With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.

Time series represent the consecutive measurements taken at equally spaced time intervals. Time series prediction uses the information in a time series to predict future values. The future value prediction is important for many business and administrative decision makers especially in e-commerce. To promote business, sales prediction and sensing of future consumer behavior can help business decision makers in marketing campaigns, budget and resource planning. In this study, deep learning based a new prediction model has been developed for the time of next purchase in ecommerce. The proposed model has been extensively tested and compared with RF, ARIMA, CNN and MLP using a retail market dataset. The experimental results show that the developed model has been more successful than RF, ARIMA, CNN and MLP to predict the time of the next purchase.

A new weighted multi-attribute based recommender system (WMARS) has been developed using extended user behavior analysis. WMARS obtained data from number of clicked items in the recommendation list, sequence of the clicked items in recommendation the list, duration of tracking, number of tracking same item, likes/dislikes, association rules of clicked items, remarks for items. WMARS has been applied to a movie web site. The experimental results have been obtained from a total of 567 heterogeneous users, including employers in different sectors, different demographic groups, and undergraduate and graduate students. Using different weighted sets of the attributes’ parameters, WMARS has been tested and compared extensively with collaborative filtering. The experimental results show that WMARS is more successful than collaborative filtering for the data set that was used.

This paper proposes an artificial neural network (ANN) to obtain the electron energy distribution functions (EEDFs) in SF₆ and argon from the following: 1) mean energies; 2) the drift velocities; and 3) other related swarm data. In order to obtain the required swarm data, the electron swarm behavior in SF₆ and argon is analyzed over the range of the density-reduced electric field strength E/N from 50 to 800 Td from a Boltzmann equation analysis based on the finite difference method under a steady-state Townsend condition. A comparison between the EEDFs calculated by the Boltzmann equation and by ANN for various values of E/N suggests that the proposed ANN yields good agreement of EEDFs with those of the Boltzmann equation solution results.

Fuzzy logic controllers (FLC) have gained popularity in the past few decades with successful implementation in many areas, including electrical machines’ drive control. Many colleges are now offering fuzzy logic courses due to successful applications of FLCs in nonlinear systems. However, teaching students a fuzzy logic controlled drive system in a laboratory, or training technical staff, is time consuming and may be an expensive task. This paper presents an educational tool for fuzzy logic controlled brushless direct current motor (BDCM), which is a part of a virtual electrical machinery laboratory project. The tool has flexible structure and graphical interface. Motor and controller parameters of the drive system can be changed easily under different operating conditions.

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) has been presented to speed control of a switched reluctance motor (SRM). SRMs have become an attractive alternative in variable speed drives due to their advantages such as structural simplicity, high reliability, high efficiency and low cost. But, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter variations. In this study, to tackle these problems, an adaptive neurofuzzy controller is proposed. Heuristic rules are derived with the membership functions then the parameters of membership functions are tuned by ANFIS. The algorithm has been implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the proposed ANFIS controller under different operating conditions of the SRM.

Text summarization is a process of generating a brief version of documents by preserving the fundamental information of documents as much as possible. Although the majority of the text summarization researches have been focused on supervised learning solutions, there are a few datasets indeed generated for summarization task, and most of the existing summarization datasets do not have human-generated goal summaries, which is vital for both summary generation and evaluation. Therefore, a new dataset was presented for abstractive and extractive summarization tasks in this study. This dataset contains academic publications, the abstracts written by the authors, and extracts in two sizes, which were generated by human readers in this research. Then, the resulting extracts were evaluated to ensure the validity of the human extract production process. Moreover, the extractive summarization problem was reinvestigated on the proposed summarization dataset. Here the main point taken into account was to analyze the feature vector to generate more informative summaries. To that end, a comprehensive syntactic feature space was generated for the proposed dataset, and the impact of these features on the informativeness of the resulting summary was investigated. Besides, the summarization capability of semantic features was experienced by using GloVe and word2vec embeddings. Finally, the use of ensembled feature space, which corresponds to the joint use of syntactic and semantic features, was proposed on a long short-term memory-based neural network model. ROUGE metrics evaluated the model summaries, and the results of these evaluations showed that the use of the proposed ensemble feature space remarkably improved the single-use of syntactic or semantic features. Additionally, the resulting summaries of the proposed approach on ensembled features prominently outperformed or provided comparable performance than summaries obtained by state-of-the-art models for extractive summarization.

