A Preliminary Review of Behavioural Biometrics for Health Monitoring in the Elderly

This article explores the potential of ICT-based biometrics for monitoring the health status of the elderly people. It departs from specific ageing and biometric traits to then focus on behavioural biometric traits like handwriting, speech and gait to finally explore their practical application in health monitoring of elderly

Quantum Artificial Intelligence Supported Autonomous Truck Platooning

Truck platooning can potentially increase the operational efficiency of freight movement on U.S. corridors, improving commercial productivity and economic vibrancy. Predicting each leader vehicle trajectory in the autonomous truck platoon using Artificial Intelligence (AI) can enhance platoon efficiency and safety. Reliance on classical AI may not be efficient for this purpose as it will increase the computational burden for each truck in the platoon. However, Quantum Artificial Intelligence (AI) can be used in this scenario to enhance learning efficiency, learning capacity, and run-time improvements. This study developed and evaluated a Long Short-Term Memory Networks (LSTM) model and a hybrid quantum-classical LSTM (QLSTM) for predicting the trajectory of each leader vehicle of an autonomous truck platoon. Both the LSTM and QLSTM provided comparable results. However, Quantum-AI is more efficient in real-time management for an automated truck platoon as it requires less computational burden. The QLSTM training required less data compared to LSTM. Moreover, QLSTM also used fewer parameters compared to classical LSTM. This study also evaluated an autonomous truck platoon\u27s operational efficacy and string stability with the prediction of trajectory from both classical LSTM and QLSTM using the Intelligent Driver Model (IDM). The platoon operating with LSTM and QLSTM trajectory prediction showed comparable operational efficiency. Moreover, the platoon operating with QLSTM trajectory prediction provided better string stability compared to LSTM

Optimal Task Scheduling in the Cloud Environment using a Mean Grey Wolf Optimization Algorithm

Cloud computing is one of the emerging areas in computing platforms, supporting heterogeneous, parallel and distributed environments. An important challenging issue in cloud computing is task scheduling, which directly influences system performance and its efficiency. The primary objective of task scheduling involves scheduling tasks related to resources and minimizing the time span of the schedule. In this study, we propose a Modified Mean Grey Wolf Optimization (MGWO) algorithm to enhance system performance, and consequently reduce scheduling issues. The main objective of this method is focused upon minimizing the makespan (execution time) and energy consumption.  These two objective functions are elaborated in the algorithm in order to suitably regulate the quality of results based on response, in order to achieve a near optimal solution. The implementation results of the proposed algorithm are evaluated using the CloudSim toolkit for standard workloads (normal and uniform). The advantage of the proposed method is evident from the simulation results, which show a comprehensive reduction in makespan and energy consumption. The outcomes of these results show that the proposed Mean GWO algorithm achieves a 8.85% makespan improvement compared to the PSO algorithm, and 3.09% compared to the standard GWO algorithm for the normal dataset. In addition, the proposed algorithm achieves 9.05% and 9.2% improvement in energy conservation compared to the PSO and standard GWO algorithms for the uniform dataset, respectively

Development of a Drone-Supported Emergency Medical Service

There is a scientific consensus that the delivery of prompt emergency medical services (EMSs) guarantees a higher survival rate. An EMS is generally able to respond to 90% of higher priority calls in less than 9 minutes, with the best chance of survival being with a response time of 4–5 minutes. The major obstacle here is that a shorter response time would require the needed resources not to pass a certain threshold in a cost/benefit analysis. This paper aims to investigate the use of drones in as an EMS to improve response times. Although the literature already provides many examples of drones used for this purpose, they have all been developed as a prototype. This confirms the technical feasibility of a drone-based solution, but there is no evidence of the economic viability for such a service. The answer to this comes by analyzing the performance of an integrated-with-drones service as a whole. For this reason, we have redesigned the entire EMS model by including drones, and we have addressed the main issues, such as which types of service can be provided from drones, in which case, what the technical requirements for drones would be, and so on. Furthermore, we developed a specific procedure to keep the number of drones at a minimum level under the constraint of the minimum intervention time. The proposed model has been applied to a real EMS case for a city in the south of Italy. The outcome was that 96 drones were able to cover an area of 2,800 km2, providing an intervention time of 4.5 minutes on average at an annual cost of less than €300,000. These results highlight that an integrated-with-drones service drastically improves the response time when compared with the traditional service, doing so at a viable cost

STRATEGY DEVELOPMENT OF SALES ORGANIZATION USING FUZZY AHP: DIGITAL TRANSFORMATION OF FMCG

A clear understanding of internal strength allows the company to innovate and adapt in a highly competitive Fast-moving consumer goods market. Therefore, the company's ability to oversee risk and opportunities determines the company's resilience. By employing homogeneous purposive sampling, which focused on the member population of 11 FMCG companies, the present study processed quantitative data from a questionnaire using a Likert scale and qualitative data through in-depth interviews with stakeholders. The data was collected through one-on-one in-depth interviews with 11 respondents online and offline in 12 major cities from August to November 2022. Later, questionnaires were processed using AHP Fuzzy to explain and take into account the role of decision-makers resembling FMCG leaders by defining interactive factors, actors, objectives, and strategies. Factor-actor analysis found that the sales director was the actor with the most influential role in the leadership factor, the sales manager was influential in the Organizational Citizenship Behavior factor, and the sales director was a prominent actor in sales management control. The main goal of the president director, IT/Digital director, supply chain director, and sales director in digital transformation is increasing effectiveness/efficiency in business processes. Besides, the sales manager and sales supervisor aspire to achieve sales targets or sales growth set by the company. In increasing effectiveness/efficiency and achieving sales targets/sales growth set by the company, prioritized strategy can be done through the development of leadership, capability, and human resource capacity. Besides, customer/business partner-oriented digitalization is vital for increasing customer/business partner satisfaction with the company's services/business processes. Furthermore, the present study found leadership models as critical for digital transformation with the realistic scenario (iterative improvement) in all likelihood. Sales organizations are expected to consistently and continuously conduct experiments to find new ways of working and produce digital initiatives that companies need to answer customer/consumer demands. Those strategies can be done by clearly identifying factors, actors, objectives, and strategies for better business execution in digital transformation