Plants are essential for sustaining life on Earth by serving as the primary producers of food tha... more Plants are essential for sustaining life on Earth by serving as the primary producers of food that feed humans and other heterotrophic organisms. However, this fertile environment also provides a breeding ground for parasitic organisms that cause catastrophic plant diseases, leading to a significant loss of global food production, which can range from 20% to 40% (Ahmad et al., 2023). Therefore, timely identification and early prevention of plant diseases and pests are crucial to protect agricultural productivity, food security,
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, cons... more This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.
Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. T... more Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plaininvolving only data-image conversion and classification. This shows the power of simply changing the problem space.
The rapid development in telecom has also led to an increase in fraud activities, which causes bo... more The rapid development in telecom has also led to an increase in fraud activities, which causes both revenue and reputation losses. For this reason, this paper proposes a new telecom fraud detection model based on behaviour deviations of users expressed through time-varying signatures. In line with the similarity of these deviations to known frauds, a suspect list has been created and reported to fraud experts for the final decision. The proposed model was developed with the MapReduce parallel programming paradigm, which provides simplicity and flexibility for large-scale applications. Finally, the model was applied on call detail records of a telecom company. The obtained results have shown that the proposed approach detects the telecom frauds with 86% success and is suitable for application into a fraud management system for real-world implementation.
Age estimation from brain MRI has proved to be considerably helpful in early diagnosis of disease... more Age estimation from brain MRI has proved to be considerably helpful in early diagnosis of diseases such as Alzheimer's and Parkinson's. In this study, curriculum learning effect on age estimation models was measured using a brain MRI dataset consisting of normal and anomaly data. Three different strategies were selected and compared using 3D Convolutional Neural Networks as the Deep Learning architecture. The strategies were as follows: (1) model training performed only on normal data, (2) model training performed on the entire dataset, (3) model training performed on normal data first and then further training on the entire dataset as per curriculum learning. The results showed that curriculum learning improved results by 20 % compared to traditional training strategies. These results suggested that in age estimation tasks datasets consisting of anomaly data could also be utilized to improve performance.
Medical object detection is one of the important methods used in the detection and diagnosis of d... more Medical object detection is one of the important methods used in the detection and diagnosis of diseases. In this study, a solution was sought for a bias problem that was not noticed in the testing environment from object detectionbased brain MRI studies but appeared in real life. While in the classical data processing pipeline, deep learning model training and testing are performed with only tumor data, a new pipeline in which both tumor and brain parts are labeled is proposed in this study. A state-of-the-art object detection model Mask RCNN was chosen as the deep learning model. According to the results obtained using the BraTS 2020 dataset, in the classical data processing method, specificity value and F1 Score were 0.60 and 0.80, respectively, while the proposed approach measured as 0.94 for both. So the proposed pipeline plays an essential role in reducing false positives, which we frequently encounter in reallife implementations.
One of the problems that often arise during the application of medical research to real life is t... more One of the problems that often arise during the application of medical research to real life is the high number of false positive cases. This situation causes experts to be warned with false alarms unnecessarily and increases their workload. This study proposes a new data centric approach to reduce bias-based false positive predictions in brain MRI-specific medical object detection applications. The proposed method has been tested using two different datasets: Gazi Brains 2020 and BraTS 2020, and three different deep learning-based object detection models: Mask R-CNN, YOLOv5, and EfficientDet. According to the results, the proposed pipeline outperformed the classical pipeline, up to 18% on the Gazi Brains 2020 dataset, and up to 24% on the BraTS 2020 dataset for mean specificity value without much change in sensitivity metric. It means that the proposed pipeline reduces false positive rates due to bias in real-life applications and it can help to reduce the workload of experts.
