When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
Animals ; Support Vector Machine ; Whales ; Eye Movements ; Algorithms

【Objective】 To explore the viability and advantages of drones in blood emergency delivery. 【Methods】 The delivery of emergency blood by drones to the Second People′s Hospital of Yuhang District(referred as Yuhang Hospital) and the Second Affiliated Hospital of Zhejiang University Medical College (referred as Binjiang Hospital) was analyzed retrospectively. The 8: 00-24: 00 traffic condition, at the interval of 2h, of working days were inquired by Baidu Map in order to compare the driving time with drone flight time. The temperature of RBCs and platelets during drone flight were monitored, and take-off and landing temperature were compared. 【Results】 47 deliveries (a total of 192 bags, 295 U) of suspended RBCs and 35 deliveries (a total of 113 bags, 159.5 therapeutic dose) of platelets were, respectively, conducted to Yuhang Hosital and Binjiang Hospital. Two transfer stations for battery charging were needed during the delivery to Yuhang Hospital, and the average one-way time by driving was similar with by drones(50 vs 55 min), without any superiority in time-efficiency. Binjiang Hospital, however, benefited from this drone delivering(driving 10mins vs drone 6 mins). As round-trip delivery for emergency blood was saved, it’s economical for the hospital to get the time-sensitive blood timely. The temperature of suspended RBCs and platelets during flight was between 2.1~7.9 ℃ and 20.2~24.2 ℃, with temperature difference at 0.3~3.7 ℃ and 0.3~3.6℃, respectively. 【Conclusion】 Drones, with good application prospects, can be applied in emergency blood delivery, and further study is needed to improve the time-efficiency and cold chain monitoring system.