ABSTRACT. Conflicting Between Technology and Humanity in Future Society And Mind

Meil Postman reported that “Technopoly” and the technology can be both an enemy and a friend of humanity[1]. Therefore, technology creates an unfounded ethic. We are surrounded by fanatic “Teuths”. These one-eyed creatures (oracle) show what benefits new technological inventions will bring. However, they do not discuss what loses they may occur. Arthur Clark, states that sufficiently defined technology is not different from magic[2]. L. Mumford, mega machine promotes absolute management and militarism [3]. On the other hand, he concludes that he closes the door to what technology brings. He says that “technology is a new currency we use in our lives”. Hunhevicz and Hall [4] discussed about how to integrate digital information, management to maintain more trust and collaboration using blockchain. Thanks to the technology that surrounded our world, operations research (OR) and artificial intelligence (AI) technics and concepts, such as intelligence, computer, information, computer, and control systems, are redefined in every field where technology is used. Moreover, in the 21st century, with the impact of exponential development, technology is also defined new methods and techniques in respect to its needs. With a massive number of concepts, the search has been taken to understand development, change, and future. Now, the coming conflict between humanity and machine or technology evokes the post-human age. In the simplest terms, the digital age has come. In this sense, in the new digital age, we see that there are artificial intelligence and optimization applications from healthcare[5] to production systems[6] to all service systems. For example, context-aware negotiating agents can acquire higher benefits for both themselves and social welfare [7].

ABSTRACT. Science philosophical principles are not thought in many universities all over the wqorld, but instead scientific principles are thought mechanically almost in university education, and as a consequence rather than breake through scientific innovative methods most often traditional paper publising takes place. In this paper, the fundamental principles of thinking and possible relationships among different thought products are presented explicitly. In the first sections mention has concentrated on different thinking stages and then after the propositional inferences toward final knowledge generations are specified. Additionally, different sub-sections within the same thinking system are also mentioned. The primary purpose from such approach is to reach at the end to scientific information through the systematic thinking, propositions and logical inferences and also to bring them into wide spread acceptable forms. Science philosophical thinking success is related to geometry (shape) in real and imaginary world, which are then converted to language, expert view principles and at the end, if necessary, mathematics, which is backbone topic in engineering education system. In this paper as the career the medicine is taken as basic concern where verbal (logical propositions) play the most significant role and therefore to discover the disease through diagnosis and to reach treatment philosophy and especially fuzzy logic principles play the most significant role. The backbone of this paper will depend on the Science Philosophy and Medicine courses given at Istanbul Medipol University.

ABSTRACT. In this presentation the importance of knowledge in the latest digital transformation will be elaborated and a general framework for KM for industry 4.0 implementations will be described. As clearly indicated by respective scientific studies the Industry 4.0 motivates the data driven society to move towards the knowledge driven one. To be successful and operative within industry 4.0 based enterprises, there is still a need for modelling and managing the respective managerial and operational knowledge. Not only the data driven knowledge generation and manipulation is necessary but also, behavior based models would be inevitable for unmanned smart systems. The level of intelligence and smartness would be heavily depends upon the capability of generating, utilizing and assessing both scientific and field based knowledge artifacts.

ABSTRACT. After the COVID-19 epidemic, which has affected the whole world in many ways since the beginning of 2020, turned into a pandemic, it was seen how important rational rapid diagnostic tests, developing treatment algorithms, of course the prevention measures to be taken and innovative studies for them. The world of science, which is in a serious race, has sought ways to benefit from artificial intelligence, which is today's technology, in many areas for the speed and quality needed to cope with the pandemic. After all; Artificial intelligence, which can simply be defined as the imitation of human intelligence, is increasingly used in other branches, especially in laboratory medicine. The need for artificial intelligence tools has become more evident in managing the increasing burden in health systems after the COVID-19 pandemic. In order to control the pandemic, artificial intelligence applications have become used in many areas from estimating the epidemiological course of the disease to the development of different diagnostic tools, from modeling the virus to treatment algorithms. Various models have been developed to more easily identify probable and asymptomatic patients, contact tracing, identify risk situations, and even determine disease progression (how the disease will progress and end) with artificial intelligence tools developed using clinical laboratory results obtained from patients with SARS-CoV-2 infection. Along with all these developments, there are some limitations regarding the use of artificial intelligence. It is seen that there are deficiencies in accessing appropriate data, validation of developed models and multidisciplinary approach. The issues of data privacy and ethical use of artificial intelligence are also gaining importance. Although artificial intelligence has some such limitations, the benefits it will provide; It is clear that its social and economic gains, its ability to predict high will also enable it to produce supportive information and develop policies at all levels of health services.

