bluuebunny/crossref_metadata_2025 huggingface dataset:
Osmanlı Bürokrasisinde Şâkirdlik: 18. Yüzyılda Başmuhasebe Kalemi Örneği
Authors: ÇETİN Engin
Published: December 2023

Osmanlı bürokrasisinde kâtip adayı olarak çeşitli kurumlarda istihdam edilen kimselere şâkird adı verilmekteydi. Başlarda Divan-ı Hümayun çatısı altındaki dar bir kâtip kadrosunun nezaretinde istihdam edilen şâkirdler, 17. yüzyıl ortalarından itibaren merkezî bürokrasinin sacayağını oluşturan Bâbıâlî, Defterhâne ve Bâb-ı Defterî’de çalışmaya başlamışlardı. Ekseriyetle küçük yaştaki çocuklardan oluşan bu grup, Osmanlı bürokrasisinin temel personel kaynağını teşkil etmekteydi. Kurumların ihtiyaç duyduğu şâkirdlerin özellikleri, kariyer yolları ve çalışma düzenleri arasında ciddi farklılıklar olmakla birlikte genel manada şâkirdlerin pek çok ortak özelliği de bulunmaktaydı. Bugüne kadar sınırlı sayıdaki Osmanlı teşkilat tarihi çalışmalarında, araştırmacılar ister istemez imparatorluğun üst bürokrasisine odaklandıklarından şâkirdlere dair ciddi bir literatür oluşmamıştır. Bu makale söz konusu eksiklikten hareketle Osmanlı bürokrasisinde şâkirdliğin mahiyeti ele alacaktır. Ancak bütün kurumları kapsayacak bir araştırmanın şu aşamada mümkün olmaması nedeniyle örneklem olarak Başmuhasebe Kalemi, dönem olarak ise 18. yüzyıl ele alınacaktır. Bu çerçevede Başmuhasebe Kalemi şâkirdlerinin nasıl tayin edildikleri, sayıları, terfi usülleri, çalışma şartları, gelirleri ve kariyerleri ortaya konulmaya çalışılacaktır.
Türk-Alman Kültür Diplomasisi ve 1957 Tarihli Kültür Antlaşması
Authors: ÖĞRETEN Ceyhun
Published: December 2023

Ülkelerin dış politika hedeflerini gerçekleştirme yolunda izlediği yöntem ve kararlar diplomasi olarak tanımlanırken uyguladıkları yöntemlerin karşılıklı iş birliği ve kültürel ilişkiler ekseninde gelişmesi kamu diplomasisini oluşturmaktadır. Kamu diplomasisinin uygulama alanı olan kültürel diplomasi, devletlerin halkları arasında fikir, tarih, sanat ve kültür ilişkilerini kapsamakta karşılıklı kültür anlaşmaları ise kültürel ilişkileri güçlendirmektedir. hukuki zemin pekişmektedir. Bu çalışmada konu edilen Türk-Alman kültür anlaşmasının temeli Birinci Dünya Savaşı’nın ardından oluşan Avrupa bütünleşmesi fikrinin İkinci Dünya Savaşı’nın sona ermesiyle tekrar gündeme gelmesi oluşturmaktadır. Avrupa bütünleşmesi düşüncesinin somut çıktısını oluşturan Avrupa Konseyi, üye devletleri iki taraflı anlaşmalar yoluyla Avrupa kültürünü korumaya, karşılıklı medeniyet, tarih ve dillerini tetkik imkânı vermeye davet etmek düşüncesiyle Konseye üye devletlerin kültür anlaşmaları yapmalarını tavsiye etmiştir. Türkiye, Yunanistan ve İtalya ile 1951’de, Fransa’yla 1952’de, İngiltere’yle 1956’da birer kültür anlaşması imzalamıştır. Almanya ile olan kültür anlaşmasıysa Federal Almanya Cumhurbaşkanı Theodor Heuss’un Türkiye ziyareti esnasında 8 Mayıs 1957’de imzalanmıştır. Bu anlaşma sonrası her iki ülkede kültür diplomasisi bağlamında değerlendirilecek birçok adım atılmıştır. Bu çalışma, Türk-Alman Kültür anlaşması çerçevesinde Almanya’nın Türkiye’deki kültür diplomasisi faaliyetleri üzerinedir. Araştırmada alan yazını, resmi yayınlar ve arşiv belgelerinden faydalanılmıştır.
Торгово-Экономические Отношения Грузии И Армении: Современное Положение И Проблемы
Authors: MAMMADOVA Naziya
Published: December 2023

В статье проводится анализ торгово-экономических связей между Грузией и Арменией в современный период. Актуальность темы статьи обусловлено тем что после распада СССР и обретения независимости внешнеполитические приоритеты Армении и Грузии существенно отличаются: Армения является членом Евразийского экономического союза (ЕАЭС), Организации Договора о коллективной безопасности (ОДКБ) и стратегическим союзником России, а Грузия стремится вступить в Европейский союз и НАТО, а также имеет стратегическое партнерство с Азербайджаном и Турцией. Таким образом экономическая политика Армении и Грузии ориентированы на различные рынки и поставщиков энергоресурсов и сырья. По этой причине в данной научно-исследовательской работе будет рассмотрены особенности экономической политики Армении и Грузии. Объектом и предметом исследовательской работы является торгово-экономические отношения между Арменией и Грузией, современное положение и проблемы. Ввиду ограниченности объема статьи автором рассматривается в основном период после распада СССР и образования Армении и Грузии как независимых государств Южного Кавказа. При работе автором применялись метод экономического анализа, метод сравнения и историко-аналитический метод.
К Вопросу О Парадигмах Истории Гарабагcкого Ханства В Современной Историографии
Authors: ISGENDEROVA Marziya
Published: December 2023

