The initial literature reviewing step is of great importance during any scientific reporting. Nevertheless, finding relevant papers grows tough as the number of online scientific publications rapidly increases. Correspondingly, the need for article recommendation systems has emerged, which aim to recommend new papers suitable for the researchers’ interests. Using these systems provides researchers access to related publications quickly and effectively. In this study, a novel article recommendation system, which is empowered by the hybrid combinations of content-based state-of-the-art methods, is proposed. Various methods are utilized comparatively for an in-depth analysis, and user profiles are evaluated. 41,000 articles collected from the ARXIV dataset are used in the performance evaluation. In the experiments in which Word2vec and LDA are combined, Precision@50, Recall@50, and F1-score@50 achieve the highest performance with .206, .791, and .498 values, respectively. The in-depth analysis and the numerical findings justify that the proposed system is strong and promising compared to the literature.
The stage before the conversion of agricultural products into post-harvest consumer products is the process of separating the raw products into appropriate classes. Today, this difficult manual separating process is a process in which a large number of workers work at an intense pace on the product line and the workforce is intensively spent. Disruptions in separating as a result of carelessness cause product loss, loss of time and cost increases. In this study, as an alternative to manual separating processes, a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object recognition and traveling salesman problem algorithms has been created. In this way, a low-budget separating system is recommended for large selecting processes with a time- and cost-effective selecting model. In the study, the creation of a real-time fast separating system with the support of the traveling salesman algorithm, performance evaluation and research and findings on the fast separating model are presented.
Today, smart phones based on different mobile communication technologies such as GSM, GPRS/EDGE, 3G, 4G, 5G and 6G have become an indispensable element for various services such as communication, entertainment, and banking. In this context, determining the base station power level is important in terms of limit values and public health, especially in places with high power density. In this study, electromagnetic power density of 31 different base stations was measured at 900 MHz frequency at 20, 40 and 60 meters distances from base stations. Since it is practically not possible to measure each base station from every distance, 3 different distances were chosen randomly. Then, using the power density values measured from different distances, electromagnetic power density estimation was made with multiple linear regression (MLR) analysis method for distances at intermediate distances (25, 30, 35, 45, 50 and 55 meters). In this study, MLR analysis method was applied for power density estimation for the first time in the literature and adjusted R-square value above 0.99 was obtained for each intermediate distance.
In this research, a hybrid artificial neural network (ANN) optimized by a genetic algorithm (GA) was used to estimate energy and exergy analyses parameters. This article presents an approach for estimating energy and exergy analyses parameters with optimized ANN model based on GA (GA-ANN) for different ternary blends consisting of diesel, biodiesel and bioethanol in a single-cylinder, water-cooled diesel engine. The data used in the experiments performed at twelve different engine speeds between 1000 and 3000 rpm with 200 rpm intervals for five different fuel mixtures consisting of fuel mixtures prepared by blends biodiesel, diesel and 5% bioethanol in different volumes constitute the input data of the models. Using these input data, engine torque (ET), amount of fuel consumed depending on fuels and speed (AFC), carbon monoxide emission values (CO), carbon dioxide emission values (CO2), hydrocarbon emission values (HC), nitrogen oxides emission values (NOx), the amount of air consumed (AAC), exhaust gas temperatures (EGT) and engine coolant temperatures (ECT) were estimated with the GA-ANN. In examining the results obtained were examined, it was proved that diesel, biodiesel and bioethanol blends were effective in predicting all the results mentioned in engine studies performed at 200 rpm intervals in the 1000-3000 rpm range. A standard ANN model used in the literature was also proposed to measure the prediction performance of GA-ANN model. The predictive results of both models were compared using various performance indices. As a result, it was revealed that the proposed GA-ANN model reached higher accuracy in estimating the exergy and energy analyses parameters of the diesel engine compared to the standard ANN technique.
Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions[20]. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches[1]. This paper describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance-based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.