ABSTRACT. Every sector in the digital era is becoming more digitised, data-driven and automated through the development of advanced data analytics, artificial intelligence and machine learning methods as well as digital technologies. Recent pandemic due to Covid-19 has demonstrated that the data can save lives as well as cost lives. The value of data has therefore become more apparent in several aspects from the discovery of vaccine and treatment regimens to the identification and isolation of individuals and populations who have been infected with Covid-19.

As the success of the data analytics, artificial intelligence and machine learning methods and digital transformation in health sector depends on the data and its capability, the organisations need to be collecting and accessing to every piece of data locally and globally, and harnessing the data more wisely.

The talk will cover the assessment of data capability and practical aspect of data-driven solutions in the age of Covid-19 and beyond along with future challenges and potential solutions.

ABSTRACT. Development and implementation of science, technology, and innovation policies are important in today's globally competitive environment. The monitoring and management of these policies are essential for the achievement of sustainable development goals. In order to reach these goals, objective data sources have a key role to analyze technology trends and detect technology gaps. Patents, projects, and academic studies are the main objective data sources. These data sources can be analyzed by using several approaches. Data mining is a useful tool to analyze collected data from these sources for researchers and investors. In this study, recent studies using data mining techniques on technology management have been analyzed and discussed in terms of the used methods, developed methodologies, and applied sectors. Additionally, the development of the existing research field over the years is analyzed by using S-curve. Trends related to the technology management field are determined in this study. Analysis of these trends is especially crucial for academics, companies, and governments. The results of this study provide useful information for academics to monitor the tendency and the development process of the field. Furthermore, this study provides a guide to the authorities for the tools available to prepare, implement, and select sustainable technological development plans for governments.

ABSTRACT. The service systems grow rapidly day by day and reaches a better level. Technological developments and changes along with social life make businesses in the service sector more competitive. Education is growing exponentially as a sector and gaining the status of a full-fledged service industry globally. Since the services provided in higher education have different dynamics than other sectors, it is not possible to measure the service quality with the same concepts and criteria. Developments in the field of technology directly affect people. The expectations and needs of people living in different ages also change depending on the conditions of that age. This difference reveals the differences between generations and generations. The Z generation, which is in the higher education age, is a generation that is intertwined with technology and also called the digital generation, unlike the previous generations. In this study, 6 dimensions and 20 sub-factors were determined to determine the quality of service that generation Z expects from higher education, considering the characteristics and dynamics of this generation. The dimensions used are as follows; "Quality of Administrative Services", “Quality of Academic Services”, "Adequacy of Campus Facilities and Social Life", "Teaching Approach and Method", "Technological Infrastructure", "Online Services", "Career Planning". As a result of the developed scale, the expectations of the students were determined. fuzzy sets have been used because the obtained data are subjective. The data were analyzed using MATLAB.

ABSTRACT. Each business excellence model aims to draw a path for excellence. Research on excellence models has a long tradition. Over the years, the efforts have reached a mature stage. Well-established excellence models are different than (their counterpart) “standards”, and they go beyond them. For example, while the quality management standards describe what to do, their well-known counterpart, the EFQM Excellence Model (the well-known equivalent) describes how should be done as well. Although in recent years there has been an increased interest in international standards on innovation management (e.g. ISO 56000, TS/CEN 16555), excellence in innovation management has not been studied as rigorously and thoroughly in the literature. To address this previously unaccounted phenomenon, and to go beyond these standards, this study proposes an innovation excellence model. This study discusses excellence in corporate innovation by focusing on its three important dimensions in detail; innovation execution system, innovation organization, and innovation engine.