В статье дается историографический анализ вопросов значимости, завоевания и ликвидации Гарабагского ханства в работах современных исследователей. Стратегическое положение Гарабагского ханства и наличие на его территории пяти меликств способствовали обоснованию в современной историографии как места Гарабагского ханства в южнокавказской политике царизма, так и разнообразия способов реализации захватнических планов России в отношении Гарабагского ханства. Современные историки показывают первостепенность Гарабагского ханства в претворении идеи царизма о создании буферного государства в качестве щита против Османского и Гаджарского государств. Этнодемографические изменения в Гарабаге вследствие переселенческой политики царизма, также свидетельствующие о стратегической значимости данного региона для России, позволили пронаблюдать общность мнения у большинства исследователей в отношении целей этой политики, направленной на увеличение численности армян в Гарабаге с целью создать здесь свою христианскую опору.

В свою очередь, рассматриваемые в статье труды современных историков выявили новый, свободный от конъюктурных концепций подход к данной теме и довели суть политики России в Гарабагском ханстве, что для завоевания этого региона, постоянно остававшегося в центре внимания России, как и Южного Кавказа, в целом, она использовала все доступные ей методы.

Выяснение в новом свете ряда аспектов изучаемой проблемы может стать еще одной ступенью в разбирательстве длившегося столетиями азербайджано-армянского конфликта. Отсюда, и весомость лепты, внесенной этими современными исследователями в изучение места Гарабагского ханства в системе азербайджано-российских отношений указанного периода.

Происходящие сегодня межгосударственные и межэтнические конфликты в Южном Кавказе, в частности, в Гарабаге, носят в себе отпечаток прошлого, и разработка авторами настоящей темы является не только актуальной, но и показывает необоснованность притязаний Армении на Гарабаг. В свою очередь, эти работы способствуют углублению информации о проблеме Гарабага за пределами Азербайджана.
Cami Kütüphane Vakfiyelerinden Nizamnamelere Kitapları Koruma Yöntemleri
Authors: DİKER Sevgi
Published: December 2023

Halk kütüphanesi olarak çalışan Osmanlı cami kütüphanelerinin vakfiyelerinin çoğu günümüze ulaşmıştır. Kitap vâkıfları tahmin edilenden daha fazladır. İlk dönemlerdeki sıklıkla camilere, tekke ve zaviyelere kurulan birkaç dolaplık kütüphanelerin yerini zamanla müstakil kütüphaneler almıştır. Vâkıfın kitap listesini, kimlere ne şartlarda hizmet verileceğini, teşkilatlanmasını belirleyen bu vakfiyeler, kitaplarının bakımsızlıktan, yırtılmaktan ve hırsızlıktan korunmasını sağlayacak maddeleri de kapsar. Kitap vâkıflarının en önemli sorunu kitaplarının bekası olduğundan vakfiyelerde kitapları korumak için birçok önlem alınmıştır. Ancak 19. yüzyıla gelindiğinde atıl kalan cami kütüphanelerindeki kitaplar hızla eksilmeye başlar. Yurtiçinde ve yurtdışında satılmaya başlayan vakıf kitaplarını korumakla ilgili ilk önlemler Evkaf-ı Hümayun Nezareti tarafından alınmaya çalışılmıştır. Vakıf kitapları yurtdışına kaçırılıyorsa da ilk Asar-ı Atika Nizamnâmelerinde koruyucu maddeler yer almaz. Vakıf kitapların tespit ve kayıt çalışmalarının başlaması koleksiyonları bilinir hale getirmiş, Evkâf-ı İslâmiye Müzesi bir kısmını kurtarmıştır. Bu çalışmada cami kütüphanelerine, vakfiyelerdeki kitapları koruma önlemlerine, yaşanan sıkıntılara ve vakıf kitaplarının devlet malı olarak korunma serüvenine yer verilmiştir.
Töre Bitiki Metninden Hareketle Gregoryan Muhiti Kıpçak Türkçesi Terimi
Authors: ATALAN Soner
Published: December 2023

Töre Bitiği, Gregoryan Kıpçakların hayatlarını düzenleyen, toplumsal ilişkilerini belirleyen bir hukuk metnidir. Mihitar Goş tarafından 12. yüzyılda Hay diliyle yazılan Dadastan Girk (Yargı Kitabı) 1518-1519 yılları arasında Hay dilinden Latinceye, 1523 yılında Kral I. Zigmont himayesinde Latinceden Lehçeye ve aynı tarihte Lehçeden Gregoryan Muhiti Kıpçak Türkçesine tercüme edilmiştir. Bu tercüme sırasında, Dadastan Girk’in giriş bölümü ve 98 maddesi Hay dilinden Kıpçak Türkçesine doğrudan tercüme edilmiş ve bu bölümlere ek olarak 24 madde Gregoryan Kıpçaklar tarafından Kıpçak Türkçesi ile eklenmiştir. Bu 24 ek madde dışında, Yargılama Usulü Yasaları bölümü tamamıyla Gregoryan Kıpçaklar tarafından yazılmıştır. Bu sayede Töre Bitiği metni sadece tercüme bir metin değil aynı zamanda Gregoryan Kıpçaklar tarafından yeniden oluşturulmuş bir metin olma özelliği kazanmıştır.

