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Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

While speaking anxiety is one of the most problematic aspects observed in English as a foreign language (EFL) context, a limited number of studies focused on the impact of collaborative learning on speaking anxiety in different learning environments. This study investigates the effects of collaborative tasks on foreign language learners' speaking anxiety in face-to-face and online learning contexts. In this experimental study, the data were collected from 34 foreign language students with a questionnaire consisting of a background part and a scale for measuring speaking anxiety. Even though the results revealed changes in speaking anxiety levels after the collaborative instruction in the face-to-face environments, no differences were found between face-to-face and online environments in terms of the impact of collaborative instruction on learners' speaking anxiety.

© 2022, Akadémiai Kiadó, Budapest, Hungary.Diversity of editorial boards of academic journals has recently become one of the most frequently discussed topics in scientific communication. Diversity of editorial board is considered to be an important element affecting the journal, the field and the region in terms of the number and diversity of publications. In parallel with this increasing awareness in the academic circles, many studies on diversity in the literature at both the field and journal levels have been carried out. In this study, the editorial boards of academic journals in the field of Library and Information Sciences were analyzed in terms of accommodating geographical representation, and the effect of such diversity, if any, on the number of publications was investigated. The data set consisted of the journals in the mentioned field in the SCImago web page Journal Rankings. The analyzes were carried out based on 6126 persons registered to 212 journals, including chief editors. The findings of the study showed that 75.11% of all members of editorial boards were from North American and European countries. While Asian countries (Central and South Asia, and East and Southeast Asia) were represented by 13.16% in journals in the field of Library and Information Sciences. A linear correlation was found between the regions where the chief editors of the journals were registered and the assignments of the editorial boards. Moreover, a linear correlation was found between the editors of journals (chief editors and other editors assigned to boards) and the scientific outputs (citable documents, citations, and h-index) in terms of regional relationship. The consistency of the results with those of other studies in the literature clearly demonstrates a need for more transparent processes in academic publishing. A policy to ensure a fair and diverse representation in boards of journals features numerous multifaceted positive effects.

In this article, a kinematic continuous time swarm model is considered by extending the results in the paper \"A class of attractions/repulsion functions for stable swarm aggregations\" (as shown in the International Journal of Control, vol. 77, pages 1567-1579). The extension regards a more general class of attraction/repulsion functions that can be used to explain the formation of rotational patterns in a swarm of agents. The main characteristic of this model is the introduction of two coordinate-coupling matrices weighting both the attractive and the repulsive interactions among the agents. A stability analysis is presented to characterize the swarm in terms of size and cohesiveness. Moreover, a class of attraction/repulsion functions is presented to guarantee the cohesiveness of the swarm in a polytopic region and a steady-state motion of the agents in a rotational frame around the swarm centroid. Numerical simulations are provided to illustrate the obtained results.

Two wheeled Self balancing robots have become more popular in recent times and used in many applications, commonly used in transportation as these balancing robots are faster and apparently stable. Balancing is achieved by using controllers tuned for the system. Inverted pendulum principle is used in these robots. This paper proposes the use of two control methods, namely Proportional-Integral-Derivative (PID) and Extended PID (EPID), for maintaining the balance of two-wheeled and self-balancing robots, even when slightly tilted, thus preventing them from falling. PID control theory generally depends up on transfer functions, this paper extends the theory using EPID. EPID provides a new perspective on the working principle of PID control and its relationship with other control theories, such as tracking control and disturbance observer. This approach is illustrated using the fundamental principle of inverted pendulum technology. The key advantage of these robots over other mobile robots is their increased stability due to zero turning radius, which allows for smoother rotations during changes in direction.

© 2023 Elsevier B.V.This paper studies the cluster consensus problem for networks with antagonistic interactions modelled by adjacency matrices with negative weights. By introducing an extended digraph representation consisting of purely cooperative interactions with lifting approach, we relate the trajectories of the system to those of an extended system with a positive digraph. The behaviours of agents in a signed network are extracted from its extended digraph. Consequently, the number of clusters and the cluster members are explicitly determined for any signed digraph by using primary and secondary layer subgraph concepts. The relation between the dynamics of a signed system and its extended representation is investigated for first and higher-order systems. The conditions that make both systems stable are stated. Additionally, the control parameters to achieve cluster consensus are derived explicitly for first, second and third order systems. The obtained results for continuous-time networks are subsequently extended to discrete-time networks. Finally, theoretical results are illustrated via numerical examples.

In this study, forecasting the number of immigrants on the Turkey\"s maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the biggest concerns of Turkey as unsupervised immigration can adversely affect the demographic and economic structure of the country. Precautions are needed as the short-, medium- and long-term impacts of undetected immigrants on the country\"s ecosystem are unpredictable, but due to the uncertainties inherent in immigration, the cost of using government resources such as patrol vehicles to capture undocumented immigrants can be extremely high. In order to both minimize the expenditure problem and keep immigration under control by providing a proper scan, forecasting the number of immigrants on the maritime line route is seen as an important problem and studied by probabilistic and non-probabilistic models. Since the data for 2020 and 2021 could not be attained yet due to COVID-19, in order to obtain forecasts and compare actual observations for 2019, which is the primarily focus of the research in this study, the dataset of interest on the number of daily immigrants between years 2016 and 2019 is obtained from Turkish Coast Guard Command within Ministry of Interior of Republic of Turkey. To obtain the most accurate forecasts, seven distinguished forecasting methods, from simple to complex, are implemented. Then, the forecast combination approach with meta-fuzzy functions which combines all methods is proposed. Consequently, the forecasting results are acquired and evaluated by using R. The evaluation of the results is made by using widely considered measurement accuracy metric root mean square error. According to the final assessments, the proposed approach gives more accurate forecasting results for the expected number of immigrants on the Turkey\"s maritime line and these results become an input to the national security project.

As technology scales, transistors become smaller and aggressive power optimization techniques combined with high operation frequencies and performance-enhancing microarchitectural techniques are employed to achieve increasingly higher performance and power efficiencies. Unfortunately, these developments make the modern systems more vulnerable to soft errors, which are becoming a critical issues in both hardware and software domains. Motivated by this observation, in this work, we propose, implement, and evaluate two error propagation metrics in order to characterize error propagation at both software and hardware levels. The first metric aims to measure error propagation on program data structures, whereas the second one measures the fraction of corrupted locations in the cache memory structure for a given period of time. We evaluate our proposed metrics by performing an empirical study of two application programs using both single-threaded and multi-threaded executions, and varying various experimental parameters such as thread count, error rate, location of errors, and architectural parameters. Our extensive experimental analysis reveals that error propagation over program data structures is highly dependent on application behavior.Further, depending on the cache parameters used, propagation of errors on cache can exhibit different patterns. This paper also discusses how our observed error propagation trends in program data structures and data caches are correlated with each other, focusing in particular on the differences in error propagation speeds in application data structures and data caches.

Smart retail stores started to take place in our lives. Several computer vision and sensor-based systems are working together to achieve such a complex and automated operation. Besides, the retail sector has several open and challenging problems which can be solved by embedded computer vision systems. One important problem to be tackled is shelf control or stock out detection. Here, shelves in a store should be controlled regularly such that no item is missing in the shelf. In this study, we propose an embedded computer vision system to solve this problem. To do so, we frame the shelf control operation as change detection. Due to the constraints posed by the retail sector, we formed the system by an ultra-low or low power microcontroller with an embedded camera attached to it. We provided all the implementation details of the system both from software and hardware perspectives. We also tested the proposed shelf control system from different perspectives. Hence, the reader can form such a system for retail sector or for a broad class of change detection problems in which stand-alone embedded system usage is mandatory.

© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.With the growing state/action space, learning a satisfactory policy for regular Reinforcement Learning (RL) algorithms such as flat Q-learning becomes quickly infeasible. One possible solution to handle such cases is to employ hierarchical RL (HRL). In this work, we present two methods to autonomously construct (1) skills (ASKA) and (2) arbitrarily elaborate superskills or complexes through defining an arbitrary number of hierarchies in HRL (ASKAC) over a graph-based iteratively-growing environment model. We employ dynamic community detection (DCD) in detecting subgoals since DCD considers local changes only over the partially growing graphs and lowers the time complexity of the subgoal detection where groups of environment states (i.e., subenvironments) are modeled by communities from the graph theory. DCD’s drawback is oversegmentation where it mispartitions a subenvironment further into smaller components. To maintain the robustness of ASKAC against DCD’s possible oversegmentation we introduce the concept of skill coupling. Skill coupling does not only robustly solve the oversegmentation issue, but it also improves HRL by building up more elaborate complexes (i.e., skill compositions) obtained at an arbitrary number of hierarchies and reduces the number of decisions leading to the goal employing these complexes. In addition to the experiments that investigate the effect of parameters, proposed methods are experimentally evaluated in grid world and taxi driver benchmark environments.

© 2023 John Wiley & Sons, Ltd.Redundant multithreading (RMT) is an effective thread-level replication method to improve the reliability requirements of applications. Although it significantly improves the robustness of applications, it comes with additional performance overhead since the redundant threads might share the same core resources. In our previous study [Efficient selective replication of critical code regions for SDC mitigation leveraging redundant multithreading. J Supercomput 2021;77:14130-14160], we presented an efficient software-level RMT approach, where we execute the most critical code regions with three threads to correct errors. In this study, we focus on further improving the performance of our software-level RMT method by presenting a set of different thread-to-core mapping alternatives. We provide different static mapping methods, which require preliminary information about the applications, such as execution time, instruction-per-cycle (IPC), or cache usage patterns, and a set of dynamic mapping methods, which map threads to cores dynamically at runtime without requiring any additional information. The dynamic mapping methods decide which threads are mapped to which cores at each scheduling point based on the IPC, cache miss rate, or cache access values of each thread as well as each core. Experimental results show that the dynamic mapping method, which maps threads to cores based on IPC values, outperforms all other static and dynamic methods. It also outperforms our baseline model, where the operating system handles the thread-to-core mappings by 8%, 7%, and 20% based on average speedup, harmonic speedup, and mean slowdown metrics.

In this paper, we propose an improved face recognition framework where the training is started with a small set of human annotated face images and then new images are incorporated into the training set with minimum human annotation effort. In order to minimize the human annotation effort for new images, the proposed framework combines three different strategies, namely self-paced learning (SPL), active learning (AL), and minimum sparse reconstruction (MSR). As in the recently proposed ASPL framework [1], SPL is used for automatic annotation of easy images, for which the classifiers are highly confident and AL is used to request the help of an expert for annotating difficult or low-confidence images. In this work, we propose to use MSR to subsample the low-confidence images based on diversity using minimum sparse reconstruction in order to further reduce the number of images that require human annotation. Thus, the proposed framework provides an improvement over the recently proposed ASPL framework [1] by employing MSR for eliminating “similar” images from the set selected by AL for human annotation. Experimental results on two large-scale datasets, namely CASIA-WebFace-Sub and CACD show that the proposed method called ASPL-MSR can achieve similar face recognition performance by using significantly less expert-annotated data as compared to the state-of-the-art. In particular, ASPLMSR requires manual annotation of only 36.10% and 54.10% of the data in CACD and CASIA-WebFace-Sub datasets, respectively, to achieve the same face recognition performance as the case when the whole training data is used with ground truth labels. The experimental results indicate that the number of manually annotated samples have been reduced by nearly 4% and 2% on the two datasets as compared to ASPL [1].

© 2023 John Wiley & Sons, Ltd.Approximate computing enhances the performance and energy efficiency of applications, while still achieving acceptable accuracy. Some of the multithreaded applications can tolerate the accuracy loss when critical sections are approximately executed, which in turn will eliminate the synchronization overhead of these applications and increase their performance. In this study, our objective is to explore the behavior of the critical sections and selectively skip the ones yielding performance improvements with an acceptable accuracy loss. We have observed the behavior of 62 critical sections of 4 selected applications. We have grouped them depending on their effects on program execution and skipped or approximated them accordingly. Our experimental study indicates that skipping 76% of the critical sections offers 2.5 (Formula presented.) performance gain with 16% accuracy loss for Raytrace whereas 1.4 (Formula presented.) performance improvement with 17% accuracy loss is obtained for Radiosity on average when 36% of the critical sections are skipped. For Water_NSquared the performance gain is 1.1 (Formula presented.) with 9% accuracy loss on average with 79% of critical sections skipped. For the Ocean_CP application we see a performance improvement of 1.6 (Formula presented.) with accuracy loss 1% when we skip 38% of critical sections.

© 2022 by the authors.This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

Security inks are used in areas such as money, expensive products, identity cards, passports. Generally, they are more expensive than conventional inks. A good security ink is expected to be fairly inexpensive and non-replicable. Pigments used in security ink are usually produced from rare earth elements. It is very difficult to produce ink from rare earth elements and high performance ink carrier systems are needed. There is no need for special carrier systems to produce ink from organic fluorescent pigments, and they allow cheaper ink production while reducing reproducibility. In this study, it is aimed to produce ink using organic-based fluorescent pigment. For this purpose, solvent-based ink formulations containing commercial solvent based organic fluorescent blue pigment in different ratios were prepared with polyurethane resin. The prepared inks were printed on the paper surface with the inkjet printing system. The colour properties of the prints obtained were determined under daylight and UV light. In addition, the gloss, adhesion, abrasion resistance, light fastness and drying times of the prints were determined. As a result, it has been determined that the ink produced is transparent in daylight, has a blue glow in UV light and can be used as a security ink in valuable documents.

The main concern with protecting fruits and vegetables from bacterial infection and growth is ensuring product quality and safety. Hydroxyethyl cellulose, with –OH in the natural cellulose molecule substituted by a hydroxyethyl group, has been widely used in oil exploitation, coating, medicine, food and polymerization process. It is nontoxic and low-cost. Aloe vera is a well-known herbal plant that is used for its therapeutic properties. The gel extracted from Aloe vera plants contains a variety of biologically active compounds, phenolic contents, and minerals. In this study, the edible films containing different proportions of aloe vera and hydroxyl ethyl cellulose were prepared. The structural and antibacterial properties of the obtained edible films were examined. The obtained films were printed with inkjet. Color and adhesion properties of printed samples were determined and it was observed that the edible films showed antibacterial properties.

