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Konuşmacıyı tanıma ya da konuşmacıyı tanımlama konuşmacının ses sinyallerine ait parametrelerinin analiz edilmesi ile otomatik olarak tanınmasıdır. İnsan sesleri sahibine çok yüksek bağlılık içerir. Bu nedenle bu çalışmada Yasin Suresini okuyan 46 farklı kişiden kim olduğunu belirlemek için Youtube üzerinden veri kümesi elde edilmiştir. Elde edilen ses dosyalarından MFCC ve LBP ile öznitelik çıkarımı yapılmıştır. Öznitelik vektörleri çeşitli sınıflandırma algoritmaları ile sınanmış ve MFCC için %35,10 başarı elde edilirken LBP için %90,74 oranında başarılı sonuçlar elde edilmiştir. Kişi doğrulama için ise LBP’de %100 sınıflandırma başarısı elde edilmiştir., Speaker recognition or speaker identification is the automatic recognition of the speaker by analyzing the parameters of the audio signals. Human voices contain a very high attachment to their owner. For this reason, in this study, a dataset was obtained from Youtube to determine who is from 46 different people who read Surah Yasin. Feature extraction was done from the obtained audio files with MFCC and LBP. Feature vectors have been tested with various classification algorithms and 35.10% success has been obtained for MFCC, while 90.74% success has been obtained for LBP. For person verification, 100% classification success was achieved in LBP.

Lung breathing sounds have been used to diagnose many diseases, including Covid-19. Nowadays, Covid-19 has affected daily life worldwide, and it has caused a global pandemic. Generally, computer vision methods have been presented to classify healthy, pneumonia, and Covid-19. They achieved high classification rates on datasets with a limited number of classes without taking into consideration other lung diseases. Our main hypothesis is to detect Covid-19 automatically among other lung diseases by using lung breathing sounds. Therefore, a dataset of lung breathing sound with ten classes has been collected, and a novel lung sounds classification method has been proposed in this paper. This method presents a novel local feature generation technique, and Substitution Box (S-Box) of the present lightweight encryption method is utilized as a pattern. A novel nonlinear pattern is presented based on S-Box, named Present-SBox-Pat (present S-Box pattern). A new pooling-based transformation (maximum tent pooling (MaTP)) is proposed to generate high, middle, and low levels features. It is considered as a preprocessing method of this work. ReliefF and iterative neighbourhood component analysis (RFINCA) selector is used to select the most discriminative and informative features. Two shallow classifiers are used to obtain results. The proposed Present-SBox-Pat and MaTP feature generation network and RFINCA feature selector-based method achieved 95.43% classification accuracy using the SVM classifier. These results demonstrated the success of techniques in generating and selecting features that facilitate the task of classifiers.

In recent years, geopolitical rivalries within the international system have been gradually increasing. It’s evident that nations that are under attack, fragmented, and militarily weak in geopolitical rivalry have no other choice than going to a war both traditionally and cognitively to survive. Military operational victories do not mean victory in cognitive warfare, and the conquest of target audiences’ mental functions is essential to win the war. With cognitive warfare planning, the mind map of the target audiences is drawn, and a suitable environment is prepared for the public to support the struggle, apart from focusing on traditional security measures. The battleground for cognitive warfare is the mindset of society, and properly planned cognitive warfare operations help overcome chaos and uncertainty. In this sense, the attack launched by Russia on Ukraine on February 24, 2022, is the scene of operations that constitute a wide range of cognitive warfare, and affects the whole world, although the outcome is still uncertain. The narratives, stories, representations and micro-mythology created in various dimensions and reflections of Ukraine’s cognitive warfare operations are closely followed by mass media all over the world. In this war, in which the non-military aspects of the RussiaUkraine War are also important, Ukraine desires to manage the perception of the international community on a large scale and tries to create behaviors that determine their identities. This article addresses the narrative strategies of Russia and Ukraine during the war that started in 2022 and continues until today; and it evaluates Ukraine’s cognitive warfare operations from various perspectives and examples.