Bu çalışmada önce, Gregoryan Kıpçaklar, Kıpçakların Gürcü ve Haylarla ilişkileri, Töre Bitiği’nin oluşum süreci, nüshaları ve içeriği hakkında bilgiler verilmiştir. Sonrasında, metninde eserin dili hakkında yazılan açıklamalar ve eserdeki millet ve dil ayrımları incelenmiştir. Bu metinde yer alan dil ve millet bilgileri aynı dille yazılan diğer eserlerdeki bilgiler ile birlikte değerlendirilmiştir. Bu bilgiler ışığında bu eserlerin yazıldığı dilin Gregoryan Muhiti Kıpçak Türkçesi terimi ile isimlendirilmesinin gerekçeleri sunulmuştur.
Kadere Karşı Koy A.Ş.’yi Bildungsroman Örneği Olarak Okumak
Authors: AKYÜZ SİZGEN Berna
Published: December 2023

Bildungsroman Türk diline “büyüme” ya da “oluşum” romanı adıyla aktarılan bir roman türüdür. Roman kişisinin anlatı boyunca yaşayacağı büyüme/olgunlaşma sürecine odaklanan bu roman türünün geçmişi oldukça eski zamanlara dayandırılabilmekle birlikte kadın yazarların artması ile bu türün gördüğü rağbetin de arttığı görülmektedir. Alev Alatlı romanlarında toplumsal yapının kodlarını çözme girişiminden geri durmayan bir yazardır. Romanlarında modern Türk toplumunun duyuş, düşünüş, yaşayış biçimlerine dair önemli gözlemler, tespitler yer almaktadır. Kadere Karşı Koy A.Ş. de bu romanlardan biri olmakla birlikte konusu itibariyle roman kişisinin yaşayacağı bildungs sürecine özel ilgi göstermektedir. Çalışma bildungsroman türüne ilişkin bilgi verilmesiyle başlayacaktır. Ardından Kadere Karşı Koy A.Ş. romanı bildungsromanın temel nitelikleri açısından ayrıntılı olarak değerlendirilecek ve ulaşılan tespitler öz biçimde ortaya konulacaktır.
Millî Kimlik İnşasında Âşıkların Kolektif Nasihatlerine Tematik Düzlemde İşlevsel Bir Bakış
Authors: BUNSUZ Halil
Published: December 2023

Türkler, tarihleri en eski zamanlara kadar ulaşan köklü bir millettir. Sözlü ve yazılı kaynaklar ışığında yapılan çalışmalarda bu milletin güçlü bir medeniyete sahip olduğu görülür. Hızla değişen ve gelişen tarihî süreçte, güçlü bir medeniyet oluşturabilmek, millî ve manevi değerlerde süreklilik ve bu değerlere bağlılık gerektirir. Türkler bu özellikleri ile tarih boyu siyasi, sosyal ve kültürel alanda varlıklarını devam ettirmişlerdir. Nitekim kurdukları devletler yıkılıp dağıldığında millî şuur ve manevi inanç ile tüm zorlukları aşarak yeni bir devlet kurmayı başarmışlardır. Ancak 20. yüzyıla gelindiğinde kurdukları en uzun ömürlü devlet olan Osmanlı Devleti yıkılmıştır. Değişen şartlarda çok uluslu devlet yapısından ulus devlet anlayışına geçilmesi gerektiği anlaşılmış ve Türkiye Cumhuriyeti kurulmuştur. Osmanlı Devleti’nin son dönemlerinde art arda gelen siyasi ve askerî başarısızlıklar, Türk milletini psikolojik bakımdan yormuştur. Bu durumun farkında olan dönem idarecileri yeni devletin sağlam temeller üzerinde kurulup inşa edilmesi için kültür, dil, edebiyat, sanat gibi alanlarda millî kimlik odaklı birtakım çalışmalar yapmışlardır. Âşıklık geleneği de yeni devletin ideolojisinin halka benimsetilmesinde faydalanılan alanlar arasında yer almıştır. Geleneğin öneminin ve âşıkların işlevinin farkında olan idareciler, bu dönemde âşıkları maddi ve manevi anlamda desteklemişlerdir. Yeni bir devletin kurulup kökleşmesi aşamasında ise âşıkların, millî kimlik inşası için kolektif bir çalışma yaptıkları tespit edilmiştir. Yapılan incelemelerde âşıkların anadil ve dil bilinci, bayrak, din duygusu, millî bayramlar, millî birlik ve beraberlik, millî egemenlik, millî kahramanlar, milliyeti sevme, şehitlik ve gazilik, Türk tarihi, vatan/memleket sevgisi, yenileşme/modernleşme çalışmaları gibi temlerde duygu ve düşüncelerini nasihat yoluyla halka kolektif bir şekilde anlatmaya çalıştıkları görülmüştür. Bu çalışmada Cumhuriyet Devri âşıklarının millî bir kimlik inşasında kolektif nasihatlerinde yer alan millî temler, milliyetçilik sosyolojisi bağlamında tematik düzlemde incelenecek, âşıkların yeni bir ideolojiyle kurulan devletin güçlenmesinde ve korunmasındaki işlevleri değerlendirilecektir.
Factors Affecting Development of the Russian Market of Fulfillment Services
Authors: Kholmovsky Stanislav , Silantev Aleksandr , Maletskaya Marina
Published: December 2023

