In this research paper, we propose an intelligent recommender system suitable for E-commerce transactions. The system employs an emerging ANN method called the Hierarchical Temporal Memory (HTM) for continuous predictive recommendation. The results considering open source data obtained from an online store were reported considering the adjustments of HTM columns parameter. The findings of the result indicate that higher columns will lead to enhanced performance with > 95% classification accuracy obtained at a set column size of 1000units. The proposed HTM-ANN is expected to be a promising alternative to existing feed-forward ANNs for real-time E-commerce applications.

Positronium Imaging requires two classes of events: double-coincidences originated from pair ofback-to-back annihilation photons and triple-coincidences comprised with two annihilation photonsand one additional prompt photon. The standard reconstruction of the emission position along theline-of-response of triple-coincidence event is the same as in the case of double-coincidence event;an information introduced by the high-energetic prompt photon is ignored. In this study, we proposeto extend the reconstruction of position of triple-coincidence event by taking into account the timeand position of prompt photon. We incorporate the knowledge about the positronium lifetime distributionand discuss the limitations of the method based on the simulation data. We highlight that theuncertainty of the estimate provided by prompt photon alone is much higher than the standard deviationestimated based on two annihilation photons. We finally demonstrate the extent of resolutionimprovement that can be obtained when estimated using three photons.

The rapidly developing field of quantum computing and the ongoing lack of widely available quantum computers create the need for scientists to build their simulators. However, mathematical simulation of such circuits usually ignores many aspects and problems found in real quantum systems. In this article, the authors describe a quantum bit emulator based on FPGA integrated circuits. In this case, FPGA technology provides real-time massive parallelism of the modeled physical phenomena. The modeled QUBIT is represented using a Bloch sphere. Its quantum state is set and modified only by precise pulses of an electrical signal, and with the help of similar pulses, it manifests its current state in real time. The constructed QUBIT was additionally equipped with decoherence mechanisms and with circuits that intentionally respond to internal and external noises that distort its current quantum state. This article presents and discusses how such a physically built emulator works.

The rapidly developing field of quantum computing and the ongoing lack of widely available quantum computers create the need for scientists to build their simulators. However, mathematical simulation of such circuits usually ignores many aspects and problems found in real quantum systems. In this article, the authors describe a quantum bit emulator based on FPGA integrated circuits. In this case, FPGA technology provides real-time massive parallelism of the modeled physical phenomena. The modeled QUBIT is represented using a Bloch sphere. Its quantum state is set and modified only by precise pulses of an electrical signal, and with the help of similar pulses, it manifests its current state in real time. The constructed QUBIT was additionally equipped with decoherence mechanisms and with circuits that intentionally respond to internal and external noises that distort its current quantum state. This article presents and discusses how such a physically built emulator works.

Microservices are an answer to various scalability challenges. They enable building large and complex systems by scaling the number of services, and development teams. Microservices allow self-management and agile processes such as Scrum. Nevertheless, a minimal management process should also be defined and documented. Documentation must be easily understandable and applicable to the developing teams to foster efficiency. We propose to use the ßMACH method, a software management guidance. The result is a minimal and systematic description. Based on the definition obtained using ßMACH, we can state that the service and team isolation is essential for a scalable microservice system. In addition, we introduce ßMACH and show how to document a software management process. The documentation is easy to understand by software developers and interesting for software engineers.

The visual modeling language UML embodies object-oriented design principles.It provides a standard way to visualize the design of a system. It exploits a richset of well-defined graphical notations for creating abstract models. However,the power of UML is lessened through partially specified formal semantics. Indeed, UML notations are semi-formal and do not lead to fully formalized andexecutable semantics. Fortunately, UML diagrams are prone to early formalization. Formal methods are a valuable tool that can help overcome the UMLconstructs’ shortage of firm semantics. It is a powerful way to ascribe precise semantics to the graphical notations used in UML diagrams and models.Our work aims to support the semantics of the UML Interaction Overview Diagram. It introduces an approach to leveraging the strengths of the MaudeRewriting Logic language as a formal specification language. The proposal relies on a model-driven engineering approach. It aims to automate the UMLInteraction Overview Diagram’s mapping to a Maude language specification.The Maude language and its linked tools, including the Maude Model Checker,are used to analyze and verify the resulting Maude specification. Finally, anapplication example shows the feasibility and benefits of the proposed approach.

Bonobo optimizer (BO) is a novel metaheuristic algorithm motivated bythe social behaviour of the bonobos. This paper presents a quantum behaved bonobo optimization algorithm (QBOA) employing an innovative metaheuristic based on the reproductive strategies and social behavior of bonobos.Whereby, the quantum mechanics are embedded into the bonobo optimizerto direct the search agents through the search space. Accordingly, under thisquantum-behaved movement, the proposed QBOA’s exploitation capability ispromoted. The performance of the proposed QBOA is exhibited on CEC2005and CEC2019 benchmarks. Moreover, the QBOA algorithm was adapted tooptimize the dynamic photovoltaic models parameters. QBOA exhibits theefficiency and adequacy to solve various optimization problems based on experimental and comparison findings, as well as its ability to implement competitiveand promising results optimizing dynamic photovoltaic models

Bu çalışmada, Allrecipes.com web sitesindeki yemek tariflerine ve üyeler tarafından verilen oylara dayalı bir yemek tavsiye sistemi geliştirildi. Toplam 1840 yemek tarifi (Diyabetik - Glutensiz - Ketojenik - Düşük Sodyum - Düşük Kolesterol - Vejetaryen – Vegan) Allrecipes.com'dan web scraping yöntemi ile kazındı ve Python'da analiz edildi. Tavsiye Sistemi, Değişimli En Küçük Kareler (DEKK) yöntemi kullanılarak oluşturuldu. Diyet Yemek Tavsiye Sistemi, kosinüs benzerlik yöntemi kullanılarak gerçekleştirildi. DEKK yönteminin büyük veri ile uygulaması bulut üzerinde gerçekleştirildi. Modelin hata kareler ortalamasının karekökü 0.495 olarak bulundu. Modelin önerdiği yemekler kullanıcı bazlı incelendi ve sonuçların tutarlı olduğu belirlendi. En çok tavsiye edilen yemekler incelendiğinde, vejetaryen tariflerin ilk sırada yer aldığı; toplamda ise ketojenik tariflerin yüksek sayıda önerildiği görüldü. Sonuç olarak, yemek tarifleri aracılığıyla yiyecekler hakkında fikir sahibi olmak ve diyetlerine göre yiyecek seçmek isteyen kullanıcılara doğru öneriler üreten web tabanlı bir yemek öneri sistemi oluşturuldu.

In knowledge discovery, the processes of applying data cleaning, data integration, data selection-transformation, and data mining methods and obtaining meaningful information from the obtained patterns are performed, respectively. In recent years, it has become quite common to use metaheuristic optimization methods in the data selection phase. In this study, the nearest neighbor algorithm, support vector machine, and decision tree algorithms from machine learning algorithms were used on health data obtained from the University of California, Irvine. The whale optimization algorithm, salp swarm optimization, slime mould optimization, particle swarm optimization, and Harris Hawks optimization methods were used for feature selection. The obtained results were compared in detail.

Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in allcases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the twomethods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.