\"Yapay Zeka uygulamaları işletmelerin dijital dönüşümleri açısından önemli biraraştırma alanı olmakla beraber ilgili alan yazında kendine pek fazla yer bulamamış,ana tartışmaların dışında kalmıştır. Ancak giderek artan çeşitlilikte uygulamalarınişletmeler tarafından kullanılması, uygulamaların giderek daha da yaygınlaşması vegüvenilirlik düzeylerinin yükselmesi ile daha çok ele alınması gerektiği görülmüştür.Bu bağlamda turizm ve seyahat bürolarının pek çok ihtiyacına cevap veren YZ uygulamalarını sınıflandırmak ve sektör bazlı değerlendirmeler yapabilmeyi hedefleyenyazımızda Raisch ve Krakowski’ye (2021)* atıfla iki yönetim uygulaması paradigmasınıele aldık. Bu iki paradigma üç farklı stratejik anlayış doğurmaktadır. Bunlar, sadeceogmantasyon odaklı YZ uygulamalarına yatırım yapmak, sadece otomasyon odaklıYZ uygulamalarına yatırım yapmak ve Her ikisine de göreceli ağırlık verecek bir yatırım stratejisi izlemek olarak sayılabilir.\"*Raisch, S., Krakowski, S. 2021. \"Artificial intelligence and management: The automation–augmentation paradox\". Academy of Management Review, 46(1), 192-210.

Veri hacmindeki artışla birlikte verinin çeşitlenmesi, özellikle gerçek zamanlı veri akışındaki hız ile birlikte verilerin doğruluğundaki belirsizlik etkenleri hayatımıza giren büyük veri kavramını doğurmuştur. Son yıllarda günlük hayatımızdaki uygulamalara girecek kadar gelişim gösteren yapay zekâ başlığı altında yer alan yöntemlerin gelişmesi, büyük veri ile birlikte kullanılarak özellikle sağlık alanında klasik çözümlere göre maliyet azaltma, zaman kazandırma, insansı hataları indirgeme gibi yararları sunabilmenin sinyallerini vermektedir. Peki yapay zekâ ile geliştirilen sistemlerin klinik çalışmalarda yer bulması ne kadar kolay olacaktır? Bu çalışma ile sorunun cevabı irdelenmekte ve elde edilen çarpıcı sonuçlar paylaşılmaktadır. The diversity of data due to the increase in its volume, the speed of real-time data flow, and the uncertainty concerning the accuracy of data gave way to the introduction of the concept of big data to the world. The development of methods under the topic of artificial intelligence, which has reached a certain point in recent years where it can be useful in our daily lives, signals its capability of offering benefits when used with big data-cost reduction, time saving, and reduction of human-like errors particularly in the field of health, compared to classical methods. So, how easy will it be for systems developed with artificial intelligence to find a place in clinical studies? In this study, the answer to this question is examined and striking results that have been obtained are shared.

© 2022 IEEE.Businesses are concerned about energy losses. Electronic gadgets have become more prevalent as a result of their adoption. The frequency with which home electricity usage data is collected has grown, allowing for sophisticated data analysis that was previously unavailable. Indeed, adopting Smart Grid (SG) networks, which are freshly improved networks of linked devices, may considerably enhance the existing energy infrastructure\"s dependability, economy, and durability. The SG involves sharing a lot of data, including information on specific users\" power use. And using this information, machine learning and deep learning algorithms may be able to detect power theft users reliably. This paper presented a Convolutional Neural Network (CNN)-based model for automated network-based vulnerability scanning that has excellent classification performance in many categories. Testing from iteration two to four iterations, this study examines research to discover the ideal configuration of the sequential model (SM) for categorization. The method is validated using a two-layer design, including an efficiency of 0.92, the whole first layer is composed of 128 nodes while the second level consists of 64 nodes. This allows for the development of a higher-level classifier for electrical signals, which may be employed in a number of applications. CNN was used to create electrical signal detectors, and SM was used to extract data from an electricity usage dataset. Furthermore, the number of features in the data set can be reduced using the Blue Monkey (BM) approach, and these results are then used to develop high-performance models. In this regard, the focus of this study has been on lowering the amount of needed features in the dataset in order to establish a rising classification algorithm for electrical signals. Experiments have applied the proposed systems\" fantastic performance, with just 666 characteristics required to combine the CNN and BM methods. Comparative to 1035 traits when CNN was used alone. This shows that the CNN and BM models are better than the CNN model in terms of lowering sufficient know while maintaining the same reliability.

© 2022 IEEE.Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.

Bu çalışmada EMG sinyalleri üzerinde öznitelikler hesaplanmıştır. Bu öznitelikler ile el hareketlerini sınıflandırmak için derin öğrenme algoritmaları kullanılmıştır. Bir zaman serisi olarak toplanan EMG sinyalleri üzerinde zaman alanında hesaplanan öznitelik vektörleri belirli boyutlarda simetrik matrisler olarak kaydedilmiştir. Yeniden oluşturulan ve resim dosyası formatında kaydedilen veri seti ile Evrişimsel Sinir Ağı eğitilmiştir. Bu eğitim sonucunda tüm veriler ile %93, test verileri ile %79 başarı ile hareket sınıflandırması gerçekleştirilmiştir. In this study, the statistical features are calculated with EMG signals. Deep learning algorithms are used to classify hand gestures with these features. the feature vectors calculated in the time domain using the EMG signals recorded as a time series are stored as symmetric matrices with specific sizes. The data set that is rebuilt and saved as image file format is used to train the convolutional neural network. Thereafter the training, motion classification is carried out with a success rate of 93% for all data and 79% for test data

İstemci sunucu sistemlerinde makine öğrenmesi modeli kullanılması bir ihtiyaçtır. Ancak istemcilerden verilerin toplanması, sunucuya aktarılması, makine öğrenmesi modeli eğitilmesi ve bu modelin istemcilerde çalışan cihazlara entegre edilmesi bir çok problemi beraberinde getirmektedir. Verilerin istemcilerden sunucuya transferi ağ trafiğine sebep olmakta, fazla enerji gerektirmekte ve veri mahremiyetini istismar edilebilmektedir. Çalışma kapsamında, bahsedilen problemlere çözüm için federe öğrenme mimarisi kullanılmaktadır. Mimariye göre, her bir istemcide istemcinin kendi verilerinden makine öğrenmesi modeli eğitilmektedir. Her bir istemcide eğitilen modeller sunucuya gönderilmekte ve sunucuda bu modeller birleştirilerek yeni bir model oluşturulmaktadır. Oluşturulan nihai model tekrar istemcilere dağıtılmaktadır. Bu çalışmada Veriden Habersiz İlişkili Kurallara Dayalı Sınıflandırma (Data Unaware Classification Based on Association, du-CBA) olarak adlandırılan ilişkisel sınıflandırma algoritması geliştirilmiştir. Federe öğrenme ile klasik öğrenme mimarilerini karşılaştırıp başarılarını ölçmek için çalışma kapsamında benzetim ortamı oluşturulmuştur. Benzetim ortamında du-CBA ve CBA algoritmaları kullanılarak modeller eğitilmiş ve sonuçlar kıyaslanmıştır. Modellerin eğitiminde University of California Irvine (UCI) veri havuzundan alınan beş veri seti kullanılmıştır. Deneysel sonuçlar, her bir veri seti için federe öğrenme ile eğitilen modellerin, klasik öğrenme ile eğitilen modellerle neredeyse aynı doğruluğu elde ettiğini ama eğitim sürelerinin yaklaşık %70 oranında azaldığını göstermiştir. Sonuçlar geliştirilen algoritmanın başarıya ulaştığını ortaya koymaktadır. It is a necessity to use machine learning model in client server systems. However, collecting data from the clients, transferring them to the server, training the machine learning model and integrating this model into the devices running on the clients bring along many problems. The transfer of data from the clients to the server causes network traffic, requires a lot of energy, and data privacy can be abused. Within the scope of the study, federated learning architecture is used to solve the mentioned problems. According to the architecture, the machine learning model is trained on each client from the client's own data. Models trained on each client are sent to the server and a new model is created by merging these models on the server. The final model created is distributed to the clients again. In this study, a relational classification algorithm called Data Unaware Classification Based on Association (du-CBA) was developed. In order to compare federated learning and classical learning architectures and measure their success, a simulation environment was created within the scope of the study. Models were trained using du-CBA and CBA algorithms in the simulation environment and the results were compared. Five data sets from the University of California Irvine (UCI) repository were used to train the models. Experimental results showed that for each dataset, the models trained with federated learning achieved almost the same accuracy as the models trained with classical learning, but the training times were decreased by about 70%. The results show that the developed algorithm has been successful.

