Design Of Surveillance System Research Articles

Design of an intelligent financial surveillance system using big data analytics for enhanced fraud detection and prevention in financial institutions

Design of an intelligent financial surveillance system using big data analytics for enhanced fraud detection and prevention in financial institutions

Synergizing IoT-Based Gender-Responsive Security Surveillance to Gauge ICT Infrastructure Maturity

Effective planning and implementation of smart security surveillance ensures campus protection for all genders. Thus, the integration of a reliable ICT infrastructure as the core foundation for the security and privacy of the institutions leads to a substantial transformation in the way individuals interact, increasing the confidentiality of the stakeholders. Hence, the study aims to assess the current state of ICT infrastructure and identify the parameters in the design of an IoT-based gender-responsive security surveillance system for Colegio de San Juan de Letran Manaoag. The study implemented a comprehensive review of related literature about maturity models and their capabilities. A focus group discussion was assembled to administer a semi-structured interview on the current state of the ICT infrastructure and conducted an online survey for the assessment of the ICT infrastructure among the administrators and staff of the MIS Department in identifying the strengths and weaknesses of the current ICT infrastructure. Employing the IT Infrastructure Maturity Model (ITI-MM) as a tool in determining the maturity levels of its capabilities. Based on the results, the ITI-MM model determined the current state of the ICT infrastructure, which is currently operating at maturity level 2, and identified the gaps in achieving the requirements between Level 1 and Level 2. In conclusion, the study indicated that measuring of the maturity level is applicable in integrating the proposed system for developing an IoT-based gender-responsive security surveillance system to enhance the security measures of the institution.

One Health governance principles for AMR surveillance: a scoping review and conceptual framework

Abstract Antimicrobial resistance (AMR) is a pressing global health issue with serious implications for health, food security, and livelihoods. Collective action, from local to global, that draws on the One Health (OH) approach to facilitate collaboration between the human, animal, and environmental sectors is required to inform initiatives to mitigate AMR. For AMR surveillance, this involves applying an intersectoral, multistakeholder perspective to guide the co-creation of knowledge and policy around the collection, analysis, and application of surveillance data to detect, monitor, and prevent AMR health threats. Currently, there is little available evidence on how to operationalize a OH approach to support integrated AMR surveillance systems, or on how the governance of such systems facilitates intersectoral action on AMR. We conducted a scoping review of the literature to identify the governance domains most relevant to applying the OH approach to the design and evaluation of AMR surveillance systems. We found that governance is a crucial component of the development of surveillance systems equipped to tackle complex, structural issues such as AMR. The governance domains identified include participation, coordination and collaboration, management, sustainability, accountability and transparency, and equity. These domains are relevant throughout all stages of policy design, implementation, and evaluation of AMR surveillance systems. Equity is both a domain and an essential component of the other domains. All the domains are interdependent and co-constitutive, so that progress in one domain can accelerate progress in another. The conceptual framework presented in this article can inform the design and evaluation of OH AMR governance systems and other complex health challenges that have similar barriers and facilitators to OH governance. The qualitative evaluation questions developed for each domain facilitate assessment of the breadth (the range of actors involved in governance) and depth (how meaningful their engagement is) for each domain relevant to OH governance. Finally, the prioritization of formal, sustainable, and democratic governance of AMR can help to facilitate achievement of the sustainable development goals (SDGs) and promote conservation of the use of antimicrobials for future generations.

Evaluation of the design of the influenza-like illness sentinel surveillance system in Brazil.

The influenza-like illness (ILI) sentinel surveillance operates in Brazil to identify respiratory viruses of public health relevance circulating in the country and was first implemented in 2000. Recently, the COVID-19 pandemic reinforced the importance of early detection of the circulation of new viruses in Brazil. Therefore, an analysis of the design of the ILI sentinel surveillance is timely. To this end, we simulated a sentinel surveillance network, identifying the municipalities that would be part of the network according to the criteria defined in the design of the ILI sentinel surveillance and, based on data from tested cases of severe acute respiratory illness (SARI) from 2014 to 2019, we drew samples for each sentinel municipality per epidemiological week. The draw was performed 1,000 times, obtaining the median and 95% quantile interval (95%QI) of virus positivity by Federative Unit and epidemiological week. According to the ILI sentinel surveillance design criteria, sentinel units would be in 64 municipalities, distributed mainly in capitals and their metropolitan areas, recommending 690 weekly samples. The design showed good sensitivity (91.65% considering the 95%QI) for qualitatively detecting respiratory viruses, even those with low circulation. However, there was important uncertainty in the quantitative estimate of positivity, reaching at least 20% in 11.34% of estimates. The results presented here aim to assist in evaluating and updating the ILI sentinel surveillance design. Strategies to reduce uncertainty in positivity estimates need to be evaluated, as does the need for greater spatial coverage.