The growing scale of the Russian e-commerce market over the recent years has led to significant quantitative and qualitative changes. These changes resulted, in particular, in the emergence of new logistics intermediaries known as order fulfillment operators, undertaking the entire range of operations related to processing online orders. But, despite obvious advantages of outsourcing logistics operations for small and medium-sized online stores, the percentage of orders completed via the services of these intermediaries has remained almost unchanged in recent years. The purpose of this study is to assess significance of the factors limiting development of the Russian market of fulfillment services. The methodology of the study is based on an analysis of the main trends in development of this sector of logistics intermediaries. The study was based on the data of statistical reporting and analytical reviews. The object of the study is the Russian fulfillment services market. The scientific novelty of the work lies in the systematization of the factors that cause restrictions in developing of the market in question. Among these factors, we can single out the underdevelopment of the logistics infrastructure of fulfillment operators; incurring risks of outsourcing certain functions of sellers to logistics intermediaries; customer dissatisfaction with quality of services provided; non-transparent pricing policy of fulfillment operators themselves. The study made it possible to determine some ways of improving performance of order fulfillment operators, which will allow them to quickly and efficiently adjust to new economic conditions.
DAB staining protocol for subsequent stereological cell counting v1
Authors: T. Henrich Martin
Published: December 2023

This protocol describes the steps for performing a double chromogen staining using DAB and SK- 4700. Stained sections can subsequently be imaged and used for stereological cell quantification with Stereo Investigator software from MBF Bioscience or other Stereology software.
Modified Arabidopsis RootsmRNA FISH Protocol v2
Authors: Duncan Susan , Johansson Hans
Published: December 2023

Single molecule RNA FISH (smRNA FISH) is an imaging method that labels individual mRNA molecules in cells to facilitate localization and quantitative studies. Here we present a modified protocol for mRNA labelling in Arabidopsis root meristem cells that retains GFP fluorescence. Although the processing steps impact GFP intensity, this protocol demonstrates visualization of RNA together with highly expressed protein. This protocol is presented together with simplified image analysis steps that aim to support inexperienced cell biologists through from experimental set up to mRNA quantification.
Kahramanmaraş'ta Hastane ve Serbest Eczanelerde Görev Yapan Eczacıların Farmakovijilans Konusundaki Bilgi ve Tutumlarının Araştırılması
Authors: Dokumacı Algül Dilara , Karataş Yusuf
Published: December 2023

Amaç: Advers ilaç reaksiyonları toplum sağlığını etkileyen önemli bir sorundur. Bu nedenle, pazarlama sonrası dönemde ilaç güvenliğinin yakından izlenmesi ve advers etkilerin bildirilmesi büyük önem taşımaktadır. Advers etkilerin sağlık çalışanları tarafından yetersiz bildirimi, farmakovijilans sisteminin gelişebilmesinin önünde önemli bir engel olarak görülmektedir. Eczacı, hastanın sağlık çalışanları ile iletişiminin son basamağını oluşturması ve kolaylıkla ulaşılabilir olması nedeniyle ilaç güvenliğinin izlenmesi ve advers etkilerin bildirilmesinde önemli sorumluluk taşımaktadır. Bu çalışmada, Kahramanmaraş ilindeki eczacıların farmakovijilans konusundaki bilgi ve tutumlarının incelenmesi ve farmakoepidemiyolojik açıdan veri tabanı oluşumuna katkı sağlanması amaçlanmıştır.
Gereç ve Yöntem: Kahramanmaraş’ta görev yapan 200 eczacıya yüz-yüze anket uygulanmış ve anketi 190 eczacı cevaplamıştır. Anket; eczacıların sosyo-demografik özellikleri, farmakovijilans konusundaki bilgileri ve advers ilaç reaksiyonu bildirimi hakkında tutumları ile ilgili bilgi edinmeye yönelik sorulardan oluşmaktadır.
Bulgular: Ankete katılan eczacıların %20’si farmakovijilansı doğru olarak tanımlamıştır. Eczacıların %55.8’i son bir yıl içerisinde kendilerine advers etki şikayeti ile başvuru olduğunu belirtmiştir. Advers etki şikayetine en çok neden olan ilaç grupları arasında, antibiyotikler (%40.8), analjezikler (%16) ve kardiyovasküler ilaçlar (%12.8) bulunmaktadır. Ankete katılan eczacıların %55.8’i advers etkilerin bildirilmesi gerektiğini bilmelerine rağmen, sadece %8.4’ü bildirim yapmıştır. Bildirim yapmama nedenleri arasında; yeterli bilgiye sahip olmamak, gerekli olduğunu düşünmemek ve zamanın kısıtlı olması gibi nedenler yer almaktadır.
Sonuç: Elde ettiğimiz bulgulara göre, Kahramanmaraş’ta görev yapan eczacıların büyük bir kısmının farmakovijilans konusunda yeterli bilgiye sahip olmadığı ve advers etki bildiriminin düşük seviyede olduğu görülmüştür. Advers etki bildirimlerinin yeterli düzeye ulaşması için mezuniyet öncesi ve sonrası eğitim programları ile farmakovijilans konusundaki farkındalığın arttırılmasına ihtiyaç bulunmaktadır.
Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?
Authors: Al-Haddad Luttfi A. , Giernacki Wojciech , Shandookh Ahmed A. , Jaber Alaa Abdulhady , Puchalski Radosław
Published: December 2023