Text summarization is the process of generating a brief version of a text that preserves the salient information of the text. For information retrieval, it is a good dimension reduction solution. In addition, it reduces the required reading time. This study focused on extracting informative summaries from multiple documents using commonly used hand-crafted features from the literature. The first investigation focused on the generation of a feature vector. The features were the number of sentences, term frequency, similarity with the title, term frequency-inverse sentence frequency, sentence position, sentence length, sentence–sentence similarity, bushy-path results, phrases of the sentence, proper nouns, n-gram co-occurrence, and length of the document. Secondly, several combinations of these features were examined and a shallow multi-layer perceptron and two differently modeled fuzzy inference systems were used to extract salient sentences from texts in the Document Understanding Conference (DUC) dataset. The summarization performances of these models were evaluated using original classification performance metrics, and recall-oriented understudy for gisting evaluation (ROUGE)-n. This study recommended the use of fuzzy systems based on a feature vector and a fuzzy rule set for extractive text summarization. The extraction methods were evaluated against a changing compression ratio. Results of experiments showed that the implemented neural model tended to incorrectly infer sentences that were not considered salient by human annotators. However, for distinguishing between summary-worthy and summary-unworthy sentences, the fuzzy inference systems performed better than the utilized neural network, as well as better than the existing fuzzy inference-based text summarization approaches in the literature.

Switched reluctance motor (SRM) is increasingly employed in industrial applications where variable speed is required because of their simple construction, ease of maintenance, low cost and high efficiency. However, the SRM performance often degrades for the machine parameter variations. The SRM converter is difficult to control due to its nonlinearities and parameter uncertainties. In this paper, to overcome this problem, a neuro fuzzy controller (NFC) is proposed. Heuristic rules are derived with the membership functions of the fuzzy variables tuned by a neural network (NN). The algorithm is implemented on a digital signal processor (TMS320F240) allowing great flexibility for various real time applications. Experimental results demonstrate the effectiveness of the NFC with various working conditions of the SRM.

Natural hazards have a great number of influencing factors. Machine-learning approaches have been employed to understand the individual and joint relations of these factors. However, it is a challenging process for a machine learning algorithm to learn the relations of a large parameter space. In this circumstance, the success of the model is highly dependent on the applied parameter reduction procedure. As a state-of-the-art neural network model, representative learning assumes full responsibility of learning from feature extraction to prediction. In this study, a representative learning technique, recurrent neural network (RNN), was applied to a natural hazard problem. To that end, it aimed to assess the landslide problem by two objectives: Landslide susceptibility and inventory. Regarding the first objective, an empirical study was performed to explore the most convenient parameter set. In landslide inventory studies, the capability of the implemented RNN on predicting the subsequent landslides based on the events before a certain time was investigated respecting the resulting parameter set of the first objective. To evaluate the behavior of implemented neural models, receiver operating characteristic analysis was performed. Precision, recall, f-measure, and accuracy values were additionally measured by changing the classification threshold. Here, it was proposed that recall metric be utilized for an evaluation of landslide mapping. Results showed that the implemented RNN achieves a high estimation capability for landslide susceptibility. By increasing the network complexity, the model started to predict the exact label of the corresponding landslide initiation point instead of estimating the susceptibility level.

Theoretical thermodynamic analysis of the absorption thermal systems is at present too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations and simulation programs. This paper proposes a new approach to performance analysis of a solar-driven ejector-absorption refrigeration system (EARS) with an aqua/ammonia working fluid. Use of artificial neuralnetworks (ANNs) has been proposed to determine the performance parameters as functions of only the working temperature, under various working conditions. Thus, this study is considered to be helpful in predicting the performance of an EARS prior to it being set up in an environment where the temperatures are known. The statistical coefficient of multiple determinations (R² – value) equals to 0.976, 0.9825, 0.9855 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively. These accuracies are acceptable for the design of an EARS. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modelling programs. The importance of the ANN approach, apart from reducing the time required, is that it is possible to find solutions that make solar-energy applications more viable and thus more attractive to potential users, such as solar engineers. Also, this approach has the advantages of computational speed, low cost for feasibility, rapid turnaround (which is especially important during iterative design-phases), and ease of design by operators with little technical experience.