© 2022 IEEE.Prediction of the time that will be spent on IT support tickets is very important for planning and optimization of IT support services that are usually bound with service level agreements. Predicting completion time of a ticket is a difficult problem, which requires substantial experience and technical expertise if done manually by a human. However, it is possible to automate this task using supervised machine learning models given we have a large amount of labeled data. In this study, we employ supervised machine learning algorithms to predict completion time of tickets for IT support. We use a real-world dataset that includes about 17 thousand tickets. We employ data science approaches to preprocess and transform the input and feed to supervised machine learning algorithms for learning models for ticket completion time prediction. More specifically we use Linear Regression, Decision Trees Regression, Random Forest Regression, Support Vector Machines Regression, and Multiple Regression algorithms. For the evaluation of these supervised models, we use several metrics such as MAE, MSE, and MAPE. Our results show varying success levels with different supervised machine learning algorithms for this difficult task. Among the models we train, the Decision Trees and Random Forest Regression show promising results.

Otonom araçların (OA'lar) en temel özelliği otonom olmalarıdır. Ancak aracın dinamik çalışma ortamı nedeniyle kontrol algoritmaları kesin olmayan, yaklaşık ve güvenilmez kararlar verebilir. Bu nedenle, özellikle tutarlı engellerden kaçınma algoritmaları olmak üzere daha sağlam sürüş algoritmalarının oluşturulmasına ihtiyaç vardır. Bazen, engellerden kaçınmak için aracın tamamen durması gerekir. Bu durumda aracın motor freni kontrolü devreye alınabilir. Bu çalışmada, reostatik frenleme olarak bilinen elektrikli fren sistemi ile otonom kara taşıtlarını etkin bir şekilde frenlemek için bulanık bir model önerilmiştir. Reostatik fren sistemi (RFS) kullanıldığı için, tasarlanan bu modelleme için bulanık denetleyicinin giriş değerleri araç hızı ve zemin kayganlığı, çıkış değeri ise reostat direnç değeridir. Geliştirilen bulanık denetleyicide Mamdani çıkarımı ve Toplama yöntemleri kullanılmıştır. Bu iki yönteme ek olarak, bulanık denetleyici ayrıca kullanıcıya ağırlık merkezi, açıortay, maksimumun ortalaması, maksimumun en küçüğü ve maksimumun en büyüğü keskinleştirme yöntemlerinin çıktısını verir. Son olarak, Python programlama dilini ve Tkinter kitaplığını kullanarak, grafik kullanıcı arayüzü, kullanıcının girdilerinin dilsel ifadesini ve üyelik derecesini, nihai bulanık çıktı grafiğini ve tüm durulaştırma yöntemlerinden (GUI) kesin çıktıları görüntüler., The most fundamental characteristic of autonomous vehicles (AVs) is their autonomy. However, due to the dynamic operating environment of the vehicle, their control algorithms may make imprecise, approximate, and unreliable decisions. Therefore, there is a need for the creation of more robust driving algorithms, notably consistent obstacle avoidance algorithms. Occasionally, the vehicle must come to a complete stop in order to avoid obstacles. In this situation, the engine brake control of the car can be engaged. In this study, a fuzzy model was proposed to effectively brake autonomous land vehicles, with an electrical braking system known as rheostatic braking. Since a rheostatic braking system (RBS) is employed, the input values of the fuzzy controller for this designed modeling are vehicle speed and ground slipperiness, and the output value is the rheostat resistance value. In the developed fuzzy controller, Mamdani inference and Aggregation methods were utilized. In addition to these two methods, the fuzzy controller also provides the output of the centroid, bisector, average of the maximum, smallest of the maximum and largest of the maximum sharpening methods to the user. Finally, using the Python programming language and the Tkinter library, the graphical user interface displays the linguistic expression and membership degree of the user's inputs, the final fuzzy output graph, and the exact outputs from all clarification methods (GUI).

Most studies in the literature aim to discuss and examine the technologies in smart cities. Much less is known about the dynamics of their acceptance by citizens. Therefore, we conducted a systematic literature review of studies examining the acceptance of smart city technologies (SCTs), focussing particularly on Technology User Acceptance Models (TAM). Using a novel framework of SCTs based on the technology visibility and citizen interaction, we categorise them as individual-based explicit SCTs, collective-purpose explicit SCTs, digitising SCTs, and infrastructure SCTs. We assess that studies about citizen acceptation of SCTs have been mainly focussed on individual technologies. Also, the review shows that the field is nascent and fragmented, and that a wide variety of variables have been used to predict the adoption of individual-based explicit SCTs, with the classic ones of TAM (perceived use and perceived usefulness) unmistakably being relevant in the smart city context as well. As a result of the fragmentation of the field, our review was not yet able to identify generalisable knowledge about the acceptance of the smart city and its technologies by citizens. We end with some suggestions for future research approaches and designs.

Utilizing industrial robots is an efficient method for addressing the labor crisis and advancing industrial technologies. As a result, industrial robots are becoming increasingly popular. Additionally, the widespread use of industrial robots will increase the interest in robot propulsion mechanisms. Legged robots should be primarily investigated because of their potential advantages. Among leg mechanisms, Jansen\"s linkage (JL) has gained popularity due to its organic walking motion, scalable design, and simple drive by rotary input. However, the highly nonlinear nature of JL makes its analysis challenging. The research provides a user-friendly toolbox design that visualizes the toe trajectory and simultaneously calculates the step height by performing a kinematic analysis of the JL using the user-supplied link lengths. In this way, the study contributes significantly to the design phase of legged robots and reduces the amount of time required.

© 2022 Elsevier LtdFinancial time series prediction problems, for decision-makers, are always crucial as they have a wide range of applications in the public and private sectors. This study presents a cascaded intuitionistic fuzzy model for financial time series prediction. The proposed prediction model has the ability to jointly and simultaneously model linear and nonlinear relationships in financial time series. Thus, it can adapt itself to both linear and non-linear surfaces of the data and can produce satisfactory predictions for financial time series. Moreover, the other reason why to be produced better predictions, the proposed model reckons non-membership degrees in addition to membership degrees in the prediction process. With these aspects, the proposed prediction model is different and superior to all models in the literature. This superiority has been proven by the analysis of 48 different financial time series containing TAIEX, DIJ, SSEC, and IEX data sets. The results have been evaluated in terms of RMSE, MAPE, and MdRAE metrics and some other perspectives as well. The proposed prediction model has achieved progress in prediction performance, up to 80% for TAIEX 2000–2004 datasets, 60% for TAIEX 2008–2018 datasets, approximately 50% for DJI and SSEC, and up to 70% for IEX. All the discussed indicators demonstrated the outstanding prediction performance of the proposed cascaded intuitionistic prediction model compared to some other state-of-the-art prediction tools.

