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Bu makale, ağ analizi ve görselleştirme yoluyla ilaç-ilaç etkileşimlerinin karmaşıklığını araştırmaktadır. İlaç-ilaç etkileşimlerini analiz etmek ve ilaçlar arasındaki ilişkileri keşfederek etkileşimli bir görselleştirme aracı sağlamak için ağ tabanlı bir yaklaşım sunulmaktadır. Ağ tabanlı yaklaşım, büyük bir ilaç-ilaç etkileşimi veri kümesine uygulanmakta ve ortaya çıkan ağın özelliklerini analiz etmektedir. Ayrıca, ilaç-ilaç etkileşimlerinin daha fazla araştırılması için ağ tabanlı yaklaşımın potansiyeli de tartışılmaktadır. Son olarak, ilaçlar arasındaki ilişkileri keşfetmek için etkileşimli bir görselleştirme aracı sağlayarak ağ tabanlı yaklaşımın etkinliği gösterilmektedir. Bu çalışmanın sonuçları, ilaç-ilaç etkileşimlerinin karmaşıklığının daha iyi anlaşılmasını sağlayacağı öngörülmekte ve ilaç keşfi ve geliştirmede ağ analizi ve görselleştirmenin potansiyel uygulamalarını önermektedir. Aynı zamanda kullanıcıların web uygulamasını ziyaret edebilmeleri ve grafiklerle doğrudan etkileşim kurabilmeleri için Pyvis ağ grafiklerini çevrimiçi olarak https://iuysal1905-streamlit-pyvis-network-app2-91q9sv.streamlit. app adresinde yayınlanmıştır.

Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.

COVID-19, küresel pandemi olarak ilan edilmiş, kritik ve ölümcül bir hastalıktır. Küresel salgın tehlikesinin sürmekte olduğu bu süreçte, pandemi ile yüzleşmek için özel politikalar ve planlar yapmak oldukça önemlidir. COVID-19 doğrulanmış verilerini kullanarak, yapay zeka teknikleri ile tahminlerde bulunmak ise gelecek planlarının geliştirilmesine yardımcı olduğu için önemli bir konudur. Bu çalışmada, Türkiye Sağlık Bakanlığı COVID-19 bilgilendirme sayfasında yayınlanan veriler kullanılarak, uyarlamalı ağ tabanlı bulanık mantık çıkarım sistemi (ANFIS) ve yapay sinir ağları (YSA) ile tahmin modelleri geliştirilmiştir. Bu modeller farklı istatistiksel değerlendirme kriterleri kullanılarak karşılaştırılmıştır. Değerlendirme sonucunda ANFIS modeli 0.0247 ortalama mutlak hata (MAE) değeri, 0.0012 ortalama kare hata (MSE) değeri, 0.0351 kök ortalama kare hata (RMSE) değeri ve 0.9847 regresyon katsayısı R2 sonucu ile en iyi model sonucunu elde etmiştir. Ayrıca ANFIS ile oluşturulan modelin gelecek beş günlük vefat sayılarını %96 doğruluk oranı ile tahmin ettiği görülmüştür.

Fake COVID-19 tweets are dangerous since they are misinformative, completely inaccurate, as threatening the efforts for flattening the pandemic curve. Thus, aside the COVID-19 pandemic, dealing with fake news and myths about the virus constitute an infodemic issue, which must be tackled by ensuring only valid information. In this context, this study proposed the Synthetic Minority Over-Sampling Technique (SMOTE) and the classifier vote ensemble (SCLAVOEM) method as a fake news classifier and a hyper parameter optimization approach for predictive modelling of COVID-19 infodemic tweets. Hyper parameter optimization variables were deployed across specific points of the proposed model and a minority oversampling of training sets was applied within imbalanced class representations. Experimental applications by the SCLAVOEM for COVID-19 infodemic prediction returned 0.999 and 1.000 weighted averages for F-measure and area under curve (AUC), respectively. Thanks to the SMOTE, the performance increases of 3.74 and 1.11%; 5.05 and 0.29%; 4.59 and 8.05% was seen in three different data sets. Eventually, the SCLAVOEM provided a framework for predictive detecting 'fake tweets' and three classifiers: 'positive', 'negative' and 'click-trap' (piege a clics). It is thought that the model will automatically flag fake information on Twitter, hence protecting the public from inaccurate and information overload.

