Efficient management of energy resources is a challenging research area in Wireless Sensor Networks (WSNs). Recent studies have revealed that clustering is an efficient topology control approach for organizing a network into a connected hierarchy which balances the traffic load of the sensor nodes and improves the overall scalability and the lifetime of WSNs. Inspired by the advantages of clustering techniques, we have three main contributions in this paper. First, we propose an energy efficient cluster formation algorithm called Active Node Cluster Formation (ANCF). The core aim to propose ANCF algorithm is to distribute heavy data traffic and high energy consumption load evenly in the network by offering unequal size of clusters in the network. The developed scheme appoints each cluster head (CH) near to the sink and sensing event while the remaining set of the cluster heads (CHs) are appointed in the middle of each cluster to achieve the highest level of energy efficiency in dense deployment. Second, we propose a lightweight sensing mechanism called Active Node Sensing Algorithm (ANSA). The key aim to propose the ANSA algorithm is to avoid high sensing overlapping data redundancy by appointing a set of active nodes in each cluster with satisfy coverage near to the event. Third, we propose an Active Node Routing Algorithm (ANRA) to address complex inter and intra cluster routing issues in highly dense deployment based on the node dominating values. Extensive experimental studies conducted through network simulator NCTUNs 6.0 reveal that our proposed scheme outperforms existing routing techniques in terms of energy efficiency, end-to-end delay and data redundancy, congestion management and setup robustness. (C) 2015 Elsevier Ltd. All rights reserved.
Citation - WoS: 17
Citation - Scopus: 29
An Efficient Network Intrusion Detection Approach Based on Logistic Regression Model and Parallel Artificial Bee Colony Algorithm
(Elsevier, 2024) Kolukisa, Burak; Dedeturk, Bilge Kagan; Hacilar, Hilal; Gungor, Vehbi Cagri; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
In recent years, the widespread use of the Internet has created many issues, especially in the area of cybersecurity. It is critical to detect intrusions in network traffic, and researchers have developed network intrusion and anomaly detection systems to cope with high numbers of attacks and attack variations. In particular, machine learning and meta-heuristic methods have been widely used for network intrusion detection systems (NIDS). However, existing studies on these systems usually suffer from low performance results such as accuracy, F1-measure, false positive rate, and false negative rate, and generally do not use automatic parameter tuning techniques. To address these challenges, this study proposes a novel approach based on a logistic regression model trained using a parallel artificial bee colony (LR-ABC) algorithm with a hyper-parameter optimization technique. The performance of the proposed model is evaluated against state -of-the-art machine learning and deep learning models on two publicly available NIDS datasets. Comparative performance evaluations show that the proposed method achieved satisfactory results with accuracy of 88.25% on the UNSW-NB15 dataset and 90.11% on the NSL-KDD dataset, and F1-measures of 88.26% and 90.15%, respectively. These findings demonstrate the efficacy of the proposed LR-ABC model in enhancing the accuracy and reliability, while providing a scalable solution to adapt to the dynamic and evolving landscape of cybersecurity threats.
Citation - WoS: 45
Citation - Scopus: 57
Energy Efficient Multi-Objective Evolutionary Routing Scheme for Reliable Data Gathering in Internet of Underwater Acoustic Sensor Networks
(Elsevier, 2019) Faheem, Muhammad; Ngadi, Asri; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Earth's surface is covered with two-thirds of water. The marine world covers the lakes, rivers and sea and is rich in natural resources largely unexplored by human beings. Recently, underwater wireless sensor network (UWSN) with the advancement in the Internet of underwater smart things has emerged as promising networking techniques to explore the mysteries of vastly unexplored ocean environments for several underwater applications. These applications include offshore exploration, pollution monitoring, disaster prevention, oceanographic data collection, offshore oil fields monitoring, tactical surveillance applications and several others. However, the underwater channel impairments caused by multipath effects, fading, bit errors, variable and high latency and low bandwidth severely limits the data transmission reliability for UWSNs-based applications. This results in poor quality-aware data gathering in UWSNs. Therefore, designing a quality of service (QoS)-aware data gathering protocol to monitor and explore oceans is challenging in the underwater environments. In this paper, we propose a bio-inspired multi-objective evolutionary routing protocol (called MERP) for UWSNs-based applications. The designed routing protocol exploits the features of the natural evolution of the multi-objective genetic algorithm in order to provide reliable and energy-aware information gathering in UWSNs. The extensive simulation results show that the developed protocol attains its defined goals compared to existing UWSNs-based routing protocols during monitoring and exploring underwater environments. (C) 2019 Elsevier B.V. All rights reserved.