Yapay zekânın sağlık hizmetlerinin şeklini değiştirmeye başlamasıyla beraber gündeme gelen ve kli... more Yapay zekânın sağlık hizmetlerinin şeklini değiştirmeye başlamasıyla beraber gündeme gelen ve klinik araştırmalarda geliştirilen uygulamaların hayata geçirilmesinin önünde duran çeşitli zorluklar vardır. Bu zorluklar; veri seti hazırlamaktan önişleme tekniklerine, model eğitiminden modelin çıktılarının yorumlanmasına kadar model geliştirme sürecinin neredeyse her fazında problem yaratmakta hatta hatalı ve yanlı modellerin geliştirilmesine sebep olmaktadır. Bu çalışmada, yapay zekânın potansiyelinin ortaya koyulmasını engelleyen zorlukların, yapay zekâ uzmanları ve sağlık çalışanları arasındaki iş birliği ile yani veri ve deneyimin birleştirilmesiyle mümkün olacağını vurgulayan bir yol haritası sunulmuştur. Bu yol haritasındaki her adımda karşılaşılan zorluklar ve önerilen çözümler, çeşitli sağlık uygulamaları ile örneklendirilmiştir. Bu yol haritasının, uygulayıcılara farklı bakış açıları sunması, geliştirilecek modellerin performansını iyileştirmesi ve gerçek dünya uygulamalarının artışında rol oynaması beklenmektedir.
There are many articles published in driving/driver behavior (DDB) but few of them have focused o... more There are many articles published in driving/driver behavior (DDB) but few of them have focused on DDB with big data in the literature. The reasons for this might be the lack of media coverage, data, expertise or big data perspectives. This paper presents a big data perspective for investigating the DDB based on models, data features, and experiences. For this purpose, DDB studies were reviewed and grouped into six perspectives. 6V's of big data (volume, velocity, variety, veracity, vulnerability and value) were also revised and discussed how these V's were compatible with DDB data. Finally, the use of big data in DDB analysis was discussed and some suggestions were presented. The lack of big data perception on DDB research was also overviewed and a new perspective was presented for researchers, applicants or business managers to do more effective and suitable studies in the field of big data DDB.
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2021
The increase in the volume and velocity of credit card transactions causes class imbalance and co... more The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. This shows that FDIC is better than other studies in detecting fraudulent instances associated with high cost. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.
Technological developments in the energy sector while offering new business insights, also produc... more Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.
B üyük veri, her alanda büyük fırsatlar yaratarak daha önce giz-li örüntülerin keşfini kolaylaştı... more B üyük veri, her alanda büyük fırsatlar yaratarak daha önce giz-li örüntülerin keşfini kolaylaştırmakta ve kararları yönlendir-mek için öngörülerin geliştirilmesine olanak sağlamaktadır. Siber uzayın genişlemesiyle, büyük veri ve siber güvenlik ilişkisi de kuvvetlenmeye başlamıştır. Bu bölümde bu ilişki; siber güvenlik için büyük veri, siber tehdit olarak büyük veri ve büyük verinin gü-venliği başlıkları altında sınıflandırılarak değerlendirilmiştir. 13.1. Giriş Günümüzde herhangi bir yerden, herhangi bir zamandan ve her-hangi bir cihazdan gelen büyük miktarda karmaşık ve heterojen ve-riler, büyük veri çağını başlatmıştır. Büyük verinin;-depolama, yönetim ve analiz için sıradan veritabanı yazılım araçlarının yeteneklerinin gerisinde kaldığı veri kümesi,-yüksek hızda toplama, keşif ve analiz sayesinde çok çeşitli ve-rinin çok büyük hacminden ekonomik olarak değer elde etmek için tasarlanan yeni nesil teknolojiler ve mimariler,-geleneksel veri işleme uygulamaları veya genel veri tabanı yöne-tim araçları kullanılarak yönetilmesi zor olan büyük ve karmaşık veri koleksiyonu, veya-farklı formatlarda ve farklı kaynaklardan çok yüksek hızlarda üretilen büyük miktardaki veriler gibi tanımlamaları olsa da aslında güncel teknolojiler ile çözüleme-yen problemlerin çözümünde kullanılabilecek yeni nesil çözümler-dir. Genellikle, Şekil 13.1'de 5V olarak özetlenen bileşenlere sahip
Big data phenomenon is a concept for large, heterogeneous and complex data sets and having many c... more Big data phenomenon is a concept for large, heterogeneous and complex data sets and having many challenges in storing, preparing, analyzing and visualizing as well as techniques and technologies for making better decision and services. Uncovered hidden patterns, unknown or unpredicted relations and secret correlations are achieved via big data analytics. This might help companies and organizations to have new ideas, get richer and deeper insights, broaden their horizons, get advantages over their competitors, etc. To make big data analytics easy and efficient, a lot of big data techniques and technologies have been developed. In this article, the chronological development of batch, real-time and hybrid technologies, their advantages and disadvantages have been reviewed. A number of criticism have been focused on available processing techniques and technologies. This paper will be a roadmap for researchers who work on big data analytics.