Design and Practical Application of a Cost Effective Intelligent Female Surveillance System Using GPS, GSM, and Arduino Technology

This paper suggests a cost effective, portable and intelligent surveillance system for women that they can utilize in risky or emergency situations. In this design, an Arduino Nano microcontroller, a GPS module, and a GSM module are the main parts used. Whenever an emergency arises, the user can instantly send an emergency message to a preset phone number along with their current location. The emergency activation button is in a handy place on the gadget, which is tiny enough to carry in a pocket or purse. This device will improve women's protection and security, particularly in places with high crime rates or poor public safety.

Retracted: Research on Architecture Design of Mobile Video Surveillance System in Information Room Based on Intelligent Robot Technology

Retracted: Research on Architecture Design of Mobile Video Surveillance System in Information Room Based on Intelligent Robot Technology

Respond quickly and effectively! Components of the military health surveillance system in natural disasters: A qualitative study

BACKGROUND: The systematic collection, analysis, and interpretation of health data by health surveillance systems provide timely and comprehensive surveillance of public health, identification health priorities, and, consequently, a quick and timely response to reduce damage during natural disasters. Since military forces appear as first responders at the scene of accidents, the present study aimed to identify the components of the military health care system during natural disasters. MATERIALS AND METHOD: Qualitative data collected through semi-structured interviews were analyzed via the conventional content analysis approach to identify the components of the military health care system in natural disasters. The participants consisted of 13 experts who were experienced in providing health services in the military and the civilian health care system during natural disasters in January 2022 to June 2022. RESULT: The identified components were classified into four main categories, namely, pre-requisite components (comprehensive health care, defined position, and providing information), driving components (system efficiency, effective communication), operational components (contingent performance, effective response), and promotional components (purposeful support, pre-disaster preparation). CONCLUSION: In conclusion, the military health surveillance system is a cooperative service for the national health system in which data is essential for making decisions on health and treatment measures during disasters. This study—by identifying four categories of the important components in the design, implementation, and development of the military health surveillance system—provides a comprehensive view of an appropriate and evidence-based military surveillance system in disasters.

Model-based surveillance system design under practical constraints with application to white-nose syndrome

Model-based surveillance system design under practical constraints with application to white-nose syndrome

A framework for the design, implementation, and evaluation of output-based surveillance systems against zoonotic threats.

Output-based standards set a prescribed target to be achieved by a surveillance system, but they leave the selection of surveillance parameters, such as test type and population to be sampled, to the responsible party in the surveillance area. This allows proportionate legislative surveillance specifications to be imposed over a range of unique geographies. This flexibility makes output-based standards useful in the context of zoonotic threat surveillance, particularly where animal pathogens act as risk indicators for human health or where multiple surveillance streams cover human, animal, and food safety sectors. Yet, these systems are also heavily reliant on the appropriate choice of surveillance options to fit the disease context and the constraints of the organization implementing the surveillance system. Here we describe a framework to assist with designing, implementing, and evaluating output-based surveillance systems showing the effectiveness of a diverse range of activities through a case study example. Despite not all activities being relevant to practitioners in every context, this framework aims to provide a useful toolbox to encourage holistic and stakeholder-focused approaches to the establishment and maintenance of productive output-based surveillance systems.

Design and Implementation of Home Video Surveillance Systems Based on IoT Location Service

Due to its simplicity, directness, and rich content, home video surveillance has gradually become the core content of the smart home security system. The rapid development of computer technology, the internet, video image processing, and transmission technology, as well as the emergence of “wireless cities,” have continuously expanded the coverage of wireless internet such as 5G and WiFi and greatly changed data processing methods and transmission speeds. Video surveillance has also gradually developed from traditional wired networking to mobile video surveillance of wireless networks. Therefore, this paper will focus on the design of the home video monitoring system based on the IoT location service. The system combines traditional network video surveillance with intelligent mobile terminals, which can meet the needs of users for video surveillance of home conditions at any location and at any time. And it compared the sensitivity, accuracy, capture time interval, and energy consumption of the two by comparing with traditional internet video surveillance.