The pathogenic variant E92K (c.274G > A) of the CFTR gene is rare in America and Europe, but it is common for people with cystic fibrosis from Russia and Turkey. We studied the effect of the E92K genetic variant on the CFTR function. The function of the CFTR channel was studied using the intestinal current measurements (ICM) method. The effects of CFTR modulators on the restoration of the CFTR function were studied in the model of intestinal organoids. To assess the effect of E92K on pre-mRNA splicing, the RT-PCR products obtained from patients’ intestinal organoid cultures were analyzed. Patients with the genetic variant E92K are characterized by an older age of diagnosis compared to homozygotes F508del and a high frequency of pancreatic sufficiency. The results of the sweat test and the ICM method showed partial preservation of the function of the CFTR channel. Functional analysis of CFTR gene expression revealed a weak effect of the E92K variant on mRNA-CFTR splicing. Lumacaftor (VX-809) has been shown to restore CFTR function in an intestinal organoid model, which allows us to consider the E92K variant as a promising target for therapy with CFTR correctors.

© 2022 IEEE.One of the applications of Natural Language Processing (NLP) is to process free text data for extracting information. Information extraction has various forms like Named Entity Recognition (NER) for detecting the named entities in the free text. Biomedical named-entity extraction task is about extracting named entities like drugs, diseases, organs, etc. from texts in medical domain. In our study, we improve commonly used models in this domain, such as biLSTM+CRF model, using transformer based language models like BERT and its domain-specific variant BioBERT in the embedding layer. We conduct several experiments on several different benchmark biomedical datasets using a variety of combination of models and embeddings such as BioBERT+biLSTM+CRF, BERT+biLSTM+CRF, Fasttext+biLSTM+CRF, and Graph Convolutional Networks. Our results show a quite visible, 4% to 13%, improvements when baseline biLSTM+CRF model is initialized with pretrained language models such as BERT and especially with domain specific one like BioBERT on several datasets.

© 2022 IEEE.In this study we used machine learning models for predicting individual stock price and volume changes using sentiments from public disclosures and tweets. Public Disclosure Platform (KAP) is the mandated regulatory platform for disclosing news about companies listed in Borsa Istanbul. Investors in Borsa Istanbul use Twitter to express their sentiments about stocks. By combining people\"s sentiment on Twitter and companies\" disclosures, our prediction model predicts the volume and price changes of individual company stocks listed in BIST30. Financial data regarding market conditions consisting of daily price changes of BIST30, DJI, USD, and Gold per Ounce are also added to enhance the prediction accuracy of the model. Our model achieves an maximum of 80% individual stock price prediction accuracy for companies with high social media presence and public disclosure count. We also achieve 74.7% mean volume prediction accuracy across all BIST30 companies.

© 2022 Elsevier LtdSingle nucleotide variants (SNVs) are single base substitutions that could influence many biological functions in the cell including gene expression, protein folding, and protein-protein interactions among many others. Thus, predictions of functional effects of cancer-related variants are crucial for drug responses and treatment options in clinical oncology. Experimental identification of these effects could be slow, inefficient, and inconvenient, hence in silico methods are gaining popularity in predicting the variants\" effects. There are many studies on the cancer variants, however, up to date, none of these have been aimed to assess the performance metrics of in silico pathogenicity methods on functional relevance of cancer variants obtained from ClinVar. To this end, we examined the pathogenicity predictions of cancer-related variant datasets of 8 cancer types (bladder, breast, colon, colorectal, kidney, liver, lung, and pancreas cancer) retrieved from ClinVar using 13 different in silico methods including SIFT, CADD, FATHMM-weighted, FATHMM-unweighted, GERP++, MetaSVM, Mutation Assessor, MutationTaster, MutPred, PolyPhen-2, Provean, Revel and VEST4. A combination of statistical performance metric analysis, prediction distribution frequency data and ROC curve analysis results have suggested that; among all in silico prediction tools, top three tools with the highest discriminatory power were found to be MutPred (AUC = 0.677), MetaSVM (AUC = 0.645) and Revel (AUC = 0.637).

© 2022 IEEE.Health care resources are limited and the efficient utilization of these resources is a must. No show for medical appointments is an everlasting problem that faces health care systems and is a huge waste of resources that can be a hindrance against improving health care services all over the world. This research aims to analyze the factors affecting this problem and create effective predictive models that can help solve it and reduce its social and economical ramifications on health care systems. This study suggests a methodology based on machine learning where different algorithms were utilized and compared and proposes a framework that can be utilized to achieve the best, and most robust classifier in terms of different performance metrics. The objective is to attempt to predict if a patient will attend his/her medical appointment or not.

Papers are the most commonly used substrates. A printable paper must have certain properties. These properties include surface smoothness, air permeability, surface energy, colour of the paper, opacity, whiteness, light fastness, gloss, and coefficient of elongation under force. In addition, a good printable paper should not allow ink to pass between its two surfaces. After the papers are produced, they are subjected to surface treatments in order to improve the above-mentioned properties and to provide extra specification. Surface treatments include calendering, surface sizing and coating processes. While filling the recesses and protrusions on the surface with the coating process, the paper's affinity for the ink is increased, and the penetration of the ink to the other surface of the paper is prevented. In the coating process basically, a filler is dispersed in a binder. In this study, it is aimed to obtain the highest quality and most printable paper by changing the type of binder used in the coating. In this study, equal amount (5% w/w) titanium dioxide filler was used in all coating formulations. In coating formulations; cationic starch, hydroxy ethyl cellulose and polyvinyl alcohol polymers were used as binders. Binder amounts are adjusted according to optimum viscosity. The obtained coating formulations were coated on the paper surface with a laboratory type coating device. Colour, gloss, surface smoothness, air permeability, surface energy and surface morphology of the coated papers were determined. It was printed with magenta offset printing ink on three different types of paper coated with different binders, using the IGT C1 offset printability test device, under 400 N/m2 pressure printing conditions. Colour and gloss measurements of the prints were made. As a result; It was determined that three different binders improved the printability parameters.

© 2022 IEEE.Natural Language Processing (NLP) is an interdisciplinary field between linguistics and computer science. Its main aim is to process natural (human) language using computer programs. Text classification is one of the main tasks of this field, and they are widely used in many different applications such as spam filtering, sentiment analysis, and document categorization. Nonetheless, there is only very little text classification work in the law domain and even less for the Turkish language. This may be attributed to the complexity within the domain. The length, complexity of documents, and use of extensive technical jargon are some of the reasons that distinguish this domain from others. Similar to the medical domain, understanding these documents requires extensive specialization. Another reason can be the scarcity of publicly available datasets. In this study, we compile sizeable unsupervised and supervised datasets from publicly available sources and experiment with several classification algorithms ranging from traditional classifiers to much more complicated deep learning and transformer-based models along with different text representations. We focus on classifying Court of Cassation decisions for their crime labels. Interestingly, the majority of the models we experiment with could be able to obtain good results. This suggests that although understanding the documents in the legal domain is complicated and requires expertise from humans, it may be relatively easier for machine learning models despite the extensive presence of the technical terms. This seems to be especially the case for transformer-based pre-trained neural language models which can be adapted to the law domain, showing high potential for future real-world applications.