Aim: Many predictive clinical tests are used together for preoperative detection of patients with difficult airway risk. In this study, we aimed to predict difficult intubation with different artificial intelligence algorithms using various clinical tests and anthropometric measurements, besides, to evaluate the accuracy performance of Cormack and Lehane (C-L) classification with artificial intelligence.Material and Methods: This study was conducted as a single-blind prospective observational study between 2016 and 2019. A total of 1486 patients with American Society of Anesthesiologists physical status I-III, scheduled to undergo elective surgery and requiring endotracheal intubation, were included. Demographic variables, clinical tests and anthropometric measurements of the patients were recorded. Difficult intubation was evaluated using the 4-grade C-L system according to the easy and difficult intubation criteria. Difficult intubation was tried to predict using 16 different artificial intelligence algorithms.Results: The highest success rate among artificial intelligence algorithms was obtained by the RandomForest method. With this method, difficult intubation was predicted with 92.85% sensitivity, 96.94% specificity, 93.69% positive predictive value and 96.52% negative predictive value. C-L classification accuracy performance also determined as 95.60%.Conclusion: Artificial intelligence has been considerably successful in predicting difficult intubation. Besides, C-L classifications of easy and difficult intubated patients were successfully predicted with artificial intelligence algorithms. Using a 6-grade modified C-L classification for laryngeal view may provide stronger difficult intubation prediction. A safer and more potent prediction in training artificial intelligence can be achieved by adding individual differences and clinical features that support the definition of difficult intubation., Amaç: Zor hava yolu riski olan hastaların preoperatif tespiti için birçok prediktif klinik test birlikte kullanılmaktadır. Bu çalışmada, çeşitli klinik testler ve antropometrik ölçümler kullanarak farklı yapay zekâ algoritmaları ile zor entübasyonun tahmin edilmesi, ayrıca Cormack ve Lehane (C-L) sınıflandırmasının doğruluk performansının yapay zekâ ile değerlendirilmesi amaçlanmıştır.Gereç ve Yöntemler: Bu çalışma, 2016 ve 2019 yılları arasında tek kör prospektif gözlemsel bir çalışma olarak gerçekleştirildi. Elektif cerrahi planlanan ve endotrakeal entübasyon gerektiren, Amerikan Anesteziyologlar Derneği fiziksel durumu I-III olan toplam 1486 hasta dahil edildi. Hastaların demografik değişkenleri, klinik testleri ve antropometrik ölçümleri kaydedildi. Zor entübasyon 4 dereceli C-L sistemi ile kolay ve zor entübasyon kriterlerine göre değerlendirildi. Zor entübasyon, 16 farklı yapay zekâ algoritması kullanılarak tahmin edilmeye çalışıldı.Bulgular: Yapay zekâ algoritmaları arasında en yüksek başarı oranı RandomForest yöntemi ile elde edilmiştir. Bu yöntemle zor entübasyon %92,85 duyarlılık, %96,94 özgüllük, %93,69 pozitif öngörü değeri ve%96,52 negatif öngörü değeri ile tahmin edildi. C-L sınıflandırması doğruluk performansı ise %95,60 olarak belirlendi.Sonuç: Yapay zekâ, zor entübasyonu tahmin etmede oldukça başarılı olmuştur. Ayrıca yapay zekâ algoritmaları ile kolay ve zor entübe hastaların C-L sınıflandırmaları başarıyla tahmin edilmiştir. Laringeal görünüm için 6 dereceli modifiye C-L sınıflandırması kullanmak, daha güçlü zor entübasyon tahmini sağlayabilir. Yapay zekâ eğitiminde daha güvenli ve daha güçlü bir tahmin, zor entübasyon tanımını destekleyen bireysel farklılıklar ve klinik özellikler eklenerek elde edilebilir.

