This work addresses a new goal oriented navigation framework for autonomous system. In the proposed work, a hybrid control system, comprising deliberative and behavior-based architectures, has been ...developed. Deliberative layer employs a monocular vision camera to obtain the position of the goal while behavior-based framework makes use of the motor schema technique for safe navigation. Fuzzy logic is also adopted in order to enhance the performance of the navigation system. A rigorous series of experiments has been conducted using two navigation methods, which are the proposed control system and the conventional navigation technique utilizing the potential field method for achieving the desired goals. Both systems are implemented in the simulated experiments using Stage simulator. By employing these two approaches, it is possible to present a comparison of the navigation results between the systems utilizing different navigation techniques. The experimental results reveal that the proposed system produces better navigation performance compared to the conventional method in terms of safe and successful navigation, with a smoother trajectory and consistent motion.
Running control software on limited computing resources is considered one of the toughest problems. In this study, an autonomous driving software has been developed that can safely complete the map ...by tracking the lanes and avoiding obstacles on a robot vehicle with limited hardware components. The data was simplified with the image processing technique and the neural network was trained. Overfitting was prevented by hyperparameter tuning and synthetic data augmentation. In order to avoid obstacles, optical flow was calculated by detecting corners every 4 seconds and was used to find the focus of expansion of the vehicle. Time-to-collision was found with the FOE and the distance between the previous position and the current position of the detected point. Optimization was made by averaging the values of close points. The balance mechanism was created according to the TTC difference calculated on the right and left parts of the vehicle.
This survey addresses the existing state of knowledge related to vision-based mobile robots, especially including their background and history, current trends, and mapless navigation. This paper not ...only discusses studies relevant to vision-based mobile robot systems but also critically evaluates the methodologies which have been developed and that directly affect such systems.
Clouds play a pivotal role in determining the weather, impacting the daily lives of everyone. The cloud type can offer insights into whether the weather will be sunny or rainy and even serve as a ...warning for severe and stormy conditions. Classified into ten distinct classes, clouds provide valuable information about both typical and exceptional weather patterns, whether they are short or long-term in nature. This study aims to anticipate cloud formations and classify them based on their shapes and colors, allowing for preemptive measures against potentially hazardous situations. To address this challenge, a solution is proposed using image processing and deep learning technologies to classify cloud images. Several models, including MobileNet V2, Inception V3, EfficientNetV2L, VGG-16, Xception, ConvNeXtSmall, and ResNet-152 V2, were employed for the classification computations. Among them, Xception yielded the best outcome with an impressive accuracy of 97.66%. By integrating artificial intelligence technologies that can accurately detect and classify cloud types into weather forecasting systems, significant improvements in forecast accuracy can be achieved. This research presents an innovative approach to studying clouds, harnessing the power of image processing and deep learning. The ability to classify clouds based on their visual characteristics opens new avenues for enhanced weather prediction and preparedness, ultimately contributing to the overall accuracy and reliability of weather forecasts.
In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to ...protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature. The results were found to be within acceptable deviation values.
In this study, mandibular tooth plate phantoms were modelled for the molar and premolar areas and then a Monte Carlo simulation was used to calculate the Bragg curve, lateral straggle/range and recoil values of protons remaining in the therapeutic energy ranges. The mass and atomic densities of all the jawbone layers were selected and the effect of layer type and thickness on the Bragg curve, lateral straggle/range and the recoil were investigated. As protons move through different layers of density, lateral straggle and increases in the range were observed. A range of energies was used for the treatment of tumours at different depths in the mandible phantom.
Simulations revealed that as the cortical bone thickness increased, Bragg peak position decreased between 0.47-3.3%. An increase in the number of layers results in a decrease in the Bragg peak position. Finally, as the proton energy increased, the amplitude of the second peak and its effect on Bragg peak position decreased.
These findings should guide the selection of appropriate energy levels in the treatment of tumour structures without damaging surrounding tissues.
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been ...used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
Along with the rapid change of information technologies and the widespread use of the internet over time, data stacks
with ample diversity and quite large volumes has emerged. The use of data mining ...is increasing day by day due to the
huge part it plays in the acquisition of information by making necessary analyses especially within a large amount of
data. Obtaining accurate information is a key factor affecting decision-making processes. Crime data is included among
the application areas of data mining, being one of the data stacks which is rapidly growing with each passing day. Crime
events constitute unwanted behaviour in every society. For this reason, it is important to extract meaningful information
from crime data. This article aims to provide an overview of the use of data mining and machine learning in crime data
and to give a new perspective on the decision-making processes by presenting examples of the use of data mining for a
crime. For this purpose, some examples of data mining and machine learning in crime and security areas are presented
by giving a conceptual framework in the subject of big data, data mining, machine learning, and deep learning along
with task types, processes, and methods.