VIBERSHIELD: An Intrusion Detection System

Abstract: Dramatic increase in unlawful intruder movements throughout the countries boundaries demands design and development of unified border surveillance and intruder detection system. The purpose of this research aims to analyse the factors which affects the detection of vibrations that originates from underground or the ground surface resulting from footsteps, vehicles and animals. It also aims to detect and differentiate the vibrations and alarming the base camp using geophones and LORA communication.

Design of Road surveillance system for low visibility, bad weather and emergency situations

Traffic accidents are major causes of death and disability worldwide. Lack of visibility due to Fog and darkness are main causes of accidents. These accidents may turn into fatalities and long traffic jams if proper actions are not taken. In this paper we initially review the accident data and scenarios and then propose a road surveillance system in low visibility and emergency. This proposed system that mainly performs three tasks as turning ON road-alert lights, if a vehicle unexpectedly stops on road-side due to any technical fault or accidents. This will help upcoming vehicles to have prior information of unexpectedly stopped vehicle that will reduce accident due to vehicles collision, other side turning ON road-street lights automatically with darkness detection. This will reduce accident by improving visibility in night or fog and Send Emergency Notification to the control room if vehicle remain still for more than 20 seconds. This will help in taking proper and timely action in case of emergencies avoiding fatalities and traffic jams for hours. The system is based on Arduino and GSM module and tested for each of the three scenarios (fog, darkness, emergency). The result shows that the proposed system worked efficiently in each condition and can be used to improve traffic safety.

Human Behavior Recognition of Video Surveillance System Based on Neural Network

Video surveillance system is widely used in modern society. Human behavior recognition based on neural network uses computer vision and image processing technology to analyze and judge the behavior of different objects in surveillance video. In order to improve the recognition accuracy of human behavior, this paper makes an in-depth study on the design of video surveillance system and target tracking using the relevant algorithms of neural network. In this paper, the application of relevant neural network algorithms and the technology of character behavior recognition are analyzed by means of experimental method comparison and algorithm comparison. The experimental data shows that in the detection of abnormal behavior, the highest accuracy rate of human behavior recognition is 90% by using the improved hu invariant moment model.

The Need to Address Fragmentation and Silos in Mortality Information Systems: The Case of Ghana and Peru.

Objectives: We aimed to understand the information architecture and degree of integration of mortality surveillance systems in Ghana and Peru. Methods: We conducted a cross-sectional study using a combination of document review and unstructured interviews to describe and analyse the sub-systems collecting mortality data. Results: We identified 18 and 16 information subsystems with independent databases capturing death events in Peru and Ghana respectively. The mortality information architecture was highly fragmented with a multiplicity of unconnected data silos and with formal and informal data collection systems. Conclusion: Reliable and timely information about who dies where and from what underlying cause is essential to reporting progress on Sustainable Development Goals, ensuring policies are responding to population health dynamics, and understanding the impact of threats and events like the COVID-19 pandemic. Integrating systems hosted in different parts of government remains a challenge for countries and limits the ability of statistics systems to produce accurate and timely information. Our study exposes multiple opportunities to improve the design of mortality surveillance systems by integrating existing subsystems currently operating in silos.

Detecting New Sources of Childhood Environmental Lead Exposure Using a Statistical Surveillance System, 2015-2019.

Objectives. To design and implement a statistical surveillance system to prospectively identify potential clusters of elevated blood lead levels (EBLLs) in children younger than 6 years in the Denver, Colorado, metro area. Methods. We evaluated the ability of 2 independent statistical surveillance methods to detect synthetic clusters of EBLLs in Denver between 2015 and 2019. Results. Together, the statistical surveillance methods took an average of 9 months to detect the synthetic clusters. This is faster than similar real-world clusters that have been reported in the past. The system was relatively unaffected by changes in the testing rate and to the blood lead reference value. Conclusions. The adequate design of a statistical surveillance system can help increase the rate at which clusters of EBLLs are detected in Denver, but doing so requires an accurate model of the spatial distribution of EBLLs. Earlier detection of clusters can help guide more effective public health interventions at the local level. (Am J Public Health. 2022;112(S7):S715-S722. https://doi.org/10.2105/AJPH.2022.307009).

How do the practical and pragmatic limitations in the design or implementation of wildlife disease surveillance systems bias our understanding of the drivers, epidemiology, and impact of pathogen traffic between wildlife and people or domestic species, or within wildlife host populations?