Since the start of the current century, artificial intelligence has gone through critical advances improving the capabilities of intelligent systems. Especially machine learning has changed remarkably and caused the rise of deep learning. Deep learning shows cutting-edge results in terms of even the most advanced, difficult problems. However, that includes a trade-off in terms of interpretability. Although traditional machine learning techniques employ interpretable working mechanisms, hybrid systems and deep learning models are black-box being beyond our understanding capabilities. So, the need for making such systems understandable, additional methods by explainable artificial intelligence (XAI) has been widely developed in last years. In this sense, this study purposes a Convolutional Neural Networks (CNN) model, which runs a new form of Grad-CAM. As providing numerical feedback in addition to the default Grad-CAM, the numerical Grad-CAM (numGrad-CAM) was used within the developed CNN model, in order to have an explainability interface for brain tumor diagnosis. In detail, the numGrad-CAM-CNN model was evaluated via technical and physicians-oriented (human-side) evaluations. The model provided average findings of 97.11% accuracy, 95.58% sensitivity, and 96.81% specificity for the target brain tumor diagnosis setup. Additionally, numGrad-CAM integration provided 90.11% accuracy according to the other CAM variations in the same CNN model. The physicians used the numGrad-CAM-CNN model gave positive responses in terms of using the model for an explainable (and safe) diagnosis decision-making perspective for brain tumors.

Günümüzde teknolojinin hızla ilerlemesi ile birlikte yapay zekâ yöntemleri de birçok alanda sıklıkla kullanılmaktadır. Yapay zekanın önemli kullanım alanlarından birisi de sağlık sektörüdür. Sağlık sektöründe erken teşhis, insan kaynaklı hataların minimuma indirilmesi gibi birçok durumda yapay zekâ yöntemleri kullanılmaktadır. Çalışmada açık kaynak erişimli internet sitesinden (kaggle.com) elde edilen 127710 adet EKG sinyallerine ait veri seti kullanılmıştır. Veri seti 100.710 adet eğitim, 1.500 adet veri de test ve kalan 25.000 adet veri ise doğrulama verisi olarak kullanılmıştır. Eğitim verileri için tasarlanan CNN modeli normal sinüs ritmi, supraventriküler erken atım, erken ventriküler kasılma, ventriküler ve normal atımın karışımı ve sınıflandırılamayan atım olmak üzere toplam beş sınıf için eğitilmiştir. Tasarlanan CNN modelinde hata oranı %5,3, duyarlık oranı %94,4, hassasiyet oranı %94,6, F-değeri ise %94,4 ve %94,7 doğruluk oranı olmak üzere beş farklı performans kriterine göre değerlendirilmiştir., Today, with the fast advancement of technology, artificial intelligence methods are frequently used in many areas. One of the important uses of artificial intelligence is the health sector. Artificial intelligence methods are used in many situations such as early diagnosis and minimization of human-induced errors in the health sector. In the study, the data set of 127710 ECG signals obtained from an open source website (kaggle.com) was used. The data set was used as 100,710 training data, 1,500 data as test and the remaining 25,000 data as validation data. Designed for training data, the CNN model was trained for a total of five classes: normal sinus rhythm, supraventricular premature beats, premature ventricular contraction, mixed ventricular and normal beats, unclassified beats. In the designed CNN model, the error rate 5.3%, the sensitivity rate 94.4%, the specificity 94.6%, the F-value 94.4% and accuracy 94.7% was evaluated according to five different performance criteria.