In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While the six-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.
Customized Semantic Segmentation for Enhanced Disease Detection of Maize Leaf Images
Authors: Mohana Priya. C Et al.
Published: December 2023

Maize leaf images are affected by various diseases. Though many image processing techniques are available to identify diseased segment of a diseased maize leaf image proper methodology to segment every chunk in the leaf as disease, shadow, healthy and background using a single methodology is still in search of. So, a single line of attack is availed using Semantic Segmentation for diseased maize Leaf images through which every pixel in an image is equated to a class. Initially multiple classes in the maize leaf images are Labeled and trained. ImagedataStore and PixelLabelDatastore are used to distinguish original images and trained images. With different classes defined and trained using the Semanticseg model and later applying semantic segmentation to the diseased maize leaf images they are segmented into various classes such as healthy, diseased, shadow and background. The shadows and background are difficult to handle and with this segmentation the exact pixel count of various classes are displayed. The output of semantic segmentation is a maize Leaf image where each pixel is equated to a particular class whereas in normal CNN the output is not an image but a class.
Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification
Authors: Mhatre Amruta , Sharma Prashant , Maurya Anup R
Published: December 2023

An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems.
NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments
Authors: Shelke Pallavi , Shahapurkar Rekha
Published: December 2023

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling.
House Price Prediction using Machine Learning Algorithms
Authors: M Angulakshmi , M Deepa , I Mala Serene , M Thilagavathi , P Aarthi
Published: December 2023

House prices are a major financial decision for everyone involved in the housing market, including potential home buyers. A major part of the real estate industry is housing. An accurate housing price prediction is a valuable tool for buyer and seller as well as real estate agents. The study is done for the purpose of knowledge among the people to understand and estimate the pricing of their houses. The prediction will be made using four machine learning algorithms such as linear regression, polynomial regression, random forest, decision tree. Linear Regression has good interpretability. Decision tree is a graphical representation of all possible solutions. Polynomial regression can be easily fitted to a wide variety of curves. Regression and classification issues are resolved with random forests .Among the given algorithm, Random forest provides better accuracy of about 89% for given dataset.
RESDEN: A Novel Deep Unified Model for Face Recognition System
Authors: Kavita Kavita , Chhillar Rajender Singh
Published: December 2023

The Face Recognition technology plays a significant role in the field of Computer Vision in contemporary times. The research article is centered on a Facial attendance system that utilizes a deep learning technique to recognize face photos. To execute face identification and classification via the use of deep learning processes, many Convolutional Neural Network (CNN) models are taken into account. Previous studies have mostly focused on either the ResNet or DenseNet-based convolutional neural network model. The present research utilizes the merging of ResNet and DenseNet to propose a hybrid model. The proposed work is expected to provide enhanced efficiency and accuracy. In the training and testing stages of the simulation, considerations are made for both binary and category classifications. The current research focuses on the use of the LFW dataset. The pictures undergo an initial step of the noise reduction process. The evaluation of picture quality is conducted by taking into account metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). After the proposed model has undergone training, it generates photographs of superior quality. Finally, the proposed system incorporates the RESDEN framework, which integrates DenseNet with a noise reduction technique, a segmentation mechanism, and a CNN based on ResNet. A comparative analysis has been conducted to evaluate the accuracy of several filtered picture sets across different convolutional neural network (CNN) models. The simulation results indicate that the suggested model exhibited a good level of performance and accuracy.
Enhancing Mental Health Awareness through Twitter Analysis: A Comparative Study of Machine Learning and Hybrid Deep Learning Techniques
Authors: Kancharapu Rohini , Ayyagari Sri Nagesh
Published: December 2023

This study explores the utilization of social media data, specifically tweets and comments, for gaining insights into individuals' mental health conditions. The objective is to enhance mental health awareness and enable early detection and intervention. Twitter data is collected using depression-related keywords, and two models are employed: a Random Forest model with TF-IDF and a hybrid CNN-LSTM model incorporating word2vec. The performance of the CNN-LSTM model surpasses that of the Random Forest model, achieving an accuracy rate of 89.4%. Furthermore, a user interface is developed to analyze users' Twitter profiles based on their tweets, allowing for potential intervention through automated reply messages. By harnessing social media data and advanced machine learning techniques, this research contributes to improving mental health awareness and timely addressing of mental health concerns.
Unleashing the Power of Deep Attention Networks: A Comprehensive Approach for Enhanced Artificial Intelligence
Authors: Prakash Anju J , S Sruthy , Agustin Sheeja , S Jinan
Published: December 2023