Decision makers want more information on the presence, distribution, movement, and impacts of pathogens in wild animals to anticipate or assess the risk of pathogen spillover from wildlife to people or domestic animals or to better understand the conservation implications of contagious diseases in wildlife. There are many impediments to designing and implementing a wildlife health surveillance system that fulfils all the expectations for ‘good’ surveillance. There are well-known pragmatic limitations such as the inability to sample targeted populations consistently and regularly in a representative manner, lack of validated tests, and funding limitations that restrict surveillance to periodic surveys. The nature and impact of the biases on data collection, generation, management, quality, analysis, interpretations, and information sharing are poorly studied. We invite authors to address the question in the title.

Object Detection with Theft Alert Using Tensorflow and Yolo

Theft is a common criminal activity that is prevailing over the years and is increasing day by day. To tackle this problem many surveillance systems have been introduced in the market. Some are simply based on video surveillance monitored by a human while some are AI-based capable of detecting suspicious activity and raising an alarm. However, none of them are intelligent enough to identify what kind of suspicious activity is being carried out and what kind of protective measures should be taken in real-time. This blog presents the design of an effective surveillance system using machine learning techniques.

CREATION OF OBJECT RECOGNITION AND TRACKING SYSTEM

In modern society, video recording systems have become widespread, allowing them to recognize objects, their absence, and changes in position. In the vast majority of intelligent systems for video monitoring and determining objects through their image, such objects as human faces, printed publications, and state registration numbers are taken into consideration. At the same time, the circumstances for accepting an image are quite strict, since there is a limit on illumination, background, location relative to the lens, and so on. All this significantly facilitates the joint work of a person with a computer, and creates the prerequisites for using all kinds of systems of artificial origin of the mind. The primary goal in the development of a method and software for the automatic design of a video surveillance system is the purpose of recognize an object whose image is transmitted through the camera. Since the image of any object depends on many moments of its direction about the video camera, illumination, characteristics of the recorder, static and dynamic characteristics of the object, it is rather difficult to arrange and present a picture in the guise of a specific mathematical model. As a result, the methods for implementing a computer representation are significantly dependent on the goals being solved and are occasionally inferior to generalization. As a result, the bulk of these methods is considered non-linear. This affects the need to accumulate the calculation of computer power and the difficulty of algorithms for work acquired through the technical channels of the resulting image. In addition to technical indicators that distort the quality of a digital image, several external moments are considered, and these are: lighting around the scene, moving objects within it, etc. As a result, to obtain the best accuracy of character recognition, it is necessary to take into account all the details. The aim of this work is to study the topic of object recognition in images and video, in order to further use the results of work in creating a system for object recognition in images and video.

Adaptive risk-based targeted surveillance for foreign animal diseases at the wildlife-livestock interface.

Animal disease surveillance is an important component of the national veterinary infrastructure to protect animal agriculture and facilitates identification of foreign animal disease (FAD) introduction. Once introduced, pathogens shared among domestic and wild animals are especially challenging to manage due to the complex ecology of spillover and spillback. Thus, early identification of FAD in wildlife is critical to minimize outbreak severity and potential impacts on animal agriculture as well as potential impacts on wildlife and biodiversity. As a result, national surveillance and monitoring programs that include wildlife are becoming increasingly common. Designing surveillance systems in wildlife or, more importantly, at the interface of wildlife and domestic animals, is especially challenging because of the frequent lack of ecological and epidemiological data for wildlife species and technical challenges associated with a lack of non-invasive methodologies. To meet the increasing need for targeted FAD surveillance and to address gaps in existing wildlife surveillance systems, we developed an adaptive risk-based targeted surveillance approach that accounts for risks in source and recipient host populations. The approach is flexible, accounts for changing disease risks through time, can be scaled from local to national extents and permits the inclusion of quantitative data or when information is limited to expert opinion. We apply this adaptive risk-based surveillance framework to prioritize areas for surveillance in wild pigs in the United States with the objective of early detection of three diseases: classical swine fever, African swine fever and foot-and-mouth disease. We discuss our surveillance framework, its application to wild pigs and discuss the utility of this framework for surveillance of other host species and diseases.

Approximating the Pareto frontier for a challenging real-world bi-objective covering problem

We study a bi-objective covering problem stemming from a real-world application concerning the design of camera surveillance systems for large-scale outdoor areas. It is in this application prohibitively costly to surveil the entire area, and therefore necessary to be able to present a decision-maker with trade-offs between total cost and the portion of the area that is surveilled. The problem can be stated as a set covering problem with two objectives, describing cost and portion of covering constraints that are fulfilled. Finding the Pareto frontier for these objectives is very computationally demanding and we therefore derive a method for finding a good approximate frontier in a practically feasible computing time. The method is based on the ϵ-constraint reformulation, an established heuristic for set covering problems, and subgradient optimization.