Today, with the widespread use of the Internet, electronic communication tools have also been widely used. One of these tools is e-mails. E-mails are easy to use and provide the opportunity to reach thousands of people at the same time. This advantage causes some bad uses. E-mail users are faced with dozens of unsolicited mails (spam) against their will. In this study, 1017 mails collected from about 20 different Gmail and Hotmail accounts were classified as spam or regular e-mail using the algorithms in the Weka program, and the success of the algorithms was compared. In the study, 45 different algorithms were tested. The highest classification success was obtained with the NavieBayesMultinominal and NavieBayesMultinominalUpdateable algorithms with 94.7886% correct classification. Among other classifier algorithms, Trees RandomForest algorithm 93.6087%, Meta. MultiClassClassifier and Functions SGD 92.4287%, Functions SMO 91.7404%, Meta RandomCommittee 91.0521%, Bayes NavieBayes and Bayes NavieBayesUpdateable 90.3638% classification success.

Ayak görüntüleri insan vücudunun önemli bir biyolojik özelliği olup insanların çeşitli özelliklerini taşır. Ayak izindeki doku, şekil, uzunluk vb. farklı niteliklere bakılarak kişi tanımlamak mümkün olabilir. El yapısı kendine özgü şekil ve cilt dokusuna sahip olsa da bunların ayak biyometrisi ile karşılaştırılması karmaşık bir durum ortaya çıkarmaktadır. Bunun temel nedenleri arasında yakın ayak parmakları, ayak izlerindeki tipik çizgilerin yokluğu ve dönmüş ayak izlerinin yüksek gürültü içermesi yer almaktadır. Fakat bu ayrıntılar el ile benzerlik göstermese de her kişi için ayak görüntülerinde farklılık oluşmasına neden olmaktadır. Bunların yanı sıra ayak görüntüleri yaş, cinsiyet, ırk, ayakkabılar ve ayakkabı giymeye başlama yaşına göre de farklılık göstermektedir. Buradaki çalışma da 100 kişiye ait sağ ve sol ayak görüntüleri olan 6944 veri toplanmıştır. Toplanan bu dosyaların yerel faz niceleme ile öznitelikleri çıkarılmıştır. Her bir görüntü dosyası için 1x256 boyutlarında vektör üretilmiştir. Tüm dosyalar için bu işlemler yapılmış ve birçok farklı sınıflandırma algoritmaları ile görüntüler kişi, yaş ve cinsiyet için sınıflandırılmıştır. Kişi tanıma için % 99.42 oranında doğruluk oranı elde edilirken, cinsiyet için % 99.87 oranında başarı elde edilmiştir. Son olarak yaş için ise % 98.14 oranında sınıflandırma başarısına ulaşılmıştır., Foot images are an important biological feature of the human body and carry various characteristics of people. The texture in the footprint, shape, length, etc. It may be possible to identify a person by looking at different qualities. Although the hand structure has its own unique shape and skin texture, comparison of these with foot biometrics reveals a complex situation. The main reasons for this include close toes, the absence of typical lines in footprints, and the high noise content of turned footprints. However, although these details are not similar to the hand, they cause differences in foot images for each person. In addition to these, foot images also differ according to age, gender, race, shoes and age of starting to wear shoes. In this study, 6944 data, which are right and left foot images of 100 people, were collected. The features of these collected files were extracted by local phase quantization. For each image file, 1x256 vectors were produced. These processes were performed for all files and images were classified for person, age and gender with many different classification algorithms. While 99.42% accuracy rate was obtained for person recognition, 99.87% success was achieved for gender. Finally, 98.14% classification success was achieved for age. All these results show that recognition from foot images is possible with high success with the method here.