Deep learning has revolutionized the field of artificial intelligence by achieving state-of-the-art performance on a variety of complex tasks. Attention mechanisms have emerged as a powerful tool to enhance the capabilities of deep neural networks by enabling them to selectively focus on relevant information. In this article, we propose a novel artificial intelligence algorithm called Deep Attention Networks (DANs), which associate multiple attention mechanisms to improve performance on interesting tasks. We evaluate DANs on benchmark datasets in natural language processing, computer vision, and speech recognition and demonstrate superior results compared to existing state-of-the-art approaches. Our approach opens up new possibilities for advancing the field of artificial intelligence and holds promise for various real-world applications. Overall, our results demonstrate the effectiveness and potential of DANs for various AI applications, and highlight the power of combining deep neural networks with attention mechanisms.
Integrating IoT and Novel Approaches to Enhance Electromagnetic Image Quality using Modern Anisotropic Diffusion and Speckle Noise Reduction Techniques
Authors: R Shekhar , Kaur Mandeep , S B Manojkumar , Gowda V Dankan , Kumar Chanakya , Praveena K.
Published: December 2023

Electromagnetic imaging is becoming more important in many sectors, and this requires high-quality pictures for reliable analysis. This study makes use of the complementary relationship between IoT and current image processing methods to improve the quality of electromagnetic images. The research presents a new framework for connecting Internet of Things sensors to imaging equipment, allowing for instantaneous input and adjustment. At the same time, the suggested system makes use of sophisticated anisotropic diffusion algorithms to bring out key details and hide noise in electromagnetic pictures. In addition, a cutting-edge technique for reducing speckle noise is used to combat this persistent issue in electromagnetic imaging. The effectiveness of the suggested system was determined via a comparison to standard imaging techniques. There was a noticeable improvement in visual sharpness, contrast, and overall clarity without any loss of information, as shown by the results. Incorporating IoT sensors also facilitated faster calibration and real-time modifications, which opened up new possibilities for use in contexts with a high degree of variation. In fields where electromagnetic imaging plays a crucial role, such as medicine, remote sensing, and aerospace, the ramifications of this study are far-reaching. Our research demonstrates how the Internet of Things (IoT) and cutting-edge image processing have the potential to dramatically improve the functionality and versatility of electromagnetic imaging systems.
A Deep CNN Framework for UAV Intrusion Detection in Intelligent Systems
Authors: Arulalan V. , Balamurugan G. , Premanand V.
Published: December 2023

Unmanned Ariel Vehicle (UAV) s are dealing with several safety and protection issues including internal hardware/software and potential attacks. In addition, detecting UAV anomalies will be a crucial responsibility to defend against hostile enemies and prevent accidents. In this research, we present a UAV and an Automatic Dependent (AD) system using surveillance and Machine Learning (ML) algorithms to analyze data from their detectors in real-time. Proposed Improved Region based Convolutional Neural Network (IRCNN) model used to generate and acquire the characteristics of untreated sensor information and characteristics to facilitate AD. The proposed model creating an Inertial Measurement Unit (IMU) & UAV sensors dataset using cyber security simulation system and Active Learning (AL) identifies aggressions based on the least probable interrogation method. This proposed model enables the identification to efficiently improve the occurrences of unexplained aggressions discovered of IRCNN at reduced labeling cost. A thorough trial showed that IRCNN-AL is effective at detecting unknown threats with frequency improvements of between 9% and 30% on comparison approaches. The AL methodology presented with as few as 1% of a labeled unexpected aggressions.
Productivity Improvement with Generative AI Framework for Data Enrichment in Agriculture
Authors: Chhaya Narvekar Et al.
Published: December 2023

The improvement in agricultural sector is essential for ensuring food security. Sector faces a multitude of challenges like climate change, resource limitations, and heightened food demand. To meet these challenges, there is an increasing demand for innovative solutions to enhance agricultural productivity, sustainability, and efficiency. This study presents an innovative framework that harnesses Generative Artificial Intelligence (GAI) to revolutionize agriculture. The objective is to conceptualize framework that integrates state-of-the-art AI techniques, encompassing deep learning and generative models, to provide farmers and stakeholders with data-driven insights and decision support tools. By leveraging GAI capabilities, study aims to address key agricultural issues, providing prototype implementation. Study concludes with various possible solution including crop yield prediction, disease identification, soil analysis, and resource optimization and future direction.
Undersea Water Objects Detection and Classification Using Optimized Region-Based Convolutional Neural Network
Authors: Jayachandran Arumugam Kaipa Sandhya,
Published: December 2023