Printed electronics are emerging technology products that we use in every moment of our daily lives. It is used in many fields from health, textile, electronics to communication. Inks with nanometal or organic content can be used in printed electronics. The ability of printed electronics to withstand temperature makes its use widespread in the electronics industry. Main aim of the study is to combine surface modified graphene oxide-based conductive inks with flame retardant materials. In this study, an effective and simple approach for the preparation of polyurethane acrylate (PUA) screen printing ink containing surface modified reduced graphene oxide (rGO) which has flame retardant activity. A new and effective flame-retardant additive; 9,10-dihydro-9,10-oxa-10-phosphaphenanthrene-10-oxide (DOPO), silane coupling agent and reduced graphene oxide was synthesized. In this synthesis, first reduced graphene oxide was modified with (methacryloyloxy)propyltrimethoxysilane, and then reacted with DOPO to obtain a flame-retardant monomer containing P and Si. Based on the successful modification reactions, screen-printing ink containing polyurethane acrylate and different amounts of modified graphene oxide content (0, 5 and 10 wt%) were prepared and screen printed on the paper surface. In addition, coatings were made on the paper surface to determine some the properties. LOI values, thermal properties, contact angle values, conductivity and surface properties of the obtained prints and coatings films were investigated. As a result, conductive screen-printing ink resistant to high temperatures was successfully produced and printed coatings and free films were formed.

© 2022 IEEE.Deep learning is one of the most significant advances in AI (AI). It is used in a variety of fields due to it has the ability to solve problems that cannot be handled by traditional technologies. The optimization of deep learning relevant to medical images is one of the most important recent advances in image analysis. Several developments have been done on Convolutional Neural Networks to achieve optimal accuracy and increase the learning speed. However, in this paper, we discuss the most recent innovations in convolutional neural networks within Classical method and Quantum method. We briefly provide a snapshot about the architecture, improvements, and principles of both (Classical and Quantum).

© 2022 IEEE.Lately, PM (permanent magnet) motors have started to be used frequently due to advantages such as high efficiency, high power density, high torque, lower losses, lower magnet costs, and more effective control with the development of power electronics. PMSM (permanent magnet synchronous motor) types and BLDCM (brushless direct current motor) types, two members of PM motors, have become frequently used motors in electric vehicles (EVs). In this study, PMSM and BLDCM are compared by optimizing magnet thicknesses, structures, and stator winding turn numbers, to improve the efficiency, using ANSYS Maxwell and ANSYS RMxprt software. In the study, surface mount magnet synchronous motor SPMSM with the inner rotor, V-shaped PM inner rotor IPMSM (interior permanent magnet synchronous motor), BLDCM with inner rotor embedded channel mount magnet, BLDCM with inner rotor embedded magnet, BLDCM with inner rotor surface mount magnet and BLDCM with outer rotor are compared.

© 2022 IEEE.Human gait phase detection has become an emerging field of study due to its impact in various clinical studies. In this study, a system is developed to detect the toe-off, mid-swing, heel-strike, and heel-off phases of a gait cycle in real-time by using a textile-based capacitive strain sensor mounted on the kneepad. Five healthy subjects performed walks including those four phases of the gait at a constant speed and gait distance in a laboratory environment while wearing the kneepad. The phases are labeled according to the gyroscope data of the Inertial Measurement Unit (IMU) located on the kneepad. An Long Short-Term Memory (LSTM) based network is utilized to detect the phases using the capacitance data obtained from the strain sensor. Recognition of four phases with 87 % accuracy is accomplished.

Increasing fossil fuel consumption and consequently the effects of greenhouse gases (GHGs) on the environment and economy are a major concern for all nations and governments. Electric vehicles (EVs) with plug-in capabilities have the potential to ease such problems. However, the extracted power from the grid for charging the EVs\" batteries will significantly impact daily power demand. To satisfy the increasing demand and ensure generation capacity adequacy, the generation expansion planning (GEP) problem is solved to determine the investment decisions for electricity generation sources. Even though there are no centralized utilities for generation planning in most markets, there is still a need to realistically solve the GEP problems and find the optimal investment decisions to tailor the incentives used by most governments to guide the market. There is also a need for a tool to analyze the effect of different charging power levels, charging policies, and penetration levels. The main goal of this paper is to provide a tool to determine realistic optimal investment plans and evaluate different cases. It is also very important to consider the stochastic nature of the electricity demand in GEP problems. We propose a scenario-based stochastic programming model to incorporate the variability in the electricity demand due to EV charging through a set of scenarios generated by Monte Carlo Simulation. The methodology starts with applying a simulation method to generate the electricity demand of EVs by considering all the possible factors affecting EVs\" demand. Each iteration of this simulation represents a possible demand profile as a result of penetrating the EVs into the market. Using all these demand profiles in GEP is preferable, but it is not computationally efficient. Computational tractability is achieved by using the clustering technique to reduce the size of such scenarios. We propose clustering methods to select a representative set from the data sets generated by the simulation and integrate EVs into GEP problems by using the selected set. The GEP models are defined to represent EVs\" demand explicitly and then solved to imply the benefit of the suggested methods. The results show that GEP models with a representative set produce more realistic solutions than the GEP models including only average EVs demand. To select representative sets, different clustering techniques and distance measurements are used and compared with respect to their performances. Two different methods are defined to choose the best number of clusters: the silhouette coefficient method and the elbow method. For each method, five different distance measurement techniques are used. In each of these techniques, three approaches are evaluated for the representative point: Min, Max, and Average. A key contribution of this article is to explore and evaluate the quality of GEP models for each case according to how close the total cost obtained from the GEP model by using clustered load curves to the total cost obtained by using the full data sets generated by simulation.

© 2022 IEEE.The aim of this research is to utilize the particle swarm approach to examine the coordinated design of the unified power flow controller (UPFC) and two power system stabilizers (PSS) in a multi-machine power system. The synchronized proposed control challenge has been presented as an optimal solution with an objective function and constraint equations to complete this goal. Period models in a multi-machine power system were used to assess the effectiveness of the proposed design under distinct operating situations. It has been shown that, even if both PSSs and UPFCs perform well in their own right, power networks stability is adversely affected by an unfavorable impact or negative interaction between controllers if they are not coordinated. Inter-Area oscillations have been efficiently dampened thanks to integrated layout.

© 2022 IEEE.In recent years, smart mobile devices have become indispensable due to the availability of office applications, the Internet, game applications, vehicle guidance or similar most of our daily lives applications in addition to traditional services such as voice calls, SMSs, and multimedia services. Due to Android\"s open source structure and easy development platforms, the number of applications on Google Play, the official Android app store increased day by day. This also brig some security related issues for the end users. The increased popularity of Android operating system on mobile devices, and the associated financial benefits attracted attackers for developing some malware for these devices, which results a significant increase in the number of Android malware applications. To detect this type of security threats, signature based detection (static detection) in generally preferred due to its easy applicability and fast identification ability. Therefore in this study it is aimed to implement an up-to-date, effective, and reliable malware detection system with the help of some deep learning algorithms. In the proposed system, RNN-based LSTM, BiLSTM and GRU algorithms are evaluated on CICInvesAndMal2019 data set which contains 8115 static features for malware detection. Experimental results show that the BiLSTM model outperforms other proposed RNN-based deep learning methods with an accuracy rate of 98.85 %.