Although the 21st Century is a time period in which the innovative solutions of Artificial Intelligence are felt intensely in daily life, it is engraved in the memories as a rapidly advancing century under the leadership by Artificial Intelligence based technologies. While Artificial Intelligence continue to build the future of humanity and the world with autonomous intelligent systems, they also bring various anxieties. Especially, it is a matter of curiosity how ethical and moral factors pushing people to paradoxical situations will be evaluated by intelligent systems, and it is often discussed whether such systems will be a threat for human life. Based on the explanations so far, objective of this study is to discuss various transformation processes and also recent paradigm changes that may be important for enterprises of the future, by considering the scope of Ethical Artificial Intelligence. In this context, general information regarding essentials of Artificial Intelligence and its applications in enterprises were given first, and then possible problems on ethical scope and solution suggestions were discussed. It is thought that this study will be a reference for Artificial Intelligence applications in enterprises of the future, and its management in the related context., 21. Yüzyıl, Yapay Zeka’nın yenilikçi çözümlerinin günlük hayatta yoğun bir şekilde hissedildiği bir zaman periyodu olmakla birlikte, Yapay Zeka tabanlı teknolojilerin önderliğinde hızla ilerleyen bir yüzyıl olarak hafızalara kazınmış durumdadır. Yapay Zeka insanlığın ve dünyanın geleceğini otonom zeki sistemler üzerinde inşa etmeye devam etmekle beraber, çeşitli endişeleri de beraberinde getirmektedir. Özellikle insanları da paradoksal durumlara iten etik ve ahlaki unsurların zeki sistemler tarafından nasıl değerlendirileceği merak konusu olmakta; hatta bu tür sistemlerin insan hayatına karşı tehdit taşıyıp taşımayacakları da sıklıkla tartışılmaktadır. Açıklamalardan hareketle bu çalışmanın amacı, Yapay Zeka Etiği ölçeğinde geleceğin işletmeleri açısından önem arz edebilecek çeşitli dönüşüm süreçlerini ve aynı zamanda güncel paradigma değişikliklerini ele almaktır. Bu bağlamda, öncelikli olarak Yapay Zeka’nın temellerine ve işletmeler tarafında nasıl uygulandığına yönelik genel bilgiler verilmiş, akabinde etik ölçekte olası problemler ve çözüm önerileri üzerine tartışılmıştır. Çalışmanın geleceğin işletmelerinde Yapay Zeka uygulamalarına ve Yapay Zeka’nın bu çerçevede yönetimine ilişkin çalışmalara ışık tutacağı düşünülmektedir.

Iron (Fe) chelating medicines and Histone deacetylase (HDAC) inhibitors are two therapy options for hereditary Friedreich's Ataxia that have been shown to improve clinical results (FA). Fe chelation molecules can be used to minimize the quantity of stored Fe, and HDAC inhibitors can be used to boost the expression of the Frataxin (FXN) gene in the process of enhancing FA. A complete quantitative structure-activity relationship (QSAR) search of inhibitors from the ChEMBL database is reported in this paper, which includes 437 compounds for Fe chelation and 1,354 compounds for HDAC inhibitors. For further investigation, the IC50 was chosen as the unit of bioactivity, and following data refinement, a final dataset of 436 and 1,163 compounds for Fe chelation and HDAC inhibition, respectively, was produced. The Random Forest (RF) technique was used to generate models, and the models created using the PubChem fingerprint were the strongest of the 12 fingerprint kinds, hence that feature was chosen for interpretation. The results showed the importance of properties related to nitrogen-containing functional groups and aromatic rings. As a result, we explained the effect of the molecular fingerprints used on the models and therefore the effects on possible drugs that can be developed for FA with artificial intelligence (XAI), which can be explained through SHAP (Shapley Additive Explanations) values. Model scripts and fingerprinting methods are also available at https://github.com/tissueandcells/XAI.

