A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city

Ravi, Logesh; Subramaniyaswamy, V.; Vijayakumar, V.; Gao, Xiao‐Zhi; Indragandhi, V.

doi:10.1016/j.future.2017.08.060

Cited by 73 publications

(26 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Increased labor productivity is expected to account for more than 50% of the contributions made to GDP by AI between 2018 and 2030 and, more generally, of emerging technologies of which AI belongs. We observe within the IC&T specialized teams an increase in the interest of exploring emerging technologies [16,21,27]. We discuss about AI and ML applications, a lot of algorithms and statistical models that develop learning processes based on large data sets like Big Data, Internet of Things.…”

Section: A General Outllook Of Emerging Technologies That Can Sustainmentioning

confidence: 99%

Tourism and Travel can Effectively Benefit from Technologies Associated with Industry 4.0

Mazilescu¹

2019

Risk in Contemporary Economy

View full text Add to dashboard Cite

Connecting many products to the Internet, presence of sensors, wireless communications expansion, robot and intelligent machine development, real-time data analysis have the potential to transform how travel and tourism are made. The benefits of developing intelligent applications in the field of tourism consist in recognizing enabling technologies as a real catalyst in driving business efficiency for the coming years. This paper highlights the fact that Artificial Intelligence (AI), Internet of Things (IoT), Robotics, All Reality are really a major business topic, particularly in the field of tourism, and there is a sufficient capacity for these technologies to support future business travel models. We consider that the conceptual and practical evolution of Industry 4.0 can positively and consistently influence the development of tourism, and there are many similarities between them. Even though the concrete applications of the AI in tourism are still insufficiently elaborated, we believe that extremely rapidly, by increasing the capacity (mobility, speed, volume od data, the ubiquity of data processing and data transfers, interconnection) of intelligent processing, tourism and tourists will benefit from much closer services of concrete needs throughout travel.

show abstract

Section: A General Outllook Of Emerging Technologies That Can Sustainmentioning

confidence: 99%

Tourism and Travel can Effectively Benefit from Technologies Associated with Industry 4.0

Mazilescu¹

2019

Risk in Contemporary Economy

View full text Add to dashboard Cite

show abstract

“…The problem that exists in scheduling is the way of sequencing the objects so as to minimize the overall computational process or time or the resources that are used during the operations. There are many Scheduling Problem exists and many of them have been solved using ACO [16,17] Jing Xiao, et al [18] solved project scheduling problem using ACO. They have proposed a new algorithm called Ant Colony System-Software Project Scheduling Problem (ACS-SPSP) algorithm.…”

Section: Some Of the Classified Problems Under This Vrp Arementioning

confidence: 99%

Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

Thirugnanasambandam¹,

R.S²,

Saravanan³

et al. 2019

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science. OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization. METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP). The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation. RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have been used. CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has also been discussed along with the experimental results.

show abstract

“…We provide the metric that will be used to access this classifier model. Once the model in complied, we fit the data [16] in the neural network. We divide the data into training and testing data.…”

Section: Building An Annmentioning

confidence: 99%

“…Not all lumps or tumours are cancers. Tumours are of two types, Benign and Malignant [16][17][18][19][20]. Malignant tumours are the ones that are categorised as cancers.…”

Section: Introductionmentioning

confidence: 99%

Optimized artificial neural network for classification of biological data

Natarajan

Rajarajan

Subramaniyaswamy

2018

IJET

View full text Add to dashboard Cite

Biological data suffers from the problem of high dimensionality which makes the process of multi-class classification difficult and also these data have elements that are incomplete and redundant. Breast Cancer is currently one of the most pre-dominant causes of death in women around the globe. The current methods for classifying a tumour as malignant or benign involve physical procedures. This often leads to mental stress. Research has now sought to implement soft computing techniques in order to classify tumours based on the data available. In this paper, a novel classifier model is implemented using Artificial Neural Networks. Optimization is done in this neural network by using a meta-heuristic algorithm called the Whale Swarm Algorithm in order to make the classifier model accurate. Experimental results show that new technique outperforms other existing models.

show abstract

A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city

Cited by 73 publications

References 93 publications

Tourism and Travel can Effectively Benefit from Technologies Associated with Industry 4.0

Tourism and Travel can Effectively Benefit from Technologies Associated with Industry 4.0

Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

Optimized artificial neural network for classification of biological data

Contact Info

Product

Resources

About