Sosyal paylaşım ağları günümüzde bir sosyal etkileşim mecrası olmanın ötesinde bir günlük rutin
ve yaşam biçimi haline gelmiştir. Gündelik yaşama dair birçok deneyim sosyal paylaşım ağları
...aracılığıyla yaşanmaya ve ağlarda kendine özgü ilişki biçimleri ve davranış pratikleri ortaya çıkmaya
başlamıştır. Sosyal medya ve özellikle de Facebook ve Twitter gibi geniş kullanıcıya sahip sosyal paylaşım
ağları, bugün kişi, toplum ve kültür üzerine etkileri bağlamında iletişim bilimleri başta olmak üzere
çoğu sosyal bilim disiplinin ilgi odağı konumundadır. Dünyada ve henüz sınırlı oranda Türkiye’de
gerçekleştirilen akademik çalışmalarda, sosyal paylaşım ağlarının yoğun olarak kimlik sunumu, toplumsallaşma,
toplumsal hareketler, mahremiyet sorunu, gözetim olgusu, yabancılaşma gibi kavram ve
konular çerçevesinde ele alındığı görülmektedir. Sosyal paylaşım ağlarındaki gündelik pratiklerin ve
bunların gerçek yaşamla geçişkenliğinin, içerik ve/veya kullanıcı deneyimleri ekseninde incelendiği bu
çalışmalar, toplum odaklı incelemelere duyulan ihtiyacı ve konunun sınırsız araştırma potansiyelini de
doğrudan ya da dolaylı şekilde ortaya koymaktadır. İlgili çalışmalar ve mizah dergilerinin toplumsal
gerçekliği yansıtma potansiyeli dikkate alınarak bu çalışmada, sosyal paylaşım ağlarının gündelik
yaşamdaki rolünün karikatürlerdeki temsili incelenmiştir. Mizah dergileri ve temel öğesi karikatür,
toplumu anlamak için bakılabilecek en verimli kaynaklardan biri olarak kabul edilmektedir. Bu yaklaşımdan
hareketle çalışmada, Türkiye’nin sosyal paylaşım ağı deneyiminin, toplumsal tarihin alternatif
ve özgün kaynağı karikatürler aracılığıyla okunması amaçlanmaktadır. Nitel araştırma yaklaşımına
göre tasarlanan ve Türkiye’nin en çok okunan üç mizah dergisi Leman, Penguen ve Uykusuz’un doküman
olarak seçildiği çalışmada, her üç dergide yer alan ilgili içerikteki karikatürler nitel içerik analizi
ile çözümlenmiştir.
Sosyal paylaşım ağları günümüzde bir sosyal etkileşim mecrası olmanın ötesinde bir günlük rutin ve yaşam biçimi haline gelmiştir. Gündelik yaşama dair birçok deneyim sosyal paylaşım ağları ...aracılığıyla yaşanmaya ve ağlarda kendine özgü ilişki biçimleri ve davranış pratikleri ortaya çıkmaya başlamıştır. Sosyal medya ve özellikle de Facebook ve Twitter gibi geniş kullanıcıya sahip sosyal paylaşım ağları, bugün kişi, toplum ve kültür üzerine etkileri bağlamında iletişim bilimleri başta olmak üzere çoğu sosyal bilim disiplinin ilgi odağı konumundadır. Dünyada ve henüz sınırlı oranda Türkiye’de gerçekleştirilen akademik çalışmalarda, sosyal paylaşım ağlarının yoğun olarak kimlik sunumu, toplumsallaşma, toplumsal hareketler, mahremiyet sorunu, gözetim olgusu, yabancılaşma gibi kavram ve konular çerçevesinde ele alındığı görülmektedir. Sosyal paylaşım ağlarındaki gündelik pratiklerin ve bunların gerçek yaşamla geçişkenliğinin, içerik ve/veya kullanıcı deneyimleri ekseninde incelendiği bu çalışmalar, toplum odaklı incelemelere duyulan ihtiyacı ve konunun sınırsız araştırma potansiyelini de doğrudan ya da dolaylı şekilde ortaya koymaktadır. İlgili çalışmalar ve mizah dergilerinin toplumsal gerçekliği yansıtma potansiyeli dikkate alınarak bu çalışmada, sosyal paylaşım ağlarının gündelik yaşamdaki rolünün karikatürlerdeki temsili incelenmiştir. Mizah dergileri ve temel öğesi karikatür, toplumu anlamak için bakılabilecek en verimli kaynaklardan biri olarak kabul edilmektedir. Bu yaklaşımdan hareketle çalışmada, Türkiye’nin sosyal paylaşım ağı deneyiminin, toplumsal tarihin alternatif ve özgün kaynağı karikatürler aracılığıyla okunması amaçlanmaktadır. Nitel araştırma yaklaşımına göre tasarlanan ve Türkiye’nin en çok okunan üç mizah dergisi Leman, Penguen ve Uykusuz’un doküman olarak seçildiği çalışmada, her üç dergide yer alan ilgili içerikteki karikatürler nitel içerik analizi ile çözümlenmiştir.
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