Studies to classify bird species on the basis of images are very difficult due to both the abundance of colors and patterns in the image, and their very close visual characteristics. In this study, six different deep learning models have been applied for the classification of bird species and the experimental results have been compared comprehensively. A dataset named 250 Bird Species, which includes a total of 31316 bird images with 225 bird species, was used as dataset. In the study, 1125 images have been used for the test and 1125 images for the validation. The comparison of VGG16, ResNet50, ResNet152V2, InceptionV3, MobileNet and DenseNet121 deep learning models have been made on the dataset respectively, according to the accuracy, precision, recall and F1-score values. In experimental studies, 94.6% accuracy value ??has been obtained with VGG16, 47.2% with ResNet50, 96.2% with ResNet152V2, 97.5% with InceptionV3, 96.9% with MobileNet and 98.2% with DenseNet121. DenseNet121 obtained the highest precision value as 0.99, sensitivity value as 0.99 and F1-score value as 0.99.

Studies have been going on for many years to predict the time before some events happen. Thus, it is aimed to minimize the damage that occurs when the event occurs or to maximize the benefit to be obtained. Studies on the prediction of subsequent events in many different areas, such as the prediction of the subsequent behavior of a customer, the prediction of the subsequent occurrence of natural disasters, the estimate of the number of future demands in a given time interval, are gradually increasing. However, in the literature, there is no successful study for predicting the time and type of event before the occurrence of crimes and emergency calls. Crime analysis is a field of research aimed at securing the threatened areas, reducing the rate of crime and saving law enforcement. High success is achieved with the use of up-to-date technologies in the efforts to resolve the crime shortly after it is committed. Similarly, emergency call analysis reduces response time and optimizes resource usage. In this study, a deep learning based prediction model for crime and emergency call analysis has been developed. With the developed model, the time of the next crime and the time of the next emergency call are predicted. The results obtained with the developed model has been compared with ARIMA which is one of the statistical time series prediction methods. Experimental results have shown that the developed deep learning-based model is more successful than ARIMA in forward-looking event time prediction.

The software industry is increasingly involved in every aspect of life and software projects are being developed to a large extent. This situation causes very important faults and negative results in the developed projects. Therefore, in order to prevent or minimize this situation, software risk management activities must be successfully implemented. In this study, a new web-based risk management process based on artificial intelligence in software projects was designed and developed. The purpose of the study is to estimate the deviations that might occur in the project outputs according to the risk factors using artificial neural networks (ANN), to minimize the harm that may be encountered in the first stages of the software life cycle and thus to provide a preventive approach for users. In order to create the ANN model of the study, a checklist form was created by preliminary discussions with academicians, experts and project managers in the software engineering field. By using this form, the actual project data were collected from 774 different companies in the software companies located in Teknokent. The generated ANN model has forty-five entrances, a single hidden layer (with fifteen neurons) and five outlets (with 45-15-5); the education R rate is 0.9978; the test R ratio is 0.9935 and the error rate is 0.001. The model is integrated into the application developed by creating the .dll library. The developed application, real project data from different areas were obtained and after obtaining the opinions of experts and academicians (10 people), 4 different scenarios were tested and results were obtained. The results clearly demonstrate that the performance of the application is high and that the use of ANN in such applications provides positive contributions to the project's success. In addition, it has been found that there is a need for applications that provide an artificial intelligence-based risk management process for the software industry.

Visually impaired individuals face many difficulties in their daily lives. In this study, a video captioning system has been developed for visually impaired individuals to analyze the events through real-time images and express them in meaningful sentences. It is aimed to better understand the problems experienced by visually impaired individuals in their daily lives. For this reason, the opinions and suggestions of the disabled individuals within the Altınokta Blind Association (Turkish organization of blind people) have been collected to produce more realistic solutions to their problems. In this study, MSVD which consists of 1970 YouTube clips has been used as training dataset. First, all clips have been muted so that the sounds of the clips have not been used in the sentence extraction process. The CNN and LSTM architectures have been used to create sentence and experimental results have been compared using BLEU 4, ROUGE-L and CIDEr and METEOR.

