Görüntü işleme tekniği kullanılarak gerçek zamanlı hareketli görüntü tanıma

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Görüntü işleme; sayısal olarak alınan görüntülerin işlenerek özelliklerinin ve yapılarının değiştirilmesini, geliştirilmesini ve bu görüntüler vasıtasıyla analizlerin yapılmasını sağlayan teknolojidir. Modern teknoloji, herhangi bir görüntünün (fotoğraf veya video) girdi olarak kullanılarak istenilen özellikte bir başka görüntünün veya girdi olarak kullanılan görüntü ile ilgili verilerin elde edilmesini mümkün kılmaktadır. Görüntü işleme ile bir görüntünün rengi, parlaklığı, boyutu, yapısı vb. özellikleri uygun yazılımlar kullanılarak değiştirilebilir, geliştirilebilir ve analiz edilebilir.Bu yazılımlar, dijital ortama aktarılan görüntülerdeki bozuklukların giderilmesi ve daha kaliteli görüntü almak için kullanılabileceği gibi nesnelerin tanımlanması, hareketli ve hareketsiz nesnelerin ayrıştırılması gibi birçok amaç için de kullanılabilir. Farklı formatlarda görüntülerin kullanıldığı her sektöre uygun çözümlerin üretilmesini sağlayan görüntü işleme; güvenlikten astronomiye, savunma sanayisinden kalite kontrolüne kadar sayısız alanda kullanılabilir.Bu çalışmada, kamera kullanılarak cihazdan alınan ardışık görüntülerde hareketin analizi için kullanılan yöntemler incelenip uygulanarak; elde edilen sonuçlar tartışılmıştır. Bu yöntemler arka plan farkı yöntemleri ve istatiksel yöntemler olmak üzere iki kategori de incelenmiştir. Uygulamada görüntüler arasında piksel farkları karşılaştırılarak gerçek zamanlı bir güvenlik uygulaması gerçekleştirilmiştir.Image Processing is a technology that image properties and structures are processed with changes, improvement for data analysis. The modern technology provide important data with using photo and video inputs. Using image processing an image color, brightness, dimensions and any other structure can be changed, improved and made analysis by using appropriate softwares.These software are used for the image reconstruction which are transferred to digital athmosphere. These images have some spoiled parts and for this we can rearrange these photos with imag processing. Image processing is used in security, astronomy, defensive,.industry.and.quaility.control.centers.In this thesis, methods that are widely used fort he motion detection in a moving picture sequence, were studied and implemented, and their performances were discussed. These methods were investigated under two major headings namely background subtraction methods and statistical methods. In this application I have compared pixel differences between images, for appliying security application

Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

ABSTRACT Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD

A new fuzzy logic based career guidance system: WEB-CGS

Izbor zanimanja na mnogo načina uvelike utječe na društveni život pojedinaca. Ipak, izbor odgovarajuće karijere postaje sve teži kad se uzme u obzir postojanje sve većeg broja zanimanja i mogućnosti usmjeravanja. Shodno tome, sve je veća važnost profesionalnog usmjeravanja. U ovom se radu razvija sustav pomoću kojega se automatski može ponuditi profesionalno usmjeravanje. To je WEB-CGS (web-based carrier guidance system) koji funkcionira kao web usluga koja se zasniva na neizrazitoj (fuzzy) logici. Cilj je olakšati pojedincu izbor odgovarajućeg zanimanja. U tom sustavu integrirani su prethodni uspjesi u obrazovanju učenika i mišljenja nastavnika te je omogućeno prepoznavanje profesionalnih interesa i mogućnosti učenika. Sustav predviđa interes učenika za usmjeravanje u području informacijske tehnologije, elektrike-elektronike, računovodstva i industrije automobila. Postignuti su obećavajući rezultati usmjeravanja za 300 neopredijeljenih studenata 9. razreda u strukovnoj srednjoj školi.Choosing a career affects individuals’ social life deeply in terms of many dimensions. However, choosing the right career is becoming increasingly difficult given the existence of an increasing number of professions and training opportunities. Consequently, the importance of career orientation increases. In this study, a system that can automatically offer vocational guidance has been developed. This new system is referred to as WEB-CGS (web-based carrier guidance system) and works as a fuzzy logic based web service. The aim is that it will make it easier for an individual to choose the right profession. In this system, students’ prior educational successes and teachers’ views were integrated in a manner which made it possible to identify the students’ professional interests and capacities. The system forecasts vocational school students’ interest with regard to Information Technology, Electrics-Electronics, Accounting, and Automotive. Promising results were obtained with regard to 300 unbiased 9th grade students in terms of orienting them towards an appropriate profession

Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach

Background: Identifying and validating biomarkers' scores of polymorphic bands are important for studies related to the molecular diversity of pathogens. Although these validations provide more relevant results, the experiments are very complex and time-consuming. Besides rapid identification of plant pathogens causing disease, assessing genetic diversity and pathotype formation using automated soft computing methods are advantageous in terms of following genetic variation of pathogens on plants. In the present study, artificial neural network (ANN) as a soft computing method was applied to classify plant pathogen types and fungicide susceptibilities using the presenceabsence of certain sequence markers as predictive features. Results: A plant pathogen, causing downy mildewdisease on cucurbitswas considered as amodelmicroorganism. Significant accuracy was achieved with particle swarm optimization (PSO) trained ANNs. Conclusions: This pioneer study for estimation of pathogen properties using molecularmarkers demonstrates that neural networks achieve good performance for the proposed application

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

A GPU accelerated hybrid GA-SVM for large scale datasets: Cu-GA-SVM

In this study, CUDA based speed optimization of a hybrid method consisting of Genetic Algorithm and Support Vector Machines has been performed. In machine learning, it is aimed to achieve high accuracy values from the developed methods. It is also a target for the proposed algorithm to work quickly while finding the results. In this study, speed parameter which is indispensable especially in real time applications is taken into consideration and a new GPU technology is used to classify the data quickly. Therefore, CUDA programming, which allows us to program on graphics processors of which importance and use are increasing in recent years, has been benefited from. Support vector machine optimized by genetic algorithm has been used as the classification algorithm. The experiments have been performed on a computer with NVIDIA GeForce 940MX graphics card, which consists of 384 CUDA core. Experiments performed on large scale data sets have shown that CUDA programming has positive effects on the results. In this way, the infrastructure of a quick system for real-time applications can be created by using the graphics processors in the classification phase of the machine learning applications

Using Artificial Intelligence Techniques for Large Scale Set Partitioning Problems

AbstractSet partitioning problems are among NP-Hard problems due to their complexities. It is difficult to prepare an algorithm that will give a precise solution in these types of problems which are difficult to solve. This study proposes a genetic algorithm based approach among artificial intelligence optimization algorithms in order to find simpler solutions to set partitioning problems. The proposed method was applied to the solution of problem of partitioning the 53 teams in the Turkish Third League into 5 subsets. The distribution of the teams into subsets was undertaken with the aim of minimizing the travel costs and the travel fatigue and preventing the subjective distinctions in the determination of subsets. This study was carried out for 2009-2010 football season and it includes a comparison of the plan prepared by Turkish Football Federation with the plan obtained through the study. The application software was developed by using C# programming language of .Net technologies

COMBUSTION PROPERTIES OF EUCALYPTUS WOOD

In this study, the combustion properties of some impregnation materials (abiotic and biotic factors) used for eucalyptus wood in interior or exterior environments were investigated. The experimental samples were prepared from Eucalyptus wood based on ASTM-D-1413-76 Tanalith-CBC, boric acid, borax, vacsol-WR, immersol-WR, polyethylen glycole-400 and ammonium sulphate were used as an impregnation material. The results indicated that, vacuum treatment on Eucalyptus gave the lowest retention value of salts. Compounds containing boron+salt increased fire resistance however water repellents decreased the wood flammability

Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

Parkinson’s disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD

Soft computing model on genetic diversity and pathotype differentiation of pathogens: A novel approach

Background: Identifying and validating biomarkers' scores of polymorphic bands are important for studies related to the molecular diversity of pathogens. Although these validations provide more relevant results, the experiments are very complex and time-consuming. Besides rapid identification of plant pathogens causing disease, assessing genetic diversity and pathotype formation using automated soft computing methods are advantageous in terms of following genetic variation of pathogens on plants. In the present study, artificial neural network (ANN) as a soft computing method was applied to classify plant pathogen types and fungicide susceptibilities using the presence/absence of certain sequence markers as predictive features. Results: A plant pathogen, causing downy mildew disease on cucurbits was considered as a model microorganism. Significant accuracy was achieved with particle swarm optimization (PSO) trained ANNs. Conclusions: This pioneer study for estimation of pathogen properties using molecular markers demonstrates that neural networks achieve good performance for the proposed application