—In previous WSN studies, it was observed that there are two common methods used in coverage area calculation: 1) image processing technique or 2) mathematical formulas. calculation of the coverage area, if the sensing ranges of the mobile nodes intersect (overlap) each other, the processing time increases while the calculation result is wrong. In this study, a new method is proposed to calculate the coverage area with relatively high accuracy, including the overlap conditions, but in a shorter time. In this method, the points that were placed as targets to be tracked in the Region of Interest (ROI) in the previous studies are now used in a different way. All covered points by any node are counted to calculate the area. However, in this case, the number of points placed in the area should be much higher than in the previous cases. Coverage performance is calculated by counting the points covered by the WSN nodes and dividing them by the total number of points defined initially. (Coverage rate) First of all, to decide the experiment’s initial conditions, 57 different cases were simulated. Different number of sensor nodes with different radii (25, 50, 100, 200) was considered and then the average of the error rate of all cases was calculated. The lowest error value was obtained in the case of the 2000x2000 area. Therefore, an area of 2000x2000 was preferred in the next steps. It can be concluded that, this method is a promising method to calculate the coverage rate in WSNs. Index Terms—Wireless Sensor Network, sensor deployment, genetic algorithm, evolutionary algorithm, coverage.

© 2022 IEEE.Reinforcement learning methods are mostly constructed on the very assumption that environments are stationary. However, most real world environments are non-stationary; that is, we assume they are composed of several stationary components (i.e., sub-environments or contexts). So, methods with this assumption are not capable of learning non-stationary environments. Reinforcement Learning - Context Detection (RL-CD) method enables the agent to learn the environment without prior information; detect the environment\"s context change points and create a partial model for each context. The underlying environment of this approach is single-agent and has shortcomings for multi-agent learning. In this study, we introduce a new approach called Multi-agent reinforcement learning-context detection (MARL-CD), which can both detect context change points and enable agents to learn non-stationary environments with multi-agent settings. This approach is based on RL-CD approach. MARL-CD is more efficient in terms of detecting context change created by the agents on the environment and detecting the context change of the environment itself. It enables an agent to detect the context changes not only from the change of environment dynamics but also from policy changes of agents in the environment. In the approach in this study, it has been shown by the experimental results that the agents spend 16% less energy and are more efficient than RL-CD in terms of detecting the change points more accurately. —Peki¸stirmeli ögrenme yakla¸sımları, ço ˘ gunlukla or- ˘ tamın duragan olması varsayımıyla etmenin ö ˘ grenmesini konu ˘ alır. Fakat, gerçek hayat uygulamalarında ortam duragan de- ˘ gildir. Birçok dura ˘ gan ortamın bir araya gelmesiyle olu¸san ˘ devingen ortamlardır. Ortamda birden fazla etmen bulunabilir ve bu etmenler de ortamı devingen hale getirmektedir. Peki¸stirmeli ö˘grenme-ba˘glam sezme (RL-CD) [1] yöntemi, etmenin devingen ortam hakkında önsel bir bilgisi olmadan ögrenmesini ˘ ve baglam de ˘ gi¸simlerinin belirlenmesini sa ˘ glayan yakla¸sımdır. ˘ Bu yakla¸sımın temelindeki ortamda tek etmen vardır ve çok etmenli ögrenim için eksiklikleri bulunmaktadır. Bu çalı¸smada ˘ çok etmenli devingen ortamlarda hem baglam de ˘ gi¸sim noktalarını ˘ sezebilen hem de etmenlerin ortamları ögrenebilmesine olanak ˘ saglayan ˘ çok etmenli peki¸stirmeli ö˘grenme-ba˘glam sezme (MARLCD) adında yeni bir yakla¸sım geli¸stirilmi¸stir. Bu yakla¸sım RLCD yöntemini temel alır. Çok etmenli ögrenmede, etmenlerin ˘ ortam üzerinde olu¸sturdukları devingenligi sezmesi ve ba ˘ glam ˘ degi¸sikli ˘ gini belirlemesi yönüyle daha verimlidir. Ba ˘ glamdaki ˘ degi¸siklikleri yalnızca ortam dinamiklerinin de ˘ gi¸siminin yanı sıra ˘ ortamdaki etmenlerin politika degi¸siklikleriyle de belirleyebilme- ˘ sini saglar. Bu çalı¸smadaki yakla¸sımda, etmenler enerjilerini %16 ˘ daha az harcayarak ve degi¸sim noktalarını daha do ˘ gru sezmesi ˘ açısından RL-CD’ye daha verimli oldugu, deney sonuçları ile ˘ gösterilmi¸stir

In scoring systems used to measure the endoscopic activity of ulcerative colitis, such as Mayo endoscopic score or Ulcerative Colitis Endoscopic Index Severity, levels increase with severity of the disease activity. Such relative ranking among the scores makes it an ordinal regression problem. On the other hand, most studies use categorical cross-entropy loss function to train deep learning models, which is not optimal for the ordinal regression problem. In this study, we propose a novel loss function, class distance weighted cross-entropy (CDW-CE), that respects the order of the classes and takes the distance of the classes into account in calculation of the cost. Experimental evaluations show that models trained with CDW-CE outperform the models trained with conventional categorical cross-entropy and other commonly used loss functions which are designed for the ordinal regression problems. In addition, the class activation maps of models trained with CDW-CE loss are more class-discriminative and they are found to be more reasonable by the domain experts.

© 2022 Authors.With the increasing importance of flexible packaging in recent years, the trend of packaging printing in the printing industry has increased. The most widely used printing systems in flexible packaging printing are flexo and rotogravure printing. Although rotogravure printing is as high quality and fast as flexo printing, it has made flexo printing more preferable because the printing preparation processes take longer and are more costly. Flexo printing is a high printing system, and it is a printing system that is widely used especially in corrugated cardboard and flexible packaging printing. In flexo printing, the main factors affecting quality are the material to be printed and its physical properties. In flexo printing, the main factors affecting quality are the material to be printed and its physical properties. In particular, the surface structure of plastic film and metallized packaging substrates with low surface energy is expected to be ink-retaining. Because the surfaces of synthetic plastic films and metallized foils have a smooth and slippery structure, preventing printing inks from sticking. Because the binder in the structure of the ink does not have a rough surface to penetrate. In order for plastic films to be printed, their surfaces must have certain properties. For this, surface improvement process called corona is applied to these material surfaces. With this process, the film material surfaces are made micro-rough and the ink adheres to the surface better and is more resistant to external factors. It should be borne in mind that the surface of a film material that has undergone a corona treatment may deteriorate over time unless it is printed on. Because the effectiveness of the corona treatment is time-limited and the surface energy inevitably decreases after a while. They are also affected by environmental conditions such as temperature and humidity. In this study, to show the importance of corona surface coating, test prints were made with flexo printing on corona treated film materials whose corona became insufficient due to time. In the examination of the microscopic images of the test prints, it was observed that the ink adhered very well and was resistant to external factors on the surfaces where the corona treatment was adequately applied. On the other hand, it was determined that the ink color intensity decreased and the print quality deteriorated in the material with insufficient corona after the corona period. As a result, in printing systems, the surface energies of the plastic films must be approximately 10 mN/m higher than the surface tension of the ink during the printing process, in order to maintain good printing quality and unchanged throughout the printing, and for this, the surface energies of the plastic films must be increased by corona surface treatment. demonstrated on test prints and microscopic images of prints.