ABSTRACT. Technology roadmapping has been employed for years as an important tool for strategic planning in technology management. It is becoming increasingly decisive for companies to gain a competitive advantage in today’s technology-intensive industries. The latest discussions in technology roadmapping literature are beyond the traditional T-plan roadmapping procedure which is based on a few workshops of experts. A number of techniques have been proposed in the recent literature to modernize technology roadmapping through data-driven approaches using algorithms, fuzzy logic, scenario analysis, and big data. There is still a great deal of work to be done in this area. In this respect, this study proposes a novel approach to augment technology roadmapping by integrating an innovation intelligence process, which is considered to have a complementary effect. This innovation intelligence process incorporates relevant patent and publication data, and its methodology is based on topic modeling and type-2 fuzzy sets. This study illustrates how this augmented technology roadmapping executes by providing a case of unmanned aerial vehicle technologies.

ABSTRACT. Today, the factors affecting the competition between businesses in the global world are increasing more and more over the years. Using the latest technology alone is not enough to stay ahead of the competition. The effect of Research and Development (R&D) activities is quite high in increasing the efficiency of the enterprises in the market. In this study, it is emphasized that using technologies in R&D to increase quality in every activity and every process of the enterprise can provide more competitive advantage. The concept of Quality 4.0 has been explained and how it can be applied in R&D has been investigated. There is no similar study in the literature on Quality 4.0 efficiency in R&D activities. The study is also unique in this respect.

ABSTRACT. Scheduling calculations are made by developing a mathematical model in single machine systems. As the number of machines and work increases, problems such as delivery time, stock cost, and production efficiency arise. Nowadays, meta-heuristic methods are frequently used to solve large-scale scheduling problems. Meta heuristic methods make the solution of the problem easier in terms of both time and ease of operation. Flow shop scheduling is based on the principle of placing the machines in series and all jobs are processed in the same order on the machines.

In this study, the scheduling problem belonging to a company manufacturing in Sakarya is optimized by using the genetic algorithm method and it is aimed to reach the table that gives the smallest time in the production schedules. The production time of 165 minutes was reduced to 103 minutes and a saving of 62 minutes per piece was achieved. The company gained 37.57% of the production time per piece. The desired result has been achieved by being able to produce 5 solenoid parts.

ABSTRACT. It is a strategy developed to improve performance in the cross-shipment distribution network. In cross-shipment, the products are grouped in distribution centers according to customer requests and continue from the point where they left the distribution, the difference from the classical supply chain and inventory model is that the product is deployed in the warehouse without coming to the stock point. In this way, the product reaches its destination faster and the additional inventory cost of the product is eliminated. In this study, the vehicle routing problem, where delivery points are taken into consideration, instead of scheduling trucks in cross-shipment. The vehicle routing problem addressed includes the time and capacity limited vehicle routing problem. In the process of determining the appropriate vehicle route in the system, tempering annealing was used. At the same time, LINDO program was used to solve the mathematical model of the problem with the route determined by simulation annealing.

ABSTRACT. In recent manufacturing environments, assembly lines play a very crucial role to make a bridge between the demand of the customers and products of the manufacturers in order to provide a rapid and online production. The efficiency and flexibility of assembly lines may determine the satisfaction level of the customers and so the survival of a company, eventually. In this study, a new cost-oriented two-sided assembly line balancing problem is introduced. In this problem, an objective function including workstation establishment cost, the salary of operators, and equipment purchasing cost is optimized according to the constraints of the two-sided assembly line balancing problem. Because of the NP-hard nature of the introduced problem, some classical and hybrid meta-heuristic solution approaches are introduced to solve it. Several test problems are solved to evaluate and compare the performances of the proposed solution approaches.

ABSTRACT. The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Three-Dimensional Bin Packing Problem (3DBPP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop three different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a generated 3DBPP dataset to observe the effects of the memory mechanism and the genetic operators separately. The results show that the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm, for solving heterogeneous 3DBPPs.

