Data mining and classification for traffic systems using genetic network programming

制度:新 ; 報告番号:甲3271号 ; 学位の種類:博士(工学) ; 授与年月日:2011/3/15 ; 早大学位記番号:新557

Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction

A study on the memory schemes for genetic network programming

制度:新 ; 報告番号:甲3376号 ; 学位の種類:博士(工学) ; 授与年月日:2011/9/15 ; 早大学位記番号:新569

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

A Study on Intrusion Detection System in Wireless Sensor Networks

The technology of Wireless Sensor Networks (WSNs) has become most significant in present day. WSNs are extensively used in applications like military, industry, health, smart homes and smart cities. All the applications of WSN require secure communication between the sensor nodes and the base station. Adversary compromises at the sensor nodes to introduce different attacks into WSN. Hence, suitable Intrusion Detection System (IDS) is essential in WSN to defend against the security attack. IDS approaches for WSN are classified based on the mechanism used to detect the attacks. In this paper, we present the taxonomy of security attacks, different IDS mechanisms for detecting attacks and performance metrics used to assess the IDS algorithm for WSNs. Future research directions on IDS in WSN are also discussed

Optimizing IC engine efficiency: A comprehensive review on biodiesel, nanofluid, and the role of artificial intelligence and machine learning

Transportation and power generation have historically relied upon Internal Combustion Engines (ICEs). However, because of environmental impact and inefficiency, considerable research has been devoted to improving their performance. Alternative fuels are necessary because of environmental concerns and the depletion of non-renewable fuel stocks. Biodiesel has the potential to reduce emissions and improve sustainability when compared to diesel fuel. Several researchers have examined using nanofluids to increase biodiesel performance in internal combustion engines. Due to their thermal and physical properties, nanoparticles in a host fluid improve engine combustion and efficiency. This comprehensive review examines three key areas for improving ICE efficiency: biodiesel as an alternative fuel, application of nanofluids, and artificial intelligence (AI)/machine learning (ML) integration. The integration of AI/ML in nanoparticle-infused biodiesel offers exciting possibilities for optimizing production processes, enhancing fuel properties, and improving engine performance. This article first discusses, the benefits of biodiesel concerning the environment and various difficulties associated with its usage. The review then explores the effects and characteristics of nanofluids in IC engines, aiming to know their impact on engine emissions and performance. After that, this review discusses the utilization of AI/ML techniques in enhancing the biodiesel-nanofluid combustion process. This article sheds light on the ongoing efforts to make ICE technology more environmentally friendly and energy-efficient by examining current research and emerging patterns in these fields. Finally, the review presents the challenges and future perspectives of the field, paving the way for future research and improvement