The aim of this study is to examine negative effects of using WhatsApp social network application in educational processes and to create a conceptual framework in this manner. In order to examine the literature related to the research topic, sources in different databases were evaluated and research works after especially 2010 have been considered accordingly. A holistic framework regarding negative results of using WhatsApp in education has been tried to be established by examining the sources in detail. There is currently not too many scientific research works on benefits and disadvantages of using WhatsApp in long-term. But since this application has been accepted by many researchers as an instant messaging approach, it appears to be a small number of studies analyzing the effects of over-use of instant messaging take place in the associated literature. When the data obtained within this research is examined it is possible to express that the students addicted to instant messaging are inadequate in academic processes, they cannot control the time spent for messaging, they neglect their homework and they are also less disciplined compared to other students. Additionally, it is also seen these students’ comprehension levels, learning skills, productivity and academic achievements have been affected negatively. It is also remarkable that students using instant messaging during reading tasks can increase the time required for completing reading, and may even feel sleepiness during daytime.

Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.

Cybersecurity is a very emerging field that protects systems, networks, and data from digital attacks. With the increase in the scale of the Internet and the evolution of cyber attacks, developing novel cybersecurity tools has become important, particularly for Internet of things (IoT) networks. This paper provides a systematic review of the application of deep learning (DL) approaches for cybersecurity. This paper provides a short description of DL methods which is used in cybersecurity, including deep belief networks, generative adversarial networks, recurrent neural networks, and others. Next, we illustrate the differences between shallow learning and DL. Moreover, a discussion is provided on the currently prevailing cyber-attacks in IoT and other networks, and the effectiveness of DL methods to manage these attacks. Besides, this paper describes studies that highlight the DL technique, cybersecurity applications, and the source of datasets. Next, a discussion is provided on the feasibility of DL systems for malware detection and classification, intrusion detection, and other frequent cyber-attacks, including identifying file type, spam, and network traffic. Our review indicates that high classification accuracy of 99.72% is obtained by restricted Boltzmann machine (RBM) when applied to a custom dataset, while long short-term memory (LSTM) achieves an accuracy of 99.80% for KDD Cup 99 dataset. Finally, this article discusses the importance of cybersecurity for reliable and practicable IoT-driven healthcare systems., 14 Pages, 8 Figures

Teknolojinin geçmişe nazaran büyük bir hızla geliştiği günümüzde, çoğu alanda yazılımların, yapay zekâ tekniklerinin uygulama alanı artmış insanların işlerini kolaylaştırıcı bir özellik haline gelmiştir. Bu açıdan birçok alanda faaliyet gösteren makine öğrenmesi tekniklerinin sıklıkla rastlandığı problem alanlarından biri sağlık alanı olmuştur. Beynin davranışlarında rastlanan bozuklukların incelenmesinde ve modellenmesinde kullanılan EEG sinyalleri, dünya çapında birçok insanı etkileyen epilepsi hastalığının tanılanmasında temel bilgi kaynağı haline gelmiştir. Bu çalışma kapsamında da epilepsi hastalarından alınan EEG sinyalleri, 500 farklı kişi bilgisini içeren zaman serisi verisi olarak kullanılmıştır. Sınıflandırılan veri setinin bölümlendirme işlemi k-fold cross validation yöntemi ile ayrılmıştır. Epileptik nöbet tanılama sınıflandırma işlemi için kullanılan makine öğrenme tekniklerinden elde edilen sonuçlar ise YSA’dan %76.39, LDA’dan %82.24, K-NN’den ise %93.41 olduğu görülmüştür., In today's world, where technology has developed rapidly compared to the past, the application area of software and artificial intelligence techniques in most areas has become a feature that facilitates the work of people. In this regard one of the problem areas where machine learning techniques operating in many fields are frequently encountered has been the field of health. EEG signals, which are used in the study and modeling of disorders in the brain's behavior, have become the main source of information in the diagnosis of epilepsy diseases that affect many people worldwide. In this study, EEG signals obtained from epilepsy patients were used as time series data containing 500 different person information. The segmentation operation of the classified data set is divided into with the k-fold cross validation method. The results obtained from the machine learning techniques used for the epileptic seizure diagnosis classification process were 76.39% from the ANN, 82.24% from the LDA, and 93.41% from the K-NN.