ABSTRACT. In this study, the nature of technology will be expressed by considering the process from technique to technology that has also gained the content of artificial intelligence. The concept of technology has always been difficult to understand and clarify. In order to make it easy to understand what technology is, we will put forward a new style of explanation based on the relation we wrote between essence, essence / form and matter, and the four reasons that existent owes its existence, in Aristotle's philosophy. In other words, the concept of technology; We will try to reveal the relationship between the concept of technology; considering the relationship between technology product (ore), form information extract (form) and material (matter) for four reasons, we will try to put it out there.

ABSTRACT. On 19th June 2020, Environmental business international [EBI] (EBIonline.org) organized a webinar on "Emerging Technology and The Environmental Industry: 2020-2030". Technology has been a key element in the evolution of the environmental industry in the last 50 years. However, the next 10 years may be the most influential decade with immense potential for Intelligent Manufacturing and Service Systems. How these new applications and developments in information technology, automation, artificial intelligence, remote sensors, and other technologies which would play a significant role the global remediation market. It is predicted that some of the emerging contaminants viz perfluoroalkyl substances [PFAS], 1-4 Dioxane, pharmaceuticals and personal care products, disinfection byproducts in drinking water their treatment technologies Hydrocarbons, Heavy metals, Nuclear waste and Asbestos etc. will attain considerable significance in the coming decade. The great diversity of industrial development scenarios on the planet, together with the intricate network of interactions that connects individuals, society and the environment lead the industry's impacts to manifest themselves in different ways on the planet. This situation signals the importance of sectors such as legal regulation, environmental consultancy and industry to invest in the adoption of sustainable pollution remediation and control practices, to translate the particular socio-environmental complexity of the different parts of the globe in affirmative propositions that favor the balance of the ecological system. Thus, remediation strategies must consider a flexible and resilient approach, which incorporates a duration compatible with the life cycle of the project to be remedied and which minimizes the use of resources, making it sustainable. In this contexts biodiversity driven cleanup appear as an example of a low cost, minimally invasive approach for the remediation of contaminated areas. This keynote aims to address global trends in the remediation sector, with an emphasis on the role of phytoremediation as a sustainable and wide-ranging strategy in different parts of the globe. Global trends in the remediation industry, seeking to highlight the diversity of existing scenarios that often require the application of efficient strategies, which incorporate the concepts of sustainability and resilience and that are compatible with resource limitations. Thus, recent research trends in remediation employing Internet of Things [IoT] with emphasis on bioremediation and pollution/contamination monitoring and control strategies currently applied in the real world are discussed.

ABSTRACT. Ambient assisted living (AAL) was emerged in 1990s. It has attracted much attention in research since mid-2000s due to: (1) demand for better quality of life, especially for elderly people, and (2) expectation of improved social and health care. AAL research has gained more and more attentions in recent years due to the growing aging population and advances in such realms as information & communication technology (ICT) and ambient intelligence (AmI). It aims at enhancing the quality of life and offering user-friendly interfaces such that persons/users can live and work in a better environment, especially for the elderly community. In general, the unnoticed assisted system will capture the users’ activities, provide appropriate real-time feedbacks or optimized solutions to relevant users through the intelligent user-friendly interactions in the home and outside. The achievement of AAL relies heavily on AmI to support the accessibility of diverse technologies in surrounding area, which are more complex than the simple devices service, and ICT to provide AAL with a vast array of new communication capabilities. In this regard, the contributing technologies and some research issues could be addressed to highlight the opportunities and challenges of AAL applications in such areas as smart health care and smart workplace. Apart from the above, examples of research relevant to AAL will be introduced and briefly discussed. It is envisaged that with more in-depth investigation into AAL, more rational applications, which enhance the quality of life and offer user-friendly interfaces, can be realized.

ABSTRACT. The use of magnetic resonance images (MRI) is a frequently used tool in disease detection. The use of healthcare professionals to examine MRI images and to identify diseases are among traditional methods. Therefore, one way to improve clinical healthcare is to present and analyze medical images more efficiently and intelligently. Brain tumors can be of different types, and accordingly, they can cause serious health problems in adults and children. Such bulks can occur anywhere in the brain in different sizes and densities. This is not a standardized situation due to its nature. The diagnoses are revealed by the experts by analyzing the tumor images manually. In the proposed model, it is aimed at automating the process and reducing human errors in the system. The model is based on the deep learning technique, which is a probabilistic neural network to identify unwanted masses in the brain. In this study, a model has been created with VGG and CNN (Convolutional Neural Network) architectures, which are among the deep learning techniques. The performance values of the model outputs, accuracy, error rates and specificity separators are discussed comparatively.