Ensemble Churn Prediction for Internet Service Provider with Machine Learning Techniques
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2020) Goy, Gokhan; Kolukisa, Burak; Bahcevan, Cenk; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; 01. Abdullah Gül University
With the developing technology in every fields, a competitive marketing environment has been arised In this competitive environment analyzing customer behavior has become vital In particular, the ability to easily change any service provider has become vet) , critical for the company to continue its existence At the same time, the amount of financial resources spent on retaining instituters much less than to obtain new clients. In this context, the traditional methods of examining vast amount of data obtained today for establishing decision support systems have lost their validities In this study. we used a dataset which is provided by TurkNet serving as an internet service provider in Turkey. Various preprocessing steps has performed on this dataset and then classification algorithms ran. Afterwards results have obtained and compared. The results of these experiments analyzed in terms of the area under the curve value In this context the aunt successful classifier algorithm has been determined as the Random Trees algorithm with a value of 0.936.
Citation - WoS: 6
Citation - Scopus: 5
Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission
(Wiley-VCH Verlag GmbH, 2024) Gulsen, Abdulkadir; Kolukisa, Burak; Caliskan, Umut; Bakir-Gungor, Burcu; Gungor, Vehbi Cagri; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Acoustic emission (AE) serves as a noninvasive technique for real-time structural health monitoring, capturing the stress waves produced by the formation and growth of cracks within a material. This study presents a novel ensemble feature selection methodology to rank features highly relevant with damage modes in AE signals gathered from edgewise compression tests on honeycomb-core carbon fiber-reinforced polymer. Two distinct features, amplitude and peak frequency, are selected for labeling the AE signals. An ensemble-supervised feature selection method ranks feature importance according to these labels. Using the ranking list, unsupervised clustering models are then applied to identify damage modes. The comparative results reveal a robust correlation between the damage modes and the features of counts and energy when amplitude is selected. Similarly, when peak frequency is chosen, a significant association is observed between the damage modes and the features of partial powers 1 and 2. These findings demonstrate that, in addition to the commonly used features, other features, such as partial powers, exhibit a correlation with damage modes. This article presents a novel ensemble feature selection methodology to rank features relevant to damage modes on acoustic emission signals in carbon fiber-reinforced polymer sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, along with common features, other features, like partial powers, have a robust correlation with damage modes.image (c) 2024 WILEY-VCH GmbH
Citation - WoS: 11
Citation - Scopus: 17
The Impact of Error Control Schemes on Lifetime of Energy Harvesting Wireless Sensor Networks in Industrial Environments
(Elsevier, 2020) Tekin, Nazli; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Due to the harsh channel conditions of the industrial environments, the data transmission over the wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of great importance for battery-limited Wireless Sensor Networks (WSNs) in industrial environments. In this paper, the lifetime analysis of error control schemes, i.e., Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ), is presented under different industrial environment channel conditions. Furthermore, the impact of energy harvesting methods on the network lifetime is investigated. A novel Mixed Integer Programming (MIP) framework is developed to maximize the network lifetime while meeting application reliability. Performance results show that utilizing HARQ-II error control scheme for Mica2 and BCH(31,21,5) for Telos improves the network lifetime while meeting the desired application reliability rate.
Citation - WoS: 1
Citation - Scopus: 1
Intelligent Traffic Light Systems Using Edge Flow Predictions
(Elsevier, 2024) Thahir, Adam Rizvi; Coskun, Mustafa; Kilic, Sultan Kubra; Gungor, Vehbi Cagri; 01. Abdullah Gül University
In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.