The aim of this study is to design and develop of drum granulator used in compost fertilizer production sectors. The production capacity of the designed drum granulator is approximately 10 tons/h and the drum speed is 11 rpm. With the designed nozzle system of the drum granulator, humidification process is carried out by adding water to the material fed to the granulator. The total length of the drum is 8 m and the drum diameter is 2 m. The drum is gear driven and has a tilt angle of 2–3°. 2–4 mm granules with a moisture content of about 60–65% (for fertilizer) are obtained in the drum granulator. The trial process of the granulator was completed and RD PVC conveyor belt assembly preventing the material from sticking to the drum surface, drum inlet design preventing overflow problems at 2.5° drum inclination angle, outlet design preventing the material to be clogged when leaving the drum. Furthermore, a system adjusting the amount of water given according to the weight of the material fed has been made to reduce the rejection rate. This system has made a significant contribution to the granule production process.

WOS: 000392712900002 In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA) and Cognitive Development Optimization Algorithm (CoDOA), have been used to solve some multidisciplinary optimization problems provided in the source book Thomas' Calculus 11th Edition and the obtained results have compared with classical optimization solutions.

Artificial Intelligence is known as one of the most effective research field of nowadays and the future. But rapid rise of Artificial Intelligence and its potential to solve all real world problems autonomously, it has caused also several anxieties. Some scientists think that intelligent systems can reach to a level, which is dangerous for the humankind so because of that some precautions should be taken. So, many sub-research fields like Machine Ethics or Artificial Intelligence Safety have appeared in time. In the context of the explanations so far, objective of this study is to suggest a secure intelligent model including human control and also precautions based on intelligent agent and Machine Learning. In the study, essential subjects regarding Artificial Intelligence Safety and details of the suggested model have been provided and also some evaluations about its potential have been done. It is though that this model can be an inspiration for safe Artificial Intelligence systems of the future., Yapay Zekâ, gerek günümüz, gerekse geleceğin en etkin araştırma alanlarından birisi olarak bilinmektedir. Ancak Yapay Zekâ’nın hızlı yükselişi ve otonom bir şekilde bütün gerçek dünya problemlerini çözebilir potansiyele sahip olması, çeşitli endişeleri de beraberinde getirmiştir. Bazı bilim insanları, zeki sistemlerin ilerleyen süreçte insanlığı tehdit edebilecek düzeye gelebileceğini ve bu nedenle çeşitli önlemlerin alınması gerektiğini düşünmektedir. Bu nedenle Makine Etiği ya da Yapay Zekâ Güvenliği gibi birçok alt-araştırma alanı da zaman içerisinde ortaya çıkmıştır. Açıklamalar bağlamında bu çalışmanın amacı da, insan denetimini de içeren, zeki etmen ve Makine Öğrenmesi odaklı önlemleri bünyesinde barındıran, güvenli bir zeki sistem modeli önermektir. Çalışmada Yapay Zekâ Güvenliği odaklı temel konularla birlikte önerilen modelin detaylarına ilişkin açıklamalar sunulmuş ve potansiyeli hakkında değerlendirmeler yapılmıştır. Modelin geleceğin güvenli Yapay Zekâ sistemlerine ilham kaynağı olabileceği düşünülmektedir.