ABSTRACT. Digital transformation is necessitating remarkable changes in developing business software applications. Traditional software development process is not sufficient enough to deal with the requirements. Business functions are to be carried out with little human intervention. Respective software should be designed with self-decision making ability. Agile and extreme programming becoming more and more dominate in software industry. Transforming the traditional software to self-decision making systems is becoming the main issue for software industry. This paper presents a general framework for enabling easy implementation of this process. Note that, the general framework proposed is based on software mock-up and utilization of new software development approaches such as micro service architecture or component based software development processes. The process starts with defining requirements through need analysis, management of the expertise to cope with those requirements, traditional software blocks, intelligent interfaces, autonomy software add-ins etc. The paper provides a conceptual model with respective explanations. It is believed that the proposed model become a guideline for the IT sector which faces a rapid transformation.

ABSTRACT. Although robots and other technologies replace manpower in today's business world with digitalization, the human factor is still one of the most important productivity and efficiency factors for many businesses. Managers have to consider many factors and constraints while selecting the human resources to be employed in their companies. Because the selection of the most suitable person with the necessary qualifications for the job is also an effective factor on the competitiveness of the company.

The personnel selection process is also linked to the experts of the companies who will make the selection. Determining the criteria to be used in the evaluation of candidates and making decisions according to the criteria can make decision-making in the processes more difficult. In this study, it is aimed to determine the priority criteria, which is a difficult stage in personnel selection. For this purpose, fuzzy dematel and grey based dematel methods, which are among the modern approaches, have been used.

ABSTRACT. PROMETHEE is an advanced multi-criteria decision-making method specifically used for outranking the actions or alternatives. The vital part of the method is to assign the most suitable preference functions to the criteria. On the other hand, this part is also the weakest link in the method since there is not much information about it in the literature, although a considerable amount of research has employed Promethee as an outranking method. This paper presents a comforting approach (CAPP) for matching preference functions with available criteria when using the PROMETHEE method. The proposed approach provides consistency and decision support to the researcher during the function selection. For instance, evaluation criteria are determined and weighed to compare Hi-Fi audio systems on the market. CAPP is then put into practice to determine the convenient preference functions matching criteria for assessing Hi-Fi audio systems. Two new preference functions also are developed and presented in the context of this work.

ABSTRACT. Problems such as customer diversity, excess demand, and, consequently, supply shortage or excess, which have been experienced in the production sector in recent years, require the intensive use of technology in this sector. In order to overcome these problems, the concept of Industry 4.0, which is a new era in the industry, is emerging. In this new era in which cyber-physical system components such as the Internet of Things, Cloud Computing, Big Data, and Artificial Intelligence are used, businesses have shown an abstention approach. On the contrary, Industry 4.0, which offers many possibilities, is a very beneficial innovation for businesses. In the research, Industry 4.0 benefits are categorized by examining the academic literature they will bring, and these are determined as criteria for the reason for the transformation of businesses. Consequently, 9 criteria are determined. For this reason, in this study, a multi criteria decision making method is used according to the preferences of the relevant 4 respondents. Criterion weights are obtained with Interval Valued Pythagorean Fuzzy AHP methods. In conclusion, it is seen that the weight of Meeting Individual Customer Requirements criterion is the highest in Interval Valued Phytagorean Fuzzy AHP.

ABSTRACT. With the fourth industrial revolution, the predictive maintenance, which has been replaced traditional maintenance, has been popular in recent years. Unlike traditional maintenance, predictive maintenance monitors equipment periodically by analyzing data collected from the equipment. The main objective of predictive maintenance is to predict problems on the equipment that may lead to stops and is to maximize utilization of machine/equipment. To achieve these aims, predictive maintenance builds a detailed maintenance program. In this study, we analyzed the failures of a specific machine for a time period. Machine learning algorithms were utilized in order to predict the current condition of the machine. Additionally, we suggest a few maintenance programs as different from the literature. The maintenance period is determined considering classical approaches such as Weibull analysis. However, machine learning algorithms can predict failures before they occur. In this case, a proactive system makes a decision to minimize machine stops. We perform a real-life case study and present our results.