The class imbalance problem, one of the common data irregularities, causes the development of und... more The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling for Imbalanced Big Data (DIBID). The design aims at modifying the existing dataset to increase the classification success. Within the study, DIBID has been implemented on public datasets under two strategies. The first strategy has been designed to present the success of the model on data sets with different imbalanced ratios. The second strategy has been designed to compare the success of the model with other imbalanced big data solutions in the literature. According to the results, DIBID outperformed other imbalanced big data solutions in the literature and increased area under the curve values between 10 % and 24 % through the case study.
Plants are essential for sustaining life on Earth by serving as the primary producers of food tha... more Plants are essential for sustaining life on Earth by serving as the primary producers of food that feed humans and other heterotrophic organisms. However, this fertile environment also provides a breeding ground for parasitic organisms that cause catastrophic plant diseases, leading to a significant loss of global food production, which can range from 20% to 40% (Ahmad et al., 2023). Therefore, timely identification and early prevention of plant diseases and pests are crucial to protect agricultural productivity, food security,
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, cons... more This survey aims to identify commonly used methods, datasets, future trends, knowledge gaps, constraints, and limitations in the field to provide an overview of current solutions used in medical image analysis in parallel with the rapid developments in transfer learning (TL). Unlike previous studies, this survey grouped the last five years of current studies for the period between January 2017 and February 2021 according to different anatomical regions and detailed the modality, medical task, TL method, source data, target data, and public or private datasets used in medical imaging. Also, it provides readers with detailed information on technical challenges, opportunities, and future research trends. In this way, an overview of recent developments is provided to help researchers to select the most effective and efficient methods and access widely used and publicly available medical datasets, research gaps, and limitations of the available literature.
Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. T... more Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plaininvolving only data-image conversion and classification. This shows the power of simply changing the problem space.
The rapid development in telecom has also led to an increase in fraud activities, which causes bo... more The rapid development in telecom has also led to an increase in fraud activities, which causes both revenue and reputation losses. For this reason, this paper proposes a new telecom fraud detection model based on behaviour deviations of users expressed through time-varying signatures. In line with the similarity of these deviations to known frauds, a suspect list has been created and reported to fraud experts for the final decision. The proposed model was developed with the MapReduce parallel programming paradigm, which provides simplicity and flexibility for large-scale applications. Finally, the model was applied on call detail records of a telecom company. The obtained results have shown that the proposed approach detects the telecom frauds with 86% success and is suitable for application into a fraud management system for real-world implementation.
Age estimation from brain MRI has proved to be considerably helpful in early diagnosis of disease... more Age estimation from brain MRI has proved to be considerably helpful in early diagnosis of diseases such as Alzheimer's and Parkinson's. In this study, curriculum learning effect on age estimation models was measured using a brain MRI dataset consisting of normal and anomaly data. Three different strategies were selected and compared using 3D Convolutional Neural Networks as the Deep Learning architecture. The strategies were as follows: (1) model training performed only on normal data, (2) model training performed on the entire dataset, (3) model training performed on normal data first and then further training on the entire dataset as per curriculum learning. The results showed that curriculum learning improved results by 20 % compared to traditional training strategies. These results suggested that in age estimation tasks datasets consisting of anomaly data could also be utilized to improve performance.
