Epidural akut hematomlar için akıllı erken uyarı sistemi

Epidural hematoma (EAH) is the accumulation of blood in the space between the outer membrane of the brain (dura mater) and the bone. Acute subdural and epidural hematoma appears on CT scan as a hyper-dense collection often located in brain convexity. Such bleeding can become fatal by increasing intracranial pressure and creating a mass effect. Therefore, it is very important to recognize these bleedings promptly in an emergency trauma setting. Thus, early diagnosis is essential to reduce mortality and morbidityratesin these cases. There has been a growing interest in artificial intelligence (AI) and machine learning (ML) algorithms for diagnostics in medical fields. In this study, a supervised learning method was used in which the decision tree ML algorithm is trained with the patients'statuses(EAH or Normal). This study proposes an early warning system (EWS) that scans all cranial CTs obtained at the trauma center. The EWS in this study, trained with CT scans from about 100 patients, can predict EAH with 100% accuracy usingimage recognition and supervised learning algorithms. Each MR section obtained for each patient is individually analyzedbyimage processing and EAH detection is made. For this, the decision tree method, which is a supervised learning algorithm, was trained and used to detect EAH in MR sections. The algorithm has been developed in such a way that it will immediately alert the emergency physician and consultant neurosurgeon by e-mail when it detects EAH in more than 10 sections in any patient.Epidural hematom (EAH), beynin dış zarı (dura mater) ile kemik arasındaki potansiyel boşlukta kan birikmesidir. Akut subdural ve epidural hematom, BT taramasında genellikle beyin konveksitesinde yer alan hiper yoğun bir koleksiyon olarak görünür. Bu tür kanamalar kafa içi basıncını artırarak ve kitle etkisi yaratarak ölümcül hale gelebilir. Bu nedenle, acil travma ortamında bu kanamaların derhal tanınması çok önemlidir. Bu nedenle bu vakalarda mortalite ve morbiditeyi düşürmek için erken tanı şarttır. Tıbbi alanlarda teşhis için yapay zeka (AI) ve makine öğrenimi (ML) algoritmalarına son zamanlarda artan bir ilgi vardır. Bu çalışmada, karar ağacı ML algoritmasının hastaların durumlarıyla (EAH veya Normal) eğitildiği denetimli bir öğrenme yöntemi kullanılmıştır. Bu çalışma, travma merkezinde elde edilen tüm kraniyal BT'leri tarayan bir erken uyarı sistemi (EWS) önermektedir. Bu çalışmadaki EWS, yaklaşık 100 hastadan alınan CT taramaları ile eğitilmiştir, görüntü tanıma ve denetimli öğrenme algoritmaları ile%100 doğrulukla EAH'yi tahmin edebilir.Her hasta için elde edilen her MR kesiti teker teker görüntü işleme analizinden geçirilir ve EAH tespiti yapılır. Bunun için bir denetimli öğrenme algoritması olan karar ağacı yöntemi eğitilerek MR kesitlerinde EAH saptaması için kullanılmıştır. Algoritma herhangi bir hastada 10’dan fazla kesitte EAH tespit ettiğindeacil durum hekimine ve danışman beyin cerrahına e-posta ile anında uyarı verecek şekilde geliştirilmiştir

Data Mining Applications in Banking Sector While Preserving Customer Privacy

In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PD

Detecting Turkish fake news via text mining to protect brand integrity

Fake news has been in our lives as part of the media for years. With the recent spread of digital news platforms, it affects not only traditional media but also online media as well. Therefore, while companies seek to increase their own brand awareness, they should also protect their brands against fake news spread on social networks and traditional media. This study discusses a solution that accurately classifies the Turkish news published online as real and fake. For this purpose, a machine learning model is trained with tagged news. Initially, the headlines were analyzed within the scope of this study that are collected from Turkish online sources. As a next step, in addition to the headlines of these news, news contexts are also used in the analysis. Analysis are done with unigrams and bigrams. The results show 95% success for the headlines and 80% for the texts for correctly classifying the fake Turkish news articles. This is the first study in the literature that introduces an ML model that can accurately identify fake news in Turkish language

Diagnosis of Covid-19 via patient breath data using artificial intelligence

Using machine learning algorithms for the rapid diagnosis and detection of the COVID-19 pandemic and isolating the patients from crowded environments are very important to controlling the epidemic. This study aims to develop a point-of-care testing (POCT) system that can detect COVID-19 by detecting volatile organic compounds (VOCs) in a patient's exhaled breath using the Gradient Boosted Trees Learner Algorithm. 294 breath samples were collected from 142 patients at Istanbul Medipol Mega Hospital between December 2020 and March 2021. 84 cases out of 142 resulted in negatives, and 58 cases resulted in positives. All these breath samples have been converted into numeric values through five air sensors. 10% of the data have been used for the validation of the model, while 75% of the test data have been used for training an AI model to predict the coronavirus presence. 25% have been used for testing. The SMOTE oversampling method was used to increase the training set size and reduce the imbalance of negative and positive classes in training and test data. Different machine learning algorithms have also been tried to develop the e-nose model. The test results have suggested that the Gradient Boosting algorithm created the best model. The Gradient Boosting model provides 95% recall when predicting COVID-19 positive patients and 96% accuracy when predicting COVID-19 negative patients