Underwater autonomous operation is becoming increasingly crucial to avoid the hazardous in the environment of high-pressure deep-sea due to the significance of underwater investigation. The most crucial piece of technology for underwater-based task is intelligent computer vision. In an underwater environment, underwater vision requires good image quality, and illumination with better classification of sea objects. This work presented a novel technique of Optimized Region-based Convolutional Neural Network (ORCNN). For this work, the Gaussian filter is used to remove the noise and enhance the image quality during pre-processing and the Improved Affinity Propagation Clustering (IAPC) model segments the objects. After that, the Region-based Convolutional Neural Network (RCNN) model classifies various objects such as urchins, seagrass, fishes, and rocks in which the RCNN parameters are tuned via a light spectrum optimizer algorithm (LSOA). can also input the segmentation prediction images and the labeled images into the discriminant convolutional network and improve the segmentation accuracy of underwater images by further enhancing the essential characteristics of learning data through the confrontation training of generators and discriminators. The experimental results demonstrate that higher performance is provided by the newly developed ORCNN -Net predictive model when compared to other comparative algorithms while considering the negative and positive metrics.
Deep Learning-Based Video Compression for Surveillance Footage
Authors: Nikita Mohod, Vishu Madaan Prateek Agrawal,
Published: December 2023

The utilization of closed-circuit television (CCTV) monitoring plays a pivotal role in the realm of video processing, providing an effective means for vigilant surveillance. However, a significant challenge associated with this practice is the substantial demand it places on storage space. Traditionally, surveillance footage is stored on hard disk drives, and due to limited storage capacities, it often necessitates periodic deletion. To tackle this issue, we have introduced an innovative method for compressing CCTV video, named “Detection-Based video Compression” (DBVC). Our DBVC model is a two-step process. In the first step, we employ advanced neural network approaches such as Mask-RCNN and YOLOv4 to determine active and idle frames within the surveillance video. These cutting-edge techniques enable precise identification of objects and events of interest in the video feed. In the second step, we construct a new video composed solely of the active frames, eliminating redundant or uneventful segments. After conducting a comprehensive analysis of the experimental results, it is evident that Mask-RCNN stands out with an impressive detection accuracy of 98% on the COCO dataset, making it a robust choice for identifying objects and events in the surveillance footage. Consequently, we chose to leverage the output generated by Mask-RCNN and YOLOv4 for subsequent processing stages. Our DBVC approach is a breakthrough in video compression technology, significantly reducing the storage space required for CCTV footage. In fact, it achieves an average compression ratio of up to 85% when using YOLOv4, surpassing the capabilities of existing state-of-the-art compression methods. This innovation not only optimizes storage efficiency but also maintains a high level of surveillance data integrity, making it a valuable advancement in the field of CCTV video processing and storage management.
BlockGov: Blockchain-Based Data Governance in the Internet of Things using Smart Contracts
Authors: M. Rajkumar, N. S. Usha K. Sudharson, C. S. Anita,
Published: December 2023

The rapid growth and integration of the Internet of Things (IoT) emphasizes the crucial need for effective data governance. This research unveils a novel framework, capitalizing on blockchain and smart contracts, aimed at decentralizing data governance in the IoT sphere. Our approach allows stakeholders to formulate and enforce data governance collaboratively, ensuring a balance between transparency, adaptability, and flexibility. Using the Ethereum platform and Solidity as our smart contract language, we constructed a demonstrative proof-of-concept. Our comparative evaluations highlighted our system's superiority, outpacing previous works with a scalability score of 95%, flexibility at 90%, and an unmatched transparency score of 100%. This framework presents a transformative paradigm for organizations and individuals working with IoT data, offering an efficient, transparent, and robust data governance mechanism.
Lightweight Cryptography based Communication Model for Device Identification, Mutual Authentication, and Encryption in a Smart City Environment
Authors: Monica Mehrotra Md Shamsul Haque Ansari,
Published: December 2023

Providing security to smart city networks is one of the challenging and demanding tasks in the present days, due to its increased utilization in smart intelligent transportation systems. For this purpose, there are various security protocols and mechanisms that have been developed in the existing works, which targets to establish the reliable and secured communication in smart city networks. However, it limits the major issues of increased computational cost, communication cost, storage overhead, and reduced efficiency. In order to solve these problems, the proposed work intends to design an intelligent security framework by using the Light-weight Cryptography based Communication Model (LCCM). Proposed framework includes the modules of setup initialization, vehicle registration, authentication, key generation, encryption, and decryption. Here, the Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications are performed with reduced cost complexity. For guaranteeing the security of networks, the random value-based key generation, data encryption, and decryption processes are performed. During the performance analysis, various evaluation measures have been used to assess the results of both convention and proposed security protocols. This paper presented a new methodology named as, LCCM for enhancing the security of smart city transportation networks.
Deep Insight into Urban Air Quality Utilizing Neural Networks for Enhanced Prediction in Korean Cities Where Factories and Ecosystem Environments Coexists
Authors: Hyunwook Kim Hyun Sim,
Published: December 2023

Increased attention is being given to air pollution in recent times. This study investigated and analyzed particulate matter data from Yeosu, Gwangyang, and Suncheon in Jeollanam-do, with a particular focus on PM2.5. Descriptive statistics, box-and-whisker plots, correlation matrices, time variations, and trend analyses were performed for this purpose. Additionally, a prediction model for PM2.5 concentrations was developed using machine learning techniques, through which future changes in air quality were forecasted.

