To reduce manual tasks and to minimise the risks from a customer, many insurance companies have applied ar-tificial intelligence (AI) solutions, including, but not limited to, machine learning (ML) and deep learning (DL). Exclusion anal-ysis is one of the primary tasks in terms of minimising the risks a customer imposes on a life insurance company. Although a few research studies have made this the primary focus, they have yet to provide explainable research for the exclusion analysis to assist the underwriting process (UP) using ML/DL methods. Therefore, this paper makes the process of exclusion classification, along with its explainability, its primary concentration to assist the underwriters in understanding the underwriting data taken from the customer disclosure information. First, we explore this problem by applying a set of four multi-label classifiers (named binary relevance, classifier chains, label powerset, and ensemble learning) blended with five ML techniques (named multinomial Naive Bayes, support vector classifier, logistic regression, random forest, decision tree), using the data provided by one of the leading insurance companies in Australia. Then, we consider the best-performing model's classification probability and feature importance as input for the explainable ML system - Shapley additive explanations and introduced explainability outcome - as a quality assurance report (QAR). This paper offers an extensive empirical evaluation by comparing different metrics and human underwriters' reviews. Finally, the result demonstrates that the binary relevance algorithm combined with the decision tree classifier outperforms other existing methods for explainable exclusion, providing a better overview of the customer's risk profile.

Measurement data from wireless sensors deployed in large agricultural areas could be used to help the automation of precision farming activities such as irrigation management, fertilization, etc. The widespread use of sensors with limited battery capacity in precision farming largely depends on data collection methods that reduce the energy consumption of transmitting measurement data and prolong the battery run time. In this article, we investigate joint clustering and multi-UAV-assisted data-gathering schemes to save the energy consumption of sensors. We establish a theoretical lower bound for the energy consumption of sensors to transport data to cluster heads and prove that the energy consumption of sensors approaches the theoretical lower bound if clusters are balanced regarding energy consumption. Therefore, the essential step of proposed heuristic multi-UAV schemes, calledGathering data Assisted by Multi-UAV with a BAlanced Clustering(GAMBAC), is to find balanced or near-balanced clusters concerning energy consumption. For sensor networks with a small number of nodes our heuristic algorithms give results close to the ones obtained by the reference solution. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Therefore, the GAMBAC algorithms could enhance the reliable data collection of sensor networks for precision farming.

Abstract A quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+ $$\epsilon$$ ϵ )-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well.

Low communication latency becomes a main concern in highly parallel computers and supercomputers that reach millions of processing cores. Random network topologies are better suited to achieve low average shortest path length and low diameter in terms of the hop counts between nodes. However, random topologies lead to two problems: (1) increased aggregate cable length on a machine room floor that would become dominant for communication latency in next-generation custom supercomputers, and (2) high routing complexity that typically requires a routing table at each node (e.g., topology-agnostic deadlock-free routing). In this context, we first propose low-degree non-random topologies that exploit the small-world effect, which has been well modeled by some random network models. Our main idea is to carefully design a set of various-length shortcuts that keep the diameter small while maintaining a short cable length for economical passive electric cables. We also propose custom routing that uses the regularity of the various-length shortcuts. Our experimental graph analyses show that our proposed topology has low diameter and low average shortest path length, which are considerably better than those of the counterpart 3-D torus and are near to those of a random topology with the same average degree. The proposed topology has average cable length drastically shorter than that of the counterpart random topology, which leads to low cost of interconnection networks. Our custom routing takes non-minimal paths to provide lower zero-load latency than the minimal custom routings on different counterpart topologies. Our discrete-event simulation results using SimGrid show that our proposed topology is suitable for applications that have irregular communication patterns or non-nearest neighbor collective communication patterns.

There are two main requirements in dealing with coverage holes in wireless sensor networks (WSNs): locating the hole boundary and finding the locations to deploy new sensors for hole patching. The current protocols on finding the patching locations always require re-running the protocols from scratch many times. This constraint causes the time complexity and energy overhead to increase proportionally to the hole size. In this paper, we propose a lightweight protocol to determine coverage holes in wireless sensor network. Our protocol does not only can determine the exact hole boundary but also approximate the boundary by a simpler shape which can help to speed up the patching location finding process. The simulation experiments show that our protocol can reduce more than 56% of time complexity and save more than 46% of energy overhead in comparison with existing protocols.

The routing stretch in a sensor network with a large hole can be controlled if the network nodes have certain information about the hole location and shape, possibly by using some mechanism for hole boundary approximation and information dissemination. However, a finer approximation (for smaller stretch) leads to higher cost of dissemination, which could be unaffordable for the sensors. We provide an insightful analysis to this fundamental trade-off of the hole approximation problem, using a geometric model. Based on this we discuss an efficient approximation-dissemination scheme using a natural, heuristic approximation technique. For reasonable conditions (network is dense enough apart from the hole), we assure that the routing stretch is under a predefined threshold > 1 while the dissemination cost is also under tight control. Our initial experiment results also confirm that our approach is significantly more efficient and economical, compared to existing proposals.