Medical object detection is one of the important methods used in the detection and diagnosis of d... more Medical object detection is one of the important methods used in the detection and diagnosis of diseases. In this study, a solution was sought for a bias problem that was not noticed in the testing environment from object detectionbased brain MRI studies but appeared in real life. While in the classical data processing pipeline, deep learning model training and testing are performed with only tumor data, a new pipeline in which both tumor and brain parts are labeled is proposed in this study. A state-of-the-art object detection model Mask RCNN was chosen as the deep learning model. According to the results obtained using the BraTS 2020 dataset, in the classical data processing method, specificity value and F1 Score were 0.60 and 0.80, respectively, while the proposed approach measured as 0.94 for both. So the proposed pipeline plays an essential role in reducing false positives, which we frequently encounter in reallife implementations.
One of the problems that often arise during the application of medical research to real life is t... more One of the problems that often arise during the application of medical research to real life is the high number of false positive cases. This situation causes experts to be warned with false alarms unnecessarily and increases their workload. This study proposes a new data centric approach to reduce bias-based false positive predictions in brain MRI-specific medical object detection applications. The proposed method has been tested using two different datasets: Gazi Brains 2020 and BraTS 2020, and three different deep learning-based object detection models: Mask R-CNN, YOLOv5, and EfficientDet. According to the results, the proposed pipeline outperformed the classical pipeline, up to 18% on the Gazi Brains 2020 dataset, and up to 24% on the BraTS 2020 dataset for mean specificity value without much change in sensitivity metric. It means that the proposed pipeline reduces false positive rates due to bias in real-life applications and it can help to reduce the workload of experts.
Yapay zekânın sağlık hizmetlerinin şeklini değiştirmeye başlamasıyla beraber gündeme gelen ve kli... more Yapay zekânın sağlık hizmetlerinin şeklini değiştirmeye başlamasıyla beraber gündeme gelen ve klinik araştırmalarda geliştirilen uygulamaların hayata geçirilmesinin önünde duran çeşitli zorluklar vardır. Bu zorluklar; veri seti hazırlamaktan önişleme tekniklerine, model eğitiminden modelin çıktılarının yorumlanmasına kadar model geliştirme sürecinin neredeyse her fazında problem yaratmakta hatta hatalı ve yanlı modellerin geliştirilmesine sebep olmaktadır. Bu çalışmada, yapay zekânın potansiyelinin ortaya koyulmasını engelleyen zorlukların, yapay zekâ uzmanları ve sağlık çalışanları arasındaki iş birliği ile yani veri ve deneyimin birleştirilmesiyle mümkün olacağını vurgulayan bir yol haritası sunulmuştur. Bu yol haritasındaki her adımda karşılaşılan zorluklar ve önerilen çözümler, çeşitli sağlık uygulamaları ile örneklendirilmiştir. Bu yol haritasının, uygulayıcılara farklı bakış açıları sunması, geliştirilecek modellerin performansını iyileştirmesi ve gerçek dünya uygulamalarının artışında rol oynaması beklenmektedir.
There are many articles published in driving/driver behavior (DDB) but few of them have focused o... more There are many articles published in driving/driver behavior (DDB) but few of them have focused on DDB with big data in the literature. The reasons for this might be the lack of media coverage, data, expertise or big data perspectives. This paper presents a big data perspective for investigating the DDB based on models, data features, and experiences. For this purpose, DDB studies were reviewed and grouped into six perspectives. 6V's of big data (volume, velocity, variety, veracity, vulnerability and value) were also revised and discussed how these V's were compatible with DDB data. Finally, the use of big data in DDB analysis was discussed and some suggestions were presented. The lack of big data perception on DDB research was also overviewed and a new perspective was presented for researchers, applicants or business managers to do more effective and suitable studies in the field of big data DDB.