Calculations were performed using R-based programs and R packages. Hourly PM2.5 data were obtained from air quality monitoring sites in Yeosu, Gwangyang, and Suncheon. After data preprocessing, the optimal prediction model was constructed using Random Forest and Gradient Boosting Machine from various machine learning algorithms.

The research results showed that there was more PM2.5 pollution in Gwangyang compared to Yeosu and Suncheon. The PM2.5 concentrations varied significantly across each monitoring site. Among the monitoring sites, the Yeosu site showed a higher correlation in PM2.5 with each other than other sites. Late winter and early spring showed higher PM2.5 concentrations, while summer and autumn showed lower concentrations. Weekly PM2.5 concentration fluctuations were not significantly different. Daily fluctuations showed an increase in PM2.5 concentrations during times of traffic congestion and a decrease in the afternoon. During the research period, the trend of PM2.5 concentration was generally decreasing.

The accuracy of the prediction model through machine learning was over 90%, and it is expected to assist in establishing effective response strategies for future changes in air quality. This study provided an updated and useful evaluation of recent PM2.5 air quality in Yeosu, Gwangyang, and Suncheon in Korea.
The Most Influential Factor as The Cause of Delays in Aluminum Form Work in The Sakura Project, East Jakarta
Authors: Widiasanti Irika
Published: December 2023

Project development in Indonesia, especially DKI Jakarta continues to grow, especially in the construction of a multi-storey building concept. These high-rise buildings, including apartments, offices, and flats, have different construction methods. The construction method used in the construction of buildings with multilevel concepts is now more advanced, using a method called aluminum formwork. Aluminum formwork work in several projects in multi-storey buildings sometimes has problems in its work which can cause delays, one of which is in the Sakura Project, East Jakarta. For example, delays that occur in aluminum formwork work, namely in the delivery of materials and the lack of skilled workers on the job. This can also be caused by executors who are used to taking project work for granted and not learning from it. Therefore, with delays in aluminum formwork work, the purpose of this study was to determine the most influential factors causing delays in aluminum formwork work. The research method used is descriptive quantitative with a survey approach where the data collection technique uses questionnaires, interviews and documentation, and is analyzed into factor analysis with the help of SPSS software. The results of this study indicate that the factors that cause delays in aluminum formwork work are labor factors with a value of 65%, material factors with a value of 71%, equipment factors with a value of 62%, place characteristic factors with a value of 69%, managerial factors with a value of value of 58%, financial factors with a value of 72%, and external factors with a value of 68%. Thus, it can be concluded that the factor causing the delay that occurred in the aluminum formwork work at the Sakura Project, East Jakarta, which is the most influential or dominant factor, is the financial factor, while the factor that has the least influence is managerial factor.
An Optimal Deep Learning Model With Effective Feature Learning Mechanism For Stock Market Prediction
Authors: K. Sujith D. Syinthiya,
Published: December 2023

The stock market has considered the active research fields today, and forecasting its behaviour is an enormous necessity. Predicting the stock market is complex, necessitating a thorough examination of data patterns. Correct forecasting outcomes can provide significant insight to investors, lowering investment risk. This paper proposes a novel Weight and Bias Tuned Long Short-term Memory (WBTLSTM) with an efficient feature extraction model, Bilateral ReLU-based Two-Dimensional Convolutional Neural Network (BR2DCNN), for stock price prediction (SPP). First, the stock data was collected from the publicly available dataset. Then the missing values imputation and data normalization is performed on the collected dataset. The preprocessed dataset extracts the most relevant features using BR2DCNN. Finally, the future stock events are predicted using the WBTLSTM. The weights and biases are tuned with the help of the Enhanced Butterfly Optimization Algorithm (EBOA). Experimental findings prove that the proposed one achieves superior outcomes compared to the conventional methods regarding some performance metrics.
Irregularity Behaviour Detection - Ad-hoc On-Demand Distance Vector Routing Protocol (IBD - AODV): A Novel Method for Determining Unusual Behaviour in Mobile Ad-hoc Networks (MANET)
Authors: N. Kanimozhi Et al.
Published: December 2023

All the communication in the mobile ad-hoc network (MANET) will depend on the intermediate neighbour nodes or router nodes. Routing protocol is very important in MANET, because all the communication will be done in the MANET depending on the neighbour or intermediate node. If the intermediate node is a malicious node, all the data will be lost or changed by the intermediate or malicious node. Ad-hoc On-demand Distance Vector routing protocol is one of the moderate routing protocol in MANET. The Ad-hoc On-demand Distance Vector Routing protocol does not have any security mechanism. This work is going to find the Irregularity Behaviour Detection (IBD) over the Ad-hoc On-Demand Distance Vector Routing (AODV) protocol. IBD is finding a trusted node by using the trust value (TV) of the node. This TV includes network performance, node energy level, and node position value. IBD-AODV is implemented and tested in the OmNetpp 6.0 simulator.
Fine-tuning Road Classification Models: Optimization Strategies for Deep Belief Networks in Transportation Big Data
Authors: B. Sangeetha Et al.
Published: December 2023

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this paper, the classification of road accident in transportation domain was analyzed with the assistance of the proposed Intelligent classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed intelligent classifier gives the improved accuracy, specificity, precision, Sensitivity, F-Measure, and reduced false positive rate.