The terrorist attacks that occurred on September 11, 2001 are events that profoundly affected the national security of the United States, changed the political landscape of the world, and put terrorism on the primary agenda of United States security policy. President George W. Bush’s declaration of the War on Terror after 9/11 is an approach that changed the course of the world in global political relations. The War on Terror, a multidimensional action, took the form of a conceptual framework for national security and was an effort to establish and maintain a global coalition on the axis of expanding cooperation. In the motives required by the discursive reactions, the War on Terror has revealed a cause-effect relationship in terms of shaping the ideas and being understood by the masses, and has been described as a step that legitimizes international policies. In public diplomacy regarding the security interests of the United States, the War on Terror has become a comprehensive public diplomacy campaign against anti-Americanism. In this context, the War on Terror is a rhetorical basis for Bush administration public diplomacy. During the Bush administration, the War on Terror, which was an act of reminder for the values of the country and used to distinguish themselves from their enemies, gained importance for the creation of unity and strength across the country. In this study, the reflection of the 9/11 attacks on public diplomacy, the War on Terror strategy and the Bush Doctrine are addressed. At the same time, the rhetoric of the War on Terror in the formation of the value judgments of the masses and the implementation of the policies is examined from the perspective of public diplomacy., 11 Eylül 2001’de meydana gelen terörist saldırılar, Birleşik Devletlerin ulusal güvenliğini derinden etkileyen, dünyanın siyasi manzarasını değiştiren ve terörün Birleşik Devletler güvenlik politikasının merkezinde yer almasını sağlayan olaylardır. 11 Eylül sonrası Başkan George W. Bush’un Teröre Karşı Savaş’ı ilan etmesi, küresel siyasi ilişkilerde dünyanın gidişatını değiştiren bir yaklaşımdır. Çok boyutlu bir eylem olan Teröre Karşı Savaş, ulusal güvenlik için kavramsal bir çerçeve şeklini almış ve iş birliğinin genişletilmesi ekseninde küresel bir koalisyon kurma ve koalisyonu sürdürme çabası olmuştur. Söylemsel tepkilerin gerektirdiği güdülerde Teröre Karşı Savaş, fikirlerin şekil alması ve kitlelerce anlaşılabilmesi açısından bir sebep-sonuç ilişkisi ortaya çıkarmış ve uluslararası politikaları meşrulaştıran bir basamak olarak nitelendirilmiştir. Birleşik Devletlerin güvenlik çıkarlarına dair kamu diplomasisinde Teröre Karşı Savaş Amerikan karşıtlığına karşı kapsamlı bir kamu diplomasisi kampanyası halini almıştır. Bu bağlamda Teröre Karşı Savaş, Bush yönetimi kamu diplomasisinin retorik bir temelidir. Bush yönetiminde Birleşik Devletler açısından ülke değerleri için bir hatırlatma eylemi olan ve kendilerini düşmanlarından ayırmaya yarayan Teröre Karşı Savaş ülkede birliğin ve kuvvetin oluşturulmasına yönelik ehemmiyet kazanmıştır. Bu çalışmada, 11 Eylül saldırılarının kamu diplomasisine yansıması, Teröre Karşı Savaş stratejisi ve Bush Doktrini ele alınmaktadır. Aynı zamanda kitlelerin değer yargılarının biçimlenmesinde ve politikaların uygulanmasında Teröre Karşı Savaş retoriği kamu diplomasisi anlayışından hareketle irdelenmektedir.

Audio forensics applications and methods are very crucial to clarify crimes. To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics. An automated gunshot audios classification method is presented in this study. To implement our automated gunshot classification method, a novel gun audios dataset was collected from YouTube with 8 classes in the first phase. A novel ML method is presented in the second phase and the proposed ML method contains three fundamental phases. These phases are a novel finger pattern (finger-pat), statistical moments and discrete wavelet transform (DWT) based feature generation network, informative/distinctive feature selection with iterative ReliefF (IRF) feature selector and classification with a k nearest neighbors (kNN) classifier (shallow) to show success of the generated and selected features by using the proposed finger-pat based feature generation network and IRF feature selector. These methods and kNN achieved 94.48% classification accuracy. These results demonstrate that our proposed method can be used in gunshot audio analysis.