In last decades, the number of web pages on the Internet has been exposed a rapid increase intrinsically, and the information on the Internet has reached a very large size. Search engines have been developed to access this large-scale information efficiently. Web crawlers play a very important role in search engines. In this paper, an efficient multi-threaded web crawler is proposed, and empirically analyzed in terms of crawling speed and coverage.

Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

In this study, three different methods from machine learning and deep learning have been implemented for preventing financial and moral losses that may occur as a result of delays in flights and to take necessary precautions by predicting the flight delay in advance, which are a serious problem in the aviation industry. Deep recurrent neural network (DRNN), long-short term memory (LSTM), and random forest (RF) have been extensively tested and compared employing a real data set covering 368 airports across the world with relevancy the success rate of forecasting of delay on flights. The experimental results showed that the LSTM model had a higher success rate of 96.50% at the recall level than the others.

Connected vehicle technology is a communication technology that enables vehicles to communicate with other vehicles and other traffic assets.It is critically important to take the necessary actions during the development, and maturation stages of this technology for Turkey to gain a share in the emerging market, have a position, and ensure data security. In this article, the situation of connected vehicle technology in other countries is summarized and the situation in Turkey is examined. As a result of the work, suggestions wer e made about the subjects that should be given priority. It has been observed that studies on this subject under the n ame of intelligent transportation systems have gained momentum in Turkey in 2014. Considering the studies carried out around the world, it has b een seen that Turkey started to work on the connected vehicle technology called Cooperative Intelligent Transportation Systems (C-ITS) in later periods and it needed more technical experience on the subject. It is noteworthy that the necessary support and resourc es should be provided in order for the action plans, research centers, and associations to work efficiently. It has been concluded that the preparation of national standardizations and regulations on data security, and reliability in connected vehicle technology is an important need and more work should be done in this area.

In daily life and scientific searches, the need for real-like and denoised images is increasing. But images are distorted by noise, resulting in lower visual image quality. For this reason, noise removal studies are carried out on images to increase the quality. Until now, various methods have been proposed to decrease noise, each technique has different advantages. This paper gives information about the methods that achieve the best results in their field and summarizes the studies about traditional denoising and deep learning based denoising methods in the field of noise reduction in video and images and compares the studies with each other. Researches show that experiments focus on the case of additive white Gaussian noise. Traditional noise removal methods, machine learning methods, deep learning methods and other mathematical methods have been used in image denoising problem over time, and deep learning methods achieve more successful results. However, according to the obtained data, it has been seen that the studies on training the model without having the original image pairs were insufficient and a single method could not be successful at different noise levels. In future studies, it is necessary to focus on how to remove the noise in real-life images.

The size of the Internet is rapidly increasing. It has become a necessity to access information on the web correctly for a short time. For this reason, search engines have arisen out of meeting this need. In this study, we propose a ranking algorithm based on page multi-attribute(PMARank). The proposed algorithm uses a novel index calculation system that acts as a pre-rank process for web pages. In the ranking procedure, the featured meta-tag of a page and its contents were extracted to locate words as ranking features. The proposed web ranking algorithm has been compared with PageRank (PR) and Hyperlink-Induced Topic Search (HITS) algorithms. Experimental results show that the proposed ranking algorithm performs better than PR and HITS algorithms according to user clickstreams on the search results page.

Campaign based sales continue to increase at a very rapid rate in recent years. Today, sales in many sectors are based on the campaign. Therefore, campaign management has become a very important issue. There is no detailed study on campaign planning and management in the literature. In this article, a model is developed for campaign management in the banking sector and prediction of prospective behaviors of customers towards the campaign. Using the association analysis structure developed specifically for the banking sector, frequent itemsets and association rules were created from the campaign data of a Portuguese bank. The prospective behavior of the customers participating in the campaign was estimated with the rules obtained. In addition, attributes that affect the behavior of customers have been identified. Experimental results have shown that marital status and credit status affect customer behavior the most. Using the developed model, a prediction is made on whether the customers will participate in the campaign or not. 87% success was achieved in the prediction of customers' participation in the campaign.