Yapay Zekâ, farklı gerçek dünyaproblemlerine etkin bir şekilde uygulanabilen ve geleceğimizi uzun bir süredirşekillendiren, önemli bilim alanlarından birisidir. Uygulandığı problem türleriçok çeşitli olmakla birlikte, bunlardan en dikkat çekenlerinden birisi demedikal teşhistir. Açıklamalardan hareketle bu çalışmanın amacı, zeki optimizasyon tabanlı Destek VektörMakineleri’ni (DVM) kullanarak diyabet teşhisi gerçekleştirmektir. Bu bağlamda,günümüz güncel zeki optimizasyon algoritmalarından beş tanesi Gaussian-RBFkernel fonksiyonu kullanan bir non-lineer DVM’yi optimize etmek amacıylakullanılmıştır. Elde edilen bulgular, farklı algoritmalar ile kurulmuş hibritsistemlerin, farklı düzeyde başarımlar gösterdiğini ancak genel anlamda zekioptimizasyon-DVM yaklaşımıyla diyabet teşhisinde yüksek oranda tutarlı sonuçlarelde edilebildiğini ortaya koymuştur. Çalışma bu yönüyle izlenen yaklaşımınYapay Zekâ tabanlı teşhis açısından önemli bir potansiyele sahip olduğunu dateyit etmektedir.&nbsp, Artificial Intelligence is one of the most importantscientific fields that can be applied effectively in different real worldproblems and has been shaping our future for a long time. While there arevarious types of problems in which it is applied, medical diagnosis is one ofthe most remarkable one among them. Moving from the explanations, objective ofthis study is to realize diabetes diagnosis by using intelligent optimizationbased Support Vector Machines (SVM). In this context, five ones of today’srecent intelligent optimization algorithms were used for optimizing a non-linearSVM, which is using a Gaussian-RBF kernel function. Obtained findings showedthat hybrid systems formed with different algorithms show different-levelsuccess but in general, good level accurate results can be achieved viaintelligent optimization-SVM approach. Hereby, the study also confirms thatthis followed approach has a significant potential for Artificial Intelligencebased diagnosis.

WOS: 000426964000001 Nowadays, there is no application area in which Artificial Intelligence oriented solutions are not employed. It is possible to see its use in even daily life and the solution scope of this scientific field of the future is growing day by day. Because of its great advantages in analyzing the physical world and solving real world problems, Artificial Intelligence techniques are often employed in different research problems that cannot be solved with traditional computational approaches. In this context, using intelligent systems to understand human features is one of the most popular research interest for recent years. In the sense of the explanations so far, objective of this study is to introduce a research in which a system that is able to extract emotions from individuals' facial expressions was designed and developed. In detail, the system considered here a Cascade Feed-forward Artificial Neural Network model trained by a recent optimization algorithm called as Vortex Optimization Algorithm. The developed system has been applied to different sets of photos from the literature and positive results were obtained at the final for each different set considered. Usak University Scientific Research and Projects Unit [2017/HD-SOSB004] This study has been supported by Usak University Scientific Research and Projects Unit with the project number 2017/HD-SOSB004.

Using open software in e-learning application is one of the most popular ways of improving effectiveness of e-learning-based processes without thinking about additional costs and even focusing on modifying the software according to needs. Because of that, it is important to have an idea about what is needed while using an e-learning-oriented open software system and how to deal with its source codes. At this point, it is a good option to add some additional features and functions to make the open source software more intelligent and practical to make both teaching-learning experiences during e-learning processes. In this context, the objective of this chapter is to discuss some possible applications of artificial intelligence to include optimization processes within open source software systems used in e-learning activities. In detail, the chapter focuses more on using swarm intelligence and machine learning techniques for this aim and expresses some theoretical views for improving the effectiveness of such software for a better e-learning experience.

The aim of the current work is to assess the challenges that gamification in education are facing nowadays. Benefits and disadvantages of using gamification in classroom are both discussed to offer a clearer view on the impact of using gamification within learning process. Exploratory study –cases are provided to investigate the relation between motivation and engagement of the students and gamification in training. Following this idea, a survey was conducted to assess how students’ behavior and motivation is affected by introducing a single, specific gamification element during a semester learning process. To stimulate competition among students, a ranking type plugin was introduced within the university learning management system used for extramural education. The results prove that motivation decreases by comparison to the previous semester., https://www.edusoft.ro/brain/index.php/brain/article/view/689/776

This paper introduce a software system including widely-used Swarm Intelligence algorithms or approaches to be used for the related scientific research studies associated with the subject area. The programmatic infrastructure of the system allows working on a fast, easy-to-use, interactive platform to perform Swarm Intelligence based studies in a more effective, efficient and accurate way. In this sense, the system employs all of the necessary controls for the algorithms and it ensures an interactive platform on which computer users can perform studies on a wide spectrum of solution approaches associated with simple and also more advanced problems., Comment: 6 pages, 2 figures, 1 table