智能计算与安全系统：第六卷无水印原版PDF

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"Advanced Computing and Systems for Security Volume Six 无水印原版pdf" 该资源是一本关于高级计算和安全系统的书籍，属于"Advances in Intelligent Systems and Computing"系列的第六卷，由Rituparna Chaki、Agostino Cortesi、Khalid Saeed和Nabendu Chaki等编辑。这本书主要关注智能系统和计算在安全领域的应用与进展，旨在深入探讨和展示智能计算技术在保障安全方面的最新研究成果。书中涵盖了广泛的领域，包括但不限于工程、自然科学、计算机和信息科学、ICT（信息通信技术）、经济学、商业、电子商务、环境、医疗保健以及生命科学。具体到安全领域，可能涉及的议题有： 1. 计算智能：讨论如何利用智能算法来解决复杂的安全问题，例如预测分析、入侵检测系统等。 2. 软计算：涵盖神经网络、模糊系统、进化计算以及这些范式的融合，它们在网络安全策略、风险评估等方面的应用。 3. 社会智能：研究社交媒体和网络行为数据如何用于识别威胁和异常行为。 4. 语境感知计算：研究如何根据用户环境和上下文信息提供个性化的安全服务。 5. 计算神经科学：探索大脑的工作机制如何启发新的安全算法设计。 6. 人工智能与虚拟社会：研究虚拟世界中的安全挑战，如虚拟身份验证和隐私保护。 7. 认知科学与系统：研究人类认知过程如何帮助构建更适应性的安全系统。 8. 视觉与感知：利用计算机视觉技术进行监控和异常检测，以增强物理空间的安全性。 9. DNA计算与免疫计算：借鉴生物学原理开发新型的安全防御策略。这本书对于IT行业的专业人士，尤其是从事安全研究和实践的人员，提供了宝贵的理论知识和实践案例，有助于他们了解和掌握智能计算在安全领域的最新发展动态。通过阅读本书，读者可以拓宽视野，提高对复杂安全问题的解决方案设计和实施能力。

6 P. Sarkar et al.

for sequencing problems to nurse re-rostering problem under hard constraints. The

complexity of this problem led the authors to design heuristic procedures. Another

paper [18] proposes a strategy to improve performance of GA for NSP by using cost

bit matrix for genetic operators, where the mutation operator was applied based on the

value in the cost bit matrix also. Thus, search space and execution time are reduced.

A two-stage mathematical modelling [19] is developed for a nurse scheduling system

which shows that different infeasibilities of genetic algorithm approaches in nurse

scheduling problems are used to ﬁnd a better feasible solution [20]. A combined

genetic algorithm with hyper simulated annealing is tested for stochastic ranking

method [14] of nurse scheduling and provides higher performance than GA with

penalty cost. But the premature convergence of traditional genetic algorithm is solved

by using memetic algorithm [21, 22]. A combined local search based algorithm hill

climbing is incorporated in genetic algorithm to result in better performance of NP-

hard nurse scheduling problem. In [5], the addition of the hill climbing operator after

each mutation operator greatly increases the speed at which better solutions are found

above the evolutionary operators alone whereas violation directed hierarchical hill

climbing [11] is executed with GA applying various crossover operators to compare

among them.

3 Research Scope

As observed from the study of existing works in this area, we have noted the research

gaps as follows:

• Existing works have not considered real-time dynamic decision-making to identify

the appropriate nurses for replacement or shufﬂing according to the priority level

of the patients.

• The existing works tend to have problems in deciding upon the multi-objective

function considering soft and hard constraints, only in speciﬁc time period and for

speciﬁc use case.

• There is lack of discussion of nurse assignment in emergency situation.

• Existing works did not quantify the amount of duty hours to which extent a nurse

is utilized in periodic assignment.

8 P. Sarkar et al.

4.2 Description of Proposed Nurse Assignment Methods

The proposed nurse assignment method for variable demand has been conceptualized

in three different aspects which are discussed as follows.

Central Nursing Care Service Module

The aim of this module is to generate and maintain a master nurse-base. Nurse web

server is used to store all information after the nurse’s registration along with other

scheduling information which are transferred in-between all sub-modules.

Home Care Nursing Service

This module is designed to provide nursing service to patients at their respective

homes. The request as per patient’s severity is communicated to central nursing

care service module. These schedules are updated as per the proposed variable time

quantum nurse scheduling (VTQNS) algorithm which is designed based on variable

request time and the response time for different patients according to their priorities

(Table 1).

Deﬁnition

Time quantum (TQ): It is deﬁned as the variable total time which is evaluated as

total duty hours T

served by a nurse plus waiting time WT for each patient who is

assigned to that nurse.

TQ  T

+WT (1)

Turnaround Time (TAT): It is the overall time required for a nurse to be assigned

to a remote patient, as given in Eq. (2).

TAT 



Start Time

(

)

− Request time



req



+CS



(2)

Trafﬁc state function f

(

j, P

[

]

)

: The state of the trafﬁc on the high road is mea-

sured by the probability ofoccurrences of the trafﬁc jam atfour different times in a day

such as EARLY MORNING(0), MID DAY(1), AFTERNOON(2), LATE NIGHT(3).

The state of the trafﬁc is expressed as a function f

(

j, P

[

]

)

of jth shift and trafﬁc

probability, where P[k] is the probability of occurrences of k number of vehicles for

trafﬁc jam in the high road, as 0.5 < P[k] ≤ 1. Following the Poisson’s distribution,

G is the mean number of trafﬁc per unit time. If J ∈ D where D 

{

0, 1, 2, 3

}

then, when j ∈ J

{

0, 3

}

, P[k] < 0.5, and when j ∈ J

{

1, 2

}

,0.5<P[k] ≤ 1.

(

j, P

[

]

)

 +1 if j

∈ J

{

0, 3

}

and P

[

]

< 0.5

 0, if j ∈ J

{

1, 2

}

and 0.5 ≤ P

[

]

≤ 1(3)

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