Mehmet Bilen

Healthcare professionals find that reviewing the vast amounts of medical data from the electronic medical record (EMR) system takes more and more time. Physicians have to carefully review the data, which in turn increases the time they spend on each patient. This could reflect on the precision and quality of the treatment strategy that should be applied to the patients. If the time required for consulting a single patient increases, the doctor can attend only a few patients in a day. The medical management authority has to deal with it by assigning more physicians and additional funding. In the healthcare sector, the term ”innovation,” which is widely used in marketing and economic circles, is becoming more widely recognized as a measurable phenomenon. Innovation can be divided into four primary categories: disruptive, architectural, radical, and routine/incremental (Sounderajah et al., 2021). Instead of describing how a product or service has changed over time, the term ”disruptive innovation” is used to describe it at a single, fixed point in time. In its most basic definition, ”disruption” is the process through which a smaller company with fewer resources can successfully compete with established incumbent businesses (Sounderajah et al., 2021). Several significant characteristics define disruption, such as (1) new market entrants that target underserved or neglected customer segments; (2) a rise in low-end or emerging markets; (3) incumbents that prioritize improving their current products for their core, mainstream customers, making them slow to respond to these new competitive threats; and (4) entrants that establish a strong position by meeting rising demand and frequently offering lower-priced alternatives (Figure 13.1). Figure 13.1 Innovation landscape map. The figure describes the different levels of innovation technologies namely Radical,Routine,Architectural,Frungal,Distruptive. https://www.w3.org/1999/xlink" content-type="black-white" specific-use="print-only" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003605416/845542a2-817a-4b58-9f7e-3bf1a926651d/content/fig13_1_PB.tif"/> https://www.w3.org/1999/xlink" content-type="colour" specific-use="web-only" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003605416/845542a2-817a-4b58-9f7e-3bf1a926651d/content/fig13_1_OC.tif"/>

This book explores the pivotal role of explainable artificial intelligence (XAI) in driving digital transformation within the healthcare sector, providing comprehensive insights into its applications, ethical and legal considerations, technological requirements, and future trends. Digital Transformation and XAI in Healthcare delves into the fundamental role of XAI in transforming healthcare, addressing critical issues such as data security, ethical considerations, and the integration of XAI into existing healthcare infrastructures. By offering a comprehensive overview of technological tools, infrastructure requirements, and legal frameworks, this book equips healthcare professionals with the knowledge to navigate the complexities of XAI applications. The book explores the future of healthcare education and the pivotal role of XAI in training the next generation of healthcare professionals. It discusses how XAI can enhance learning experiences and provide more personalized education, ensuring that future clinicians are well equipped to utilize advanced AI technologies. It also delves into the technological tools and infrastructure required for implementing XAI, as well as data management and privacy concerns. The exploration of global collaborations and innovative projects highlights the book's unique perspective on the international impact of XAI in healthcare. Intended for healthcare professionals, researchers, and students, this book will provide valuable insights into the future of healthcare technology. Readers will be equipped with the knowledge to harness the power of XAI, ensuring that AI systems are not only accurate but also transparent, trustworthy, and ethically sound.

Regardless of young or old, people have quickly stepped into the world of internet with today's communication technologies such as phones, tablets, computers and smart devices. As the place of the Internet in people's lives increases, social media platforms are diversifying and users want to take part in these platforms. With the increase in the number of social media users, some negativities are encountered. The most important problem encountered in social media platforms is cyber bullying. Although cyber bullying seems to be a daily dialogue between social media users or between groups, the situation of encountering is increasing day by day with the diversity of shared information, content and agenda social media environments. With the development of technology, it is necessary to develop a platform that detects bullying with artificial intelligence technologies. One of the biggest difficulties in text classification problems that we encounter during the development of these platforms is the need to train the artificial intelligence algorithm to be used with labeled data. In this study, 21 different people, including journalists, athletes, scientists, doctors, politicians, comedians, social media phenomena, and artists who actively use social media, were selected in order to create the necessary dataset for training the models to be developed to detect cyber bullying situations. The public messages (mentions) of these 21 people sent via Twitter were compiled. After filtering the repetitive and meaningless messages sent by bot accounts out of 10500 tweets compiled, the number of messages in the dataset decreased to 7706. The labeling process, which is necessary for the dataset to be used for training and testing purposes in classification processes, was carried out by three independent people who were given preliminary information about cyberbullying (1=Includes Cyber bullying, 0=Does not include Cyber bullying). The majority of the tags, which were read and assigned by 3 different people, were accepted as the final class of the relevant message. Afterwards, the dataset was preprocessed in accordance with the principles of natural language processing and made suitable for classification algorithms. The findings obtained after the classification processes performed with the basic classification algorithms are shared. When the findings are examined, it is understood that the data set created has the competence to be used in the detection and prevention of cyber bullying. In this context, it is predicted that training specially developed and optimized artificial intelligence algorithms with the relevant dataset for the detection of cyberbullying will greatly increase the success rate.