Robot process automation (RPA) and its future

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don’t require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions

Belge tabanlı bankacılık süreçleri için akıllı sistem tasarımı

Business process automation has been helping companies by eliminating mundane and repetitive tasks. Automation tools have been used in many sectors, providing high full-time employee (FTE) savings and low error rates to the companies. Banks have been utilizing the automation tools for core banking and branch operations. In addition, banks receive hundreds of notice documents from courts, municipalities and third parties; which usually contain private and time-sensitive information about their customers. Automated document processing requires special solutions such as optical character recognition (OCR), natural language processing (NLP) and taxonomy; and modern process automation tools can utilize these solutions to provide end-to-end automation. This paper discusses how the notice documents such as account closure, payment blockage in Turkey can be automated. It also shows how automation can efficiently and effectively process the documents by providing experimental results for each document type.İş süreci otomasyonu, sıradan ve tekrarlayan görevleri ortadan kaldırarak şirketlere yardımcı olmaktadır. Otomasyon araçları birçok sektörde kullanılmakta olup, şirketlere yüksek tam zamanlı çalışan (FTE) tasarrufu ve düşük hata oranları sağlamaktadır. Bankalar, ana bankacılık ve şube operasyonları için otomasyon araçlarını kullanmaktadırlar. Ayrıca her gün bankalara mahkemelerden, belediyelerden ve üçüncü şahıslardan yüzlerce ihbar belgesi gelir; ve bu belgeler genellikle müşteriler hakkında özel ve zamana duyarlı bilgiler içerir. Bu belgelerin otomatik işlenmesi prosedürü, optik karakter tanıma (OCR), doğal dil işleme (NLP) ve sınıflandırma gibi özel çözümler gerektirir; ve modern süreç otomasyon araçları, uçtan uca otomasyon sağlamak için bu çözümleri kullanabilir. Bu makale, Türkiye'de hesap kapatma, ödeme blokajı gibi bildirim belgelerinin nasıl otomatikleştirilebileceğini tartışmaktadır

Analysis of customer churn in the banking industry using data mining

Today, banks have a very important place in the great economic environments of countries. As in every sector, there are many competitors and a great competitive environment in the banking field. Especially individual customers prefer digital channels to make their banking transactions faster and easier. Banks need to take fast and industry-leading steps to meet these expectations of their customers. They need to differentiate themselves from the competition with innovative features by giving importance to digital. The main goals of the banks in the competitive environment are gaining new customers, increasing customer loyalty, reducing customer churn rates, and providing superior customer satisfaction. In this study, customer data belonging to a bank were analyzed with data analysis algorithms. Customer churn analysis was performed using different machine algorithms. The model was created on the Knime platform. This study performs a customer loss analysis using data mining algorithms. The aim is to reveal the reasons for losing customers, the elements of customer loyalty and to help develop customer relations activities accordingly

RPA in energy and utilities

Robotic Process Automation (RPA) is an effective technology that uses software robots that typically mimic an employee to automate everyday tasks. These tasks are often related to the business processes of the E&U companies such as billing, payments, check-in and check-out and other office tasks. While RPA is gaining increasing popularity in different industries, the E&U sector lags behind others in terms of maturity. Energy and utilities (E&U) is a customer-centered industry where each individual is dependent on the provided services for their daily needs. The likelihood of human error in the sector is high, given the large number of transactions happening every day, so RPA becomes essential to help companies manage transactions efficiently and improve the customer experience which is often missed by the companies. This chapter discusses how RPA can positively impact both internal processes of E&U companies and their customer-facing operations

Robot process automation (RPA) and its future

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions

Robotic Process Automation (RPA) applications in COVID-19

The COVID-19 pandemic took the entire world by surprise. Governments and companies hastily implemented measures to protect public health as well as the countries’ economies. Many companies had to close their offices and shut down their factories. Most white-collar employees had to start working from home and online communication tools became key components of their lives. While governments and companies were adapting to the new normal, sickness-resilient digital employees, the software robots, became more apparent. Robot Process Automation (RPA) introduced the ultra-performing and fast-learning software robots into our lives. These robots can work 24/7 in many areas, assisting human employees and helping customers. This chapter discusses different applications of RPA robots around the world and in Turkey during the COVID-19 pandemic. RPA became the new disruptive technology that offered innovation to the business world. It found application areas during COVID-19 in various sectors such as health care, education, and public sectors. Governments and companies got help from the RPA robots to track patients, optimize supply chain processes, assist students and teachers in online classes and respond to the increased demand in online shopping