Log anomaly detection is vital in managing large-scale and distributed systems used today. Log analysis must be done in a short time and with high accuracy to be beneficial. As attacks on systems become more and more complex, traditional log anomaly detection methods have become more cumbersome, unsuccessful, and unuseful. In this study, a deep learning-based model has been developed for anomaly detection using log data from application servers in large-scale systems. First, pre-processing was carried out on the log data, and then parsing and grouping were carried out. The Drain method was used to parse the log files. The obtained data were divided into two groups, and the training and testing of the deep learning model developed were carried out. In the feature extraction phase, log data were converted into vectors and used as input for the developed model. The developed model learns normal and abnormal behavior in the data set and then detects abnormal behavior. The results obtained from the experimental studies showed that the developed model successfully detected 93% of the anomaly data. It has been observed that the level of success at the data labeling stage is very effective in training the model and detecting anomalies.

One of the biggest problems in V2X communication is the inclusion of faulty or illegal nodes in the network. In V2X systems using PKI, the primary approach is to revoke the certificates of malicious and compromised vehicles and add them to a CRL. The issue of CRL distribution from the central server without high latency and high traffic is a subject of research. In this paper, a semi-distributed certificate revocation control system called trust-chain is proposed, which reduces the demand for CRL servers. In the simulation results, the number of requests to the central CRL server is reduced by 35% on average using the proposed semi-distributed structure. Additionally, in cases where vehicles cannot communicate with the RSU or central server, vehicles can communicate securely owing to the semi-distributed framework of the proposed system.

Driver drowsiness is one of the most important factors in traffic accidents. For this reason, systems should be developed to detect drowsiness early and to warn the driver by examining the driver or driving situations. These developed systems play an important role in preventing accidents. Three techniques are used to detect drowsiness: ’Based on Vehicle Parameters’, ’Based on Physiological Parameters’ and ’Based on Behavioral Parameters’. In this study, the studies on fatigue detection systems were examined and a literature study was presented and the techniques used were examined. The deep learning methods used in the studies were also examined and presented. Finally, the data sets used in the studies were compared and the general results were shared.

Day by day the usage of infrared cameras has been increasing in the world. With the increasing use of thermal infrared cameras and images, especially in military, security and medicine, the need for coloring thermal infrared images to visible spectrum has arisen. In this study, a deep based model has been developed to generate visible spectrum images (RGB - Red Green Blue) from thermal infrared (TIR) images. In the proposed model, an encoder-decoder architecture with skip connections has been used to generate RGB images. KAIST-MS (Korea Advanced Institute of Science and Technology-Multispectral) dataset used for training and test the developed model. The experimental results extensively tested using Least Absolute Deviations (L1), Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM).

Nowadays, the size of data continues to increase more rapidly every day. Considering this situation, large-scale processing has become a very important issue in document clustering, due to its capability to organize large numbers of documents into few meaningful and consistent clusters. In this study, a dataset consisting of 390 English textbooks of a total size of 7.61 GB, is used for the clustering task. Locality sensitive hashing and k-shingles methods are used to obtain clusters with high quality. Clusters are evaluated using cluster validity indices. According to the experimental results, high-quality clusters have been obtained, with 0.88 and 0.79 for Silhouette and Davies–Bouldin scores, respectively.

In this study, Long Short-Term Memory (LSTM) based Query Auto-Completion (QAC) has been proposed to generate a query completion list using input prefix. The performance of the QAC system has been evaluated by using the relevancy score, and the quality of the QAC generation system has been evaluated by using partial and complete matching strategies, success rate, normalized discounted cumulative gain, and mean average precision. The proposed LSTM based QAC system has been extensively tested using AOL and ORCAS datasets. According to experimental results, the performance of the proposed QAC system is more successful with the partial matching strategy. Also, the quality of the QAC generation list by the proposed QAC system is better on the complete matching strategy.

This study has been prepared to set light on the performance difficulties encountered in large datasets on graph databases and to increase performance in Create, Read, Update, Delete (CRUD) operations with Approximate Membership Functions (AMF). For this purpose, the Bloom filter from the AMF family is proposed in a scalable structure. Neo4j commercial graph database was preferred in experimental studies as a graph data model. In the experimental studies, it has been observed that the proposed method for all CRUD operations produces better results than the BTREE indexing method used by the database. The proposed AMF method can be preferred for performance optimization in such databases.