ABSTRACT. The availability of timely data presents an opportunity for now casting of many lagged economic and noneconomic indicators. From this perspective, data describing/summarizing online activities of the internet users can be utilized as data sources of these nowcasts. This study attempts to predict Consumer Confidence Index (CCI) by considering the related search activities of people. We assume that the expenditure intentions of the people can be seen in the corresponding internet search activities. Therefore, in this study, we use Google Trends service to discover the volume of search activities for some set of queries. In Google Trends, the search volumes are scaled between 0 and 100 by considering relative size of search. While “100” represents the highest popularity, “Zero” implies that there is not enough data for the relevant term. The data set employed in this study covers the outputs of the Google Trends queries about the selected drivers of CCI (Turkish lira US dollar exchange rate, Unemployment rate, Election and Court Case between January, 2004 and December, 2019). We use monthly query volumes of these selected drivers as the inputs of an Artificial Neural Network (ANN) while Official CCI is our output. The result shows high correlation between official CCI and predicted CCI. Since the Google Trends queries are easily accessible and free, this approach can be evaluated as a simple way to predict CCI. The methodology of this study can also be utilized for now casting economic variables.

ABSTRACT. Worm gears are power and motion transmission systems that are frequently used in industrial machines and many other engineering applications. In order for these gearboxes to work properly and to avoid any fault in the machine they are used in, their maintenance must be followed carefully. In this study, the faults in the worm gearbox (WG) were detected by Artificial Neural Network (ANN) based on different data sets. The performances of ANN based classifiers developed by using data sets established from features obtained from vibration data, sound data and thermal images were compared. It has been observed that the most successful ANN classifier is the classifier using all three data sets.

ABSTRACT. Worm gearboxes (WG) are used in many machines for industrial purposes. Therefore, it is important to detect and prevent faults in WGs for efficient operations of the machines. In this study, vibration data, sound data and thermal images collected from a test rig developed to detect faults of WGs were analysed. In order to discriminate the faults occurred due to several environmental factors, a classification methodology is designed. Feature extraction step is applied for each data set and then the data are classified with k-nearest neighbour (k-NN) and support vector machine (SVM) algorithms. The fault classification performances of k-NN and SVM algorithms were presented comparatively. Although the accuracy rates are close, it is observed that SVM performs better than the k-NN for fault classification problems of WGs.

ABSTRACT. In bus fleets, vehicles must be operational and have a high availability rate. Customers who do not receive the expected service lead to serious customer dissatisfaction and cost loss. The expected service is not provided due to breakdowns and problems during periodic maintenance. During periodic and breakdown maintenance, parts that faulty or complete their life time must be replaced with a new one. Spare parts needed during maintenance must be in stock on time and in the required amount. The maintenance-repair department has thousands of spare parts to manage due to different model-type vehicles from different manufacturers. In order for the maintenance-repair department to operate productive, this case reveals the need to accurately forecast the need for spare parts. Otherwise, the productivity of the maintenance-repair department will decrease and the vehicles will not be able to perform their planned duties. Forecasting methods are divided into two as qualitative and quantitative. Qualitative method is a forecasting method carried out by the expert based on own experience and intuiton. Quantitative method is a forecasting method in which mathematical models are carried out by making statistical calculations using historical data. Artificial Neural Networks (ANN), which is a quantitative method, is an artificial intelligence technique that can produce new information based on the ability of the human brain to produce information through learning. Another method, Support Vector Regression (SVR), is a forecasting method that tries to determine the regression function that will minimize the expected error based on the statistical learning theory. In this study, demand forecasting was carried out with ANN and SVR for critical spare parts determined in a urban transport bus fleet. The developed model includes critical variables such as the number of vehicles in the fleet, the number of breakdowns that cause parts to change, and the life time of parts.The application was carried out with real data of the last three years.The predicted results carried out with ANN and SVR were compared.