Data centers are systems that host devices utilizing recording and communication technologies, which are expected to operate securely and accurately. Consequently, transforming data centers into smart environments for control purposes has become a significant area of focus. In this study, we monitor the cabinet environment within data centers and ensure that the control system reaches the predetermined optimal state values in the event of undesirable situations. Threshold control was implemented for humidity and flame data, while fuzzy logic theory was applied to temperature data. Fuzzy clusters can be adjusted according to the data center's location at the user's request. This approach allows users to input desired optimal and threshold values into the system, which are then evaluated based on the situation. The designed system ensures data center security with minimal personnel involvement. Additionally, all problematic events are recorded in the system, enabling them to be viewed on a webpage and communicated to designated personnel via email. In the conducted study, the fuzzy-controlled temperature value outputs are reported as heating (40%), cooling (53%), and instances where the system does not perform heating or cooling.

Beyin tümörleri kafatası içinde anormal hücre ve kitle büyümesinin genel adıdır. Beyin tümörü tanısı konulabilmesi için en yaygın tetkik beyin dokusu ve dokudaki yabancı kitleleri gösteren MR (manyetik rezonans) görüntülemesinin yapılmasıdır. Tanı konduktan sonra hızlıca tedavi süreci planlamalıdır. MR görüntüleri çekildikten sonra uzman radyologlar tarafından görüntülerin incelenerek raporlanması zaman alabilmektedir. Son yıllarda hızla gelişen derin öğrenme teknolojileri ile tıp alanında bulunan yenilikler sayesinde hastalıkların erken ve doğru teşhis edilmesi için çeşitli çalışmalar yapılmaktadır. İnsan kaynaklı hataların en aza indirilmesi bu çalışmalar içerisinde önemli bir yere sahiptir. Bu çalışmada MRI görüntülerinin işaretlenerek uzmanlara yardımcı olması için yapay zekâ tekniklerinden yararlanılarak yeni bir evrişimli sinir ağı modeli eğitilmiştir. Eğitim aşamasında U-Net modelinden yararlanılarak, BRAST veri kümesinin %80’i kullanılmıştır. Veri kümesi içerisindeki örneklerin %20’si modelin performansının değerlendirilmesi için kullanılmıştır. Eğitim ve test işlemleri sonucunda elde edilen bulgular incelendiğinde eğitilen modelin tüm tümör, tümör çekirdeği ve genişleyen tümör bölgelerini sırayla 0.908, 0.807 ve 0.877 Benzerlik oranı (BO, Dice Coefficent Score) ile başarılı bir şekilde işaretleme yapabilen bir model eğitildiği görülmektedir.

Artificial Neural Networks (ANN) are important data processing algorithms which are used for solving nonlinear problems. Through classical approaches, mathematical infrastructure and complex equations in ANN are difficult to understand. Interactive and multimedia-based courseware has the potential to overcome these difficulties. In this study, a web based educational courseware for ANN was developed to provide an effective and efficient learning environment so that the difficulties can be overcome. This interactive courseware was also enriched with animations and text-based course contents. In addition to this, the effects of ANN parameters’ changes were observed directly through graphical results. In this way, users can easily understand the fundamentals and working mechanism of ANN. Without using any commercial libraries, the courseware was developed with ASP.NET, an object-oriented programming language. The courseware supports file formats such as XML, TXT, and CSV so that it can co-operate with other software. “Balance and Scale” data set was used to evaluate the performance of the courseware. 0.9918 accuracy, 1 specificity and 1 sensitivity values were achieved. When this study is compared to previous studies, improvements in terms of visuality, understandability and interactivity can clearly be identified.

Today, the amount of biological data types obtained are increasing every day. Among these data types are micro arrays that play an important role in cancer diagnosis. The data analysis that are carried out through traditional approaches have proven unsuccessful in delivering efficient results on data types where data complexity is high and where sampling is low. For this reason, using a hybrid algorithm by merging the effective features of two distinct algorithms will yield effective results. In this study, a classification process was performed firstly by dimension reduction on micro array data that were obtained from the tissues from patients with a tumor in their central nervous system and then by using an artificial neural network algorithm that was optimized through Fire Fly Algorithm (FF), a hybrid approach. The data obtained were compared to K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) classification algorithms, which are frequently used in the literature. Also, the results were compared to the findings that were obtained from artificial neural networks, which are reinforced by Genetic Algorithm (GA), another hybrid approach. Then the results were shared. The performance results obtained show that hybrid approaches present a highly precise and more efficient classification process but they show a slower performance than basic classification algorithms.