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2021
The increase in the volume and velocity of credit card transactions causes class imbalance and co... more The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. This shows that FDIC is better than other studies in detecting fraudulent instances associated with high cost. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.
Technological developments in the energy sector while offering new business insights, also produc... more Technological developments in the energy sector while offering new business insights, also produces complex data. In this study, the relationship between smart grid and big data approaches have been investigated. After analyzing where the big data techniques and technologies are used in which areas of smart grid systems, the big data technologies used to detect attacks on smart grids have been focused on. Big data analytics produces efficient solutions, but it is more critical to choose which algorithm and metric. For this reason, an application prototype has been proposed using big data approaches to detect attacks on smart grids. The algorithms with high accuracy were determined as 92% with Random Forest and 87% with Decision Tree.
B üyük veri, her alanda büyük fırsatlar yaratarak daha önce giz-li örüntülerin keşfini kolaylaştı... more B üyük veri, her alanda büyük fırsatlar yaratarak daha önce giz-li örüntülerin keşfini kolaylaştırmakta ve kararları yönlendir-mek için öngörülerin geliştirilmesine olanak sağlamaktadır. Siber uzayın genişlemesiyle, büyük veri ve siber güvenlik ilişkisi de kuvvetlenmeye başlamıştır. Bu bölümde bu ilişki; siber güvenlik için büyük veri, siber tehdit olarak büyük veri ve büyük verinin gü-venliği başlıkları altında sınıflandırılarak değerlendirilmiştir. 13.1. Giriş Günümüzde herhangi bir yerden, herhangi bir zamandan ve her-hangi bir cihazdan gelen büyük miktarda karmaşık ve heterojen ve-riler, büyük veri çağını başlatmıştır. Büyük verinin;-depolama, yönetim ve analiz için sıradan veritabanı yazılım araçlarının yeteneklerinin gerisinde kaldığı veri kümesi,-yüksek hızda toplama, keşif ve analiz sayesinde çok çeşitli ve-rinin çok büyük hacminden ekonomik olarak değer elde etmek için tasarlanan yeni nesil teknolojiler ve mimariler,-geleneksel veri işleme uygulamaları veya genel veri tabanı yöne-tim araçları kullanılarak yönetilmesi zor olan büyük ve karmaşık veri koleksiyonu, veya-farklı formatlarda ve farklı kaynaklardan çok yüksek hızlarda üretilen büyük miktardaki veriler gibi tanımlamaları olsa da aslında güncel teknolojiler ile çözüleme-yen problemlerin çözümünde kullanılabilecek yeni nesil çözümler-dir. Genellikle, Şekil 13.1'de 5V olarak özetlenen bileşenlere sahip
Big data phenomenon is a concept for large, heterogeneous and complex data sets and having many c... more Big data phenomenon is a concept for large, heterogeneous and complex data sets and having many challenges in storing, preparing, analyzing and visualizing as well as techniques and technologies for making better decision and services. Uncovered hidden patterns, unknown or unpredicted relations and secret correlations are achieved via big data analytics. This might help companies and organizations to have new ideas, get richer and deeper insights, broaden their horizons, get advantages over their competitors, etc. To make big data analytics easy and efficient, a lot of big data techniques and technologies have been developed. In this article, the chronological development of batch, real-time and hybrid technologies, their advantages and disadvantages have been reviewed. A number of criticism have been focused on available processing techniques and technologies. This paper will be a roadmap for researchers who work on big data analytics.
The class imbalance problem, one of the common data irregularities, causes the development of und... more The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling for Imbalanced Big Data (DIBID). The design aims at modifying the existing dataset to increase the classification success. Within the study, DIBID has been implemented on public datasets under two strategies. The first strategy has been designed to present the success of the model on data sets with different imbalanced ratios. The second strategy has been designed to compare the success of the model with other imbalanced big data solutions in the literature. According to the results, DIBID outperformed other imbalanced big data solutions in the literature and increased area under the curve values between 10 % and 24 % through the case study.
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