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An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

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Evidence-Based Quality-Aware Agile software Development Process: Design and Evaluation

SSRN

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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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A Review of Artificial Intelligence in Serious Game for Public Health

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Journal of Physics: Conference Series 2021年第1期1830卷

作者： Abdul Rafiq Tuty Asmawaty Abdul Kadir Siti Normaziah Ihsan Soft Computing and Intelligent System Research Group (SPINT) Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia

Serious games used in public health for purposes including training, learning, prediction, coaching, diagnostic, rehabilitation and supporting. However, the serious game focuses mainly on user experience and graphical application, but less attention on applying artificial intelligence (AI). By enhancing artificial intelligence, it will have the capability to solve issues, especially in the field of public health. This article discussed a review of the use of artificial intelligence in a serious game for public health. The main idea of this paper is to gather all the related articles and create a trend analysis of the use of Artificial Intelligence (AI) in a serious game for public health. The related articles were applied to Artificial intelligence (AI) in the area of decision-making. The final section discussed the new trend and the future of Artificial intelligence using current AI techniques.

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Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Research Square

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Research Square 2021年

作者： Ryan, Conor Kshirsagar, Meghana Vaidya, Gauri Cunningham, Andrew Sivaraman, R. Biocomputing and Developmental Systems Group Lero The Science Foundation Ireland Research Centre for Software Computer Science and Information System Department University of Limerick LimerickV94 T9PX Ireland Intel Research and Development Ireland Limited LeixlipW23 CX68 Ireland Department of Electronics Communication Engineering School of Electrical and Electronics Engineering Shanmugha Arts Science Technology and Research Academy Deemed to be University Thanjavur613401 India

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve. © 2021, CC BY.

关键词： Monte Carlo methods

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Indoor localization estimation techniques in wireless sensor network: A review

Indoor localization estimation techniques in wireless sensor...

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2018 IEEE International Conference on Automatic Control and Intelligent systems, I2CACIS 2018

作者： Shahkhir Mozamir, M. Bakar, Rohani Binti Abu Din, Wan Isni Soffiah Wan Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia Soft Computing and Intelligent System Research Group Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia System Network and Security Research Group Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang Malaysia

ISBN: (纸本)9781538656549

Recently, Wireless Sensor Network (WSN) are an important research area because of its real-time response, accurate, improved node capability, low in cost and simple infrastructure. Because of the huge number of sensor nodes, the main issue of WSN is the estimation of their location. The nodes which are deployed may not be known their own position. In localization, different techniques are used for estimation to gain the coordinate of the node. In this paper, we reviewed on the current techniques in estimation phase for indoor localization from 2013 to 2018. This reviewed will be useful for the researchers to implement the techniques for estimation phase to increase the accuracy. ©2018 IEEE.

关键词： Sensor nodes

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Assessing Safety-Critical systems from Operational Testing: A Study on Autonomous Vehicles

arXiv

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arXiv 2020年

作者： Zhao, Xingyu Salako, Kizito Strigini, Lorenzo Robu, Valentin Flynn, David Smart System Group School of Engineering and Physical Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Centre for Software Reliability School of Mathematics Computer Science and Engineering City University of London Northampton SquareEC1V 0HB United Kingdom

Context: Demonstrating high reliability and safety for safety-critical systems (SCSs) remains a hard problem. Diverse evidence needs to be combined in a rigorous way: in particular, results of operational testing with other evidence from design and verification. Growing use of machine learning in SCSs, by precluding most established methods for gaining assurance, makes evidence from operational testing even more important for supporting safety and reliability claims. Objective: We revisit the problem of using operational testing to demonstrate high reliability. We use Autonomous Vehicles (AVs) as a current example. AVs are making their debut on public roads: methods for assessing whether an AV is safe enough are urgently needed. We demonstrate how to answer 5 questions that would arise in assessing an AV type, starting with those proposed by a highly-cited study. Method: We apply new theorems extending our Conservative Bayesian Inference (CBI) approach, which exploit the rigour of Bayesian methods while reducing the risk of involuntary misuse associated (we argue) with now-common applications of Bayesian inference;we define additional conditions needed for applying these methods to AVs. Results: Prior knowledge can bring substantial advantages if the AV design allows strong expectations of safety before road testing. We also show how naive attempts at conservative assessment may lead to over-optimism instead;why extrapolating the trend of disengagements (take-overs by human drivers) is not suitable for safety claims;use of knowledge that an AV has moved to a "less stressful" environment. Conclusion: While some reliability targets will remain too high to be practically verifiable, our CBI approach removes a major source of doubt: it allows use of prior knowledge without inducing dangerously optimistic biases. For certain ranges of required reliability and prior beliefs, CBI thus supports feasible, sound arguments. Useful conservative claims can be derived from limite

关键词： Autonomous vehicles

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Development Of Cross-Sectoral Active Network Management systems Using New engineering Paradigms

Development Of Cross-Sectoral Active Network Management Syst...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Marcel Ludwig Jan Mehlich Schaugar Azad Markus Zdrallek Christian Thommessen Nicolas Witte Nils Loose Ralf Settertobulte Uwe Weber Institute of Power System Engineering University of Wuppertal Wuppertal Germany Chair of Energy Technology University of Duisburg-Essen Duisburg Germany Research Group for Business Informatics and Software Engineering University of Duisburg-Essen Essen Germany Stadtwerke Lemgo GmbH Lemgo Germany

An Active Network Management system comprises a large number of different technical systems that interact in several ways to pursue local objectives. The Smart Grid Architecture Model was developed for the description of the coupling of these technical systems and scenarios within an Active Network Management system. In this paper, this modelling paradigm is applied for the requirements engineering and a uniform description of a cross-sectoral Active Network Management system. Essential requirements are shown and described in relation to the two upper layers of the Smart Grid Architecture Model. Based on the requirements engineering results, the implementation of a multi agent system is proposed. For the development of this multi agent system, a new software engineering paradigm is applied. Finally, the functionality use-case developed cross-sectoral Active Network system are described, which will be investigated in a later field test.