Nowadays, user behaviour analysis is gaining importance due to the increasing interest of users in e-commerce platforms. By providing users with a personalized shopping experience, customer satisfaction and sales rates can be increased. In this study, a deep learning based hybrid model has been developed for predicting the next purchase time. The developed model has been compared extensively with ARIMA and LSTM. Experimental results showed that the developed deep learning model has more successful than ARIMA and LSTM in predicting the next purchase time.

Satellite images can be corrupted by noise during image capture, transfer or due to bad environmental conditions. In daily life and scientific searches, the need for more accurate images are increasing. However, images are distorted by noise, resulting in lower visual image quality. For this reason, noise removal studies are carried out on images to increase the quality. Until now, various methods have been proposed to decrease noise and each technique have different advantages. This paper, summarizes the studies in the field of noise reduction in video and images and compares the studies with each other.

With the rapid development of information technologies and the widespread use of the internet, the volume and diversity of data has also increased. Meaningful information and important results can be obtained by processing this data, which is expressed with the concept of big data. In this study, a machine learning platform that can automatically learn from data sets with different data types and dimensions has been developed. When the dataset of any field is given as an input to the developed automatic machine learning platform, the most appropriate machine learning model is determined. With this platform, which has a Web interface that can be easily used by people who are experts in their field but do not have sufficient knowledge in the field of machine learning and data science, the most suitable machine learning model for the data set is suggested to the users and training and test results for different models can be obtained and compared. The experimental studies have shown that the developed platform is successful in knitting a machine learning model suitable for the dataset.

Hierarchical Fuzzy System (HFS) is a popular approach for handling curse of dimensionality problem occurred in complex fuzzy rule-based systems with various and numerous inputs. However, the processes of modeling and reasoning of HFS have some critical issues to be considered. In this study, the effect of these issues on the accuracy and stability of the resulting system has been investigated, and an end-to-end HFS framework has been proposed. The proposed framework has three main steps such as single system modeling, rule partitioning and HFS reasoning. It is fully automated, generic, almost independent from data, and applicable for any kind of inference problem. In addition, the proposed framework preserves accuracy and stability during the HFS reasoning. These judgments have been ensured by a number of experimental studies on several datasets about software faulty prediction (SFP) problem with a large feature space. The main contributions of this paper are as follows: (i) it provides the entire HFS implementation from problem definition to calculation of final output, (ii) it increases the accuracy of recently proposed rule generation scheme in the literature, (iii) it presents the only possible fuzzy system solution for SFP problem containing a large feature space with reasonable accuracy.

Smart home systems are one of today's active working issues because of the increase comfort, raised living standards, security, health, economic benefits. Smart home systems should interact with the activities of people living in a house and be remotely accessible, monitorable and controllable. A smart home system with the mentioned features has been developed in this study. The system collects data from the behaviors and interactions of the people in the house. It evaluates the collected data adaptively and outputs the results. It produces control signals by using the results. In addition, a simulation was developed to collect data from devices such as an IoT device. The resulting data is examined using rule subtraction from sequential patterns, a data mining method. In this way, the habits of the users in a house are added to the system as a scenario. By using scenarios and rules, the interaction with the user of the system is increased by predicting what the user can do next. The results show energy saving and comfort increase with the developed system.

In this paper, a new method proposed for extracting and matching the Search Result Record (SRR) data items from different search engines. The method first detects SRRs for a given Web search result. Afterwards, an SRR simplification algorithm is devised to deal with complexity of SRR Document Object Model (DOM) Trees. SRRs and their data items (or properties) are extracted after simplification. Data items are normalized in local and global domain as a last step. Experimental results show that the proposed methods are successful in extracting and merging the SRRs.

In this paper, a new method proposed for finding and extracting the SRRs. The method first detects content dense nodes on HTML DOM and then extracts SRRs to suggest a list of candidate HTML DOM nodes for a given single research result Web page instance. Afterwards an evaluation algorithm has been applied to the candidate list to find the best solution without any human interaction and manual process. Experimental results show that the proposed methods are successful for finding and extracting the SRRs.