© 2022 IEEE.It is very important to fight the Mediterranean fruit fly pest in orchards in our country. If this fight is not given, serious economic losses occur. Traps are currently being set up on trees to detect pests. It is important that these traps are checked by farmers at regular intervals in terms of pesticide applications of fruits. This process increases the workload of farmers. Therefore, a smart trap design that automatically detects the number of Mediterranean fruit flies is proposed in this study. The smart trap is implemented as an embedded system using the STM 32F746GDISCOVERY development board with an Arm Cortex M7 processor on it. Via the proposed system, quantitative data can be obtained about the population of Mediterranean fruit flies in the determined region. Machine learning is performed on the proposed embedded system. Therefore, a deep learning-based structure that can work on the embedded system has been designed. Obtained preliminary results indicate that the proposed system can be used for Mediterranean fruit fly detection and automatic determination of its number in a given region. Ülkemizde meyve bahçelerinde Akdeniz meyve sinegi˘ zararlısı ile mücadele çok önemlidir. Bu mücadelenin verilmemesi durumunda ciddi ekonomik kayıplar olu¸smaktadır. ¸Su anda zararlının tespit edilebilmesi için meyve agaçlarının üzerine tu- ˘ zaklar kurulmaktadır. Bu tuzakların düzenli aralıklarla çiftçiler tarafından kontrol edilmesi, meyvelerin ilaçlanma uygulamaları açısından önem arz etmektedir. Bu i¸slem çiftçilerin i¸s yükünü arttırmaktadır. Bu çalı¸smada, Akdeniz meyve sinegi zararlısı ˘ sayısını otomatik olarak tespit eden bir akıllı tuzak tasarımı önerilmi¸stir. Akıllı tuzak, Arm Cortex M7 i¸slemcisi barındıran STM 32F746GDISCOVERY geli¸stirme kartı kullanılarak gömülü sistem olarak gerçeklenmi¸stir. Önerilen sistem sayesinde belirlenen bölgede Akdeniz meyve sinegi zararlısının popülasyonu ˘ hakkında nicel veriler elde edilebilmektedir. Önerilen gömülü sistem üzerinde makine ögrenmesi yapılmaktadır. Bunun için gö- ˘ mülü sistem üzerinde çalı¸sabilecek derin ögrenme temelli bir yapı ˘ kurgulanmı¸stır. Elde edilen ön sonuçlar önerilen sistemin Akdeniz meyve sinegi tespiti ve belirli bir alandaki sayısının otomatik ˘ olarak bulunması için kullanılabilecegini göstermektedir.

© 2022 IEEE.Deep Learning (DL) is the most efficient technique to handle a wide range of challenging problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The development of DL has raised some privacy, justice, and national security issues. Deepfake is a DL-based application that has been very popular in recent years and is one of the reasons for these problems. Deepfake technology can create fake images and videos that are difficult for humans to recognize as real or not. Therefore, it needs to be proposed some automated methods for devices to detect and evaluate threats. In another word, digital and visual media must maintain their integrity. A set of rules used for Deepfake and some methods to detect the content created by Deepfake have been proposed in the literature. This paper summarizes what we have in the critical discussion about the problems, opportunities, and prospects of Deepfake technology. We aim for this work to be an alternative guide to getting knowledge of Deepfake detection methods. First, we cover Deepfake history and Deepfake techniques. Then, we present how a better and more robust Deepfake detection method can be designed to deal with fake content.

We can define printed electronics as the application of printing on a material by means of semiconductor, dielectric and electronic components. This process basically takes place by adding materials that will provide conductivity properties into the printing inks. In addition to printing with traditional printing systems, drop-on demand inkjet printing provides an important advantage in this field thanks to its non-contact and digital patterning capabilities. Therefore, the demand for inkjet printing printable inks based on high-performance electronics is also increasing to expand the scope of possible applications for printed electronics. In recent years, inkjet printing technology has become more and more popular due to its use in various applications such as photovoltaic cells, light-emitting diodes (LED), organic thin-thin transistors, displays, radio frequency identification devices (RFID), smart clothing and sensors. For these applications, the unique feature of inkjet printing technology is that it can print on a wide range of materials and is a digital, contactless and plateless system. We can count other advantages of this technology as low cost and savings from waste. These properties make the inkjet printing technique particularly suitable for printing conductive patterns on a variety of flexible substrates in the manufacture of electronic circuits or devices. The electrical conductivity on the printed material may vary depending on the substrate, printing speed and pressure. For this purpose, a specially prepared test scale with lines of different thicknesses was printed on the polyethylene film material with BENTSAI BT-HH6105B1 Portable Handheld Mobile inkjet printing machine. The effect of machine speed and pressure values on conductivity was observed in the prints made with silver-based conductive inks. As a result, it was concluded that the conductivity value increased as the printing pressure increased at constant speed, and on the other hand, the conductivity value increased as the printing speed decreased when the pressure was fixed.

© 2022 IEEE.Using convolutional neural networks, this thesis aims to create a system for fully automated identification of bird species based on spectrogram images. Spectrogram analysis is more difficult when trying to make an advance identification of a bird species. On a publicly available dataset of 8000 audio examples, we\"ve begun by analyzing the challenges of bird species detection, segmentation, and classification to achieve our goal. It has been determined also that deep learning-based technique CNN with Fully convolutional learning calls for easier results because it eliminates the possible future modelling error caused by an imprecise knowledge of bird species and works well on coding in cohesion with the spectral analysis kernel using the librosa library. We have concluded. After obtaining the dataset from the open-source repository, it is then processed locally. For training, testing, and validation we used a subset of the dataset of 8000 sound samples. We offered a method relying on a CNN reset learned that proved to be very quick and optimum because it was first needing the spectrogram analytic kernel to learn what to class in bird species, and then it gets the system trained on features extracted. In a novel 9-step implementation, a bird species spectrogram can be detected from an audio sample. There was a loss of less than 0.0063, and the conditioning workouts accuracy is 0.9895 for the system, 0.9 as precision, and training and validation use 50 epochs in system.

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use., This work is part of the Grand Challenges ICODA pilot initiative, delivered by Health Data Research UK and funded by the Bill & Melinda Gates Foundation and the Minderoo Foundation. The philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund; UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z and 220757/Z/20/Z]; the Bill & Melinda Gates Foundation [OPP1209135]; the National Institute for Health Research (NIHR; award CO-CIN-01); the Medical Research Council (MRC; grant MC_PC_19059); the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE)(award 200907); NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927); Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153); NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013); NIHR Clinical Research Network (infrastructure support); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and was coordinated out of Sunnybrook Research Institute; the endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014-12]; Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589]; European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; COVID clinical management team, AIIMS, Rishikesh, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; Wellcome Trust fellowship [205228/Z/16/Z]; the Liverpool School of Tropical Medicine; the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 study team; the Research Council of Norway grant no 312780; a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies, in-kind contribution; preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection; Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland; Artificial Intelligence for Pandemics (A14PAN) at University of Queensland; the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009); the Prince Charles Hospital Foundation, Australia; UK Medical Research Council Clinical Research Training Fellowship MR/V001671/1; Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship number 303953/2018-7; Firland Foundation, Shoreline, Washington, USA; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funding by the REACTing (REsearch & ACtion emergING infectious diseases) consortium and by a grant of the French Ministry of Health (PHRC n°20-0424).