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Key Enabling Technologies for Drones

Key Enabling Technologies for Drones

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Euromicro Symposium on Digital system Design

作者： Mahmoud Hussein Réda Nouacer Yassine Ouhammou Eugenio Villar Federico Corradi Carlo Tieri Rodrigo Castiñeira Software and System Engineering Department (DILS) P.C. 174 CEA LIST Gif-sur-Yvette France Faculty of Computers and Information Menofia University Egypt LIAS/ISAE-ENSMA Futuroscope France Microelectronics Engineering Group University of Cantabria Santander Spain Ultra Low Power Systems for IoT Stichting IMEC Nederland Eindhoven The Netherlands TEKNE S.R.L Province of Chieti Italy INDRA SISTEMAS SA (INDRA) Calle Villalar 3 Madrid Spain

ISBN: (数字)9781728195353

ISBN: (纸本)9781728195360

The idea of having drones into the national airspace raises serious concerns. These concerns are for nearly all spectrum of society which ranges from government facilities and aviation authorities to private citizens. To guarantee a high level of safety and security, drones must be implemented as highly constrained systems with a certain number of functions (technologies). In this paper, we identify the key technologies for drones based on their common and specific usages. These technologies are grouped into four categories: U-space capabilities, system functions, payloads, and tools. We also list the contributions of COMP4DRONES project in terms of improving technologies and easing drone customization including its safe operations.

关键词： Drones Payloads Agriculture Sensors Surveillance Navigation Tools

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The behavior, constraint, and scenario (BeCoS) tool: A web-based software application for modeling behaviors and scenarios

The behavior, constraint, and scenario (BeCoS) tool: A web-b...

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AIAA Aerospace Sciences Meeting, 2018

作者： Kaderka, Justin D. Rozek, Matthew L. Arballo, John K. Wagner, David A. Ingham, Michel D. System Architectures and Behaviors Group Jet Propulsion Laboratory California Institute of Technology M/S 321-425P PasadenaCA91109 United States Flight System Systems Engineering Group Jet Propulsion Laboratory California Institute of Technology M/S 321-525E PasadenaCA91109 United States Structure of the Universe Group Jet Propulsion Laboratory California Institute of Technology M/S 169-327 PasadenaCA91109 United States System Architectures and Behaviors Group Jet Propulsion Laboratory California Institute of Technology M/S 301-445P PasadenaCA91109 United States Software and Information Systems Engineering Group Jet Propulsion Laboratory California Institute of Technology M/S 321-541 PasadenaCA91109 United States

ISBN: (数字)9781624105241

ISBN: (纸本)9781624105241

The Behavior, Constraint, and Scenario (BeCoS) tool has been developed to allow engineers to specify system and component behaviors. The tool is a web application that is developed in JavaScript and uses the React framework for the user interface and Redux for maintaining application state. The foundation of the tool is its underlying ontology, which expands upon a previously-defined behavior ontology with a scenario ontology. The behavior ontology includes elements like behaving elements, state variables, parameters, and constraints, while the scenario ontology includes core constructs like activities, temporal constraints, and timepoints. BeCoS allows users to easily create behaving elements and to specify their state variables, parameters, state machines, and constraints. BeCoS also allows users to develop temporal constraint networks that specify constraints on component states over time. BeCoS is a prototype tool that has been deployed and tested by systems engineers on the Europa Clipper project, which generated several use cases and helped steer its current developmental effort. By enabling systems engineers to specify behavior in a semantically-rigorous manner, BeCoS is an enabling technology for analyses that previously could not be performed, and when exporting its model to other tools, allows for consistent behavior models to be used. © 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Ontology

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Predicting Churn: How Multilayer Perceptron Method Can Help with Customer Retention in Telecom Industry

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IOP Conference Series: Materials Science and engineering 2020年第1期864卷

作者： NNA Sjarif NF Azmi HM Sarkan SM Sam MZ Osman Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia Soft Computing Research & Intelligent System Group (SPINT) Faculty of Systems Computer & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Pahang Malaysia

Customer churn prediction has been used widely in various kind of domain especially subscription-basis industries. With the rapid growth of telecommunication industry over the last decade, this industry not only focuses on providing numerous products, but also satisfying the customers as it is one of the key solutions to remain competitive. This research proposed MultiLayer Perceptron Method for churn prediction. The evaluation is compared with three classifiers which includes are Support Vector Machine, Naïve Bayes and Decision Tree in term of several aspects. In preprocessing phase, we employed Principal Component Analysis and normalization to find the correlation among all the variables. For the postprocessing, InfoGainAttribute is used to identify the highest factor attribute that leads to customer retention. It is found that MultiLayer Perceptron outperforms other classifiers and international plan plays important role to retain customer from leaving organization.

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