Genetic algorithm is one of data mining classification techniques and it has been applied successfully in a wide range of applications. However, the performance of Genetic algorithm fluctuates significantly. This research work combines Genetic algorithm with fuzzy logic to adapt dynamically crossover and mutation parameters of Genetic algorithm. Two different datasets are taken during the experiment. Several experiments have been performed to prove the effectiveness of the proposed algorithm. Results show that the rules generated from a proposed algorithm are significantly better with high fitness and more efficient as compared to a normal Genetic algorithm.
Personalized trip recommendation attempts to recommend a sequence of Points of Interest (POIs) to a user. Compared with a single POI recommendation, the POIs sequence recommendation is challenging. There are only a couple of studies focusing on POIs sequence recommendations. It is a challenge to generate a reliable sequence of POIs. The two consecutive POIs should not be similar or from the same category. In developing the sequence of POIs, it is necessary to consider the categories of consecutive POIs. The user with no recorded history is also a challenge to address in trip recommendations. Another problem is that recommending the exact and accurate location makes the users bored. Looking at the same kind of POIs, again and again, is sometimes irritating and tedious. To address these issues in recommendation lies in searching for the sequential, relevant, novel, and unexpected (with high satisfaction) Points of Interest (POIs) to plan a personalized trip. To generate sequential POIs, we will consider POI similarity and category differences among consecutive POIs. We will use serendipity in our trip recommendation. To deal with the challenges of discovering and evaluating user satisfaction, we proposed a Serendipity-Oriented Personalized Trip Recommendation (SOTR). A compelling recommendation algorithm should not just prescribe what we are probably going to appreciate but additionally recommend random yet objective elements to assist with keeping an open window to different worlds and discoveries. We evaluated our algorithm using information acquired from a real-life dataset and user travel histories extracted from a Foursquare dataset. It has been observationally confirmed that serendipity impacts and increases user satisfaction and social goals. Based on that, SOTR recommends a trip with high user satisfaction to maximize user experience. We show that our algorithm outperforms various recommendation methods by satisfying user interests in the trip.
Su dağıtım sistemlerinin işletilmesindeki en yüksek gider kalemini, pompalama için kullanılan enerji maliyeti oluşturmaktadır. Çok zamanlı elektrik tarifesi ve kentsel su talebi ile bağlantılı olarak, su dağıtım sistemlerindeki istasyonlara ait pompalama çizelgesinin optimizasyonu problemi matematiksel olarak modellenebilir. Bu çalışmada, pompalama enerji maliyetini düşürmeye yönelik olarak, pompalama çizelgesinin optimizasyonu problemini çözmek için yeni bir algoritma (SDPA-sadeleştirilmiş dinamik programlama algoritması) elde edilmiştir. Yapılan simülasyonlarda SDPA’nın, konvansiyonel su seviyesi kontrolü (CWCL-conventional water level control) yaklaşımının kullanılmasıyla oluşan enerji maliyetinde, geliştirilmiş dinamik programlama algoritması (IDPA-improved dynamic programming algorithm) yaklaşımına kıyasla daha fazla tasarruf elde edebileceği sonucuna ulaşılmıştır. Ayrıca SDPA, IDPA yaklaşımında karşılaşılan karar periyodu küçülürken pompa çalışma frekansının artması probleminin de önüne geçerek, pompa açma-kapama karar sayısını oldukça düşük tutmayı başarmıştır. SDPA, Sakarya ilindeki bir su dağıtım sistemine ait bir pompalama istasyonuna uygulanmış ve puant periyodundaki enerji talebini önemli ölçüde gece periyoduna kaydırarak, CWLC yaklaşımına kıyasla enerji maliyetinde yaklaşık %12’lik bir tasarruf sağlamıştır. Su dağıtım sistemlerine ait pompalama istasyonlarında ilave yatırım yapmaksızın, sadece pompalama çizelgesinin yapılandırıldığı bu algoritmanın kullanılmasıyla, önemli ölçüde enerji tasarrufu sağlanabileceği görülmüştür.
In recent years, security has become more critical, especially with the introduction of the Internet in all areas of our lives and developments in the Internet of Things (IoT) platform. He studies on security algorithm designs to be used in these platforms are increasing day by day. In order to use these algorithms safely, they must have sufficient security levels. In this study, cryptography algorithms are used to perform performance and security analysis of lightweight encryption algorithms. In the security and performance analyzes, histogram, correlation, NPCR (Number of Pixels Change Rate) and UACI (Unified Average Changing Intensity), entropy, encryption quality and time analyzes of the encryption processes are performed. Using the obtained results, evaluation of the security and performance levels of the algorithms is presented.
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