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International Conference on computer and Applications (ICCA)

作者： Wijesinghe W.M.A. J Abeysinghe Y.P. P Rukshana M.A. F Adhikari A.M.P. S Kavinga Yapa Abeywardhana Deemantha Siriwardhana Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Employers should recognize that employees are the most vulnerable aspect of business environments since these cyber hazards are growing because of user neglect, lack of fundamental security discipline, and a fast-changing threat context. The theoretical approach proposed in the study bridges the difference by carefully following and handling the staff's internet presence. Overall, the study describes a comprehensive solution to govern the security of employees' internet presence by utilizing Artificial Neural Networks (ANN) for advanced intrusion detection, Natural Language Processing (NLP) for implementation and enforcement of browser-based security policies, visual and text data recognition to identify phishing attempts, and lastly analyzing and profiling users based on their online presence via a mathematical model. As a result, this study provides a comprehensive solution for mitigating risks related to human factors in corporate environments.

关键词： Visualization Text recognition computer hacking Phishing Intrusion detection Artificial neural networks Human factors Natural language processing Mathematical models Hazards

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Intelligent optimisation in smart and sustainable compressed air systems: Towards support for decision-making under faulty conditions

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Procedia CIRP 2024年 122卷 635-640页

作者： Jasmine Mallia Emmanuel Francalanza Peter Xuereb Daniel Baldacchino Paul Refalo Department of Industrial and Manufacturing Engineering Faculty of Engineering University of Malta Msida MSD2080 Malta Department of Computer Information Systems Faculty of Information & Communication Technology University of Malta Msida MSD2080 Malta

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs.

关键词： pneumatic systems automation control optimisation sustainability

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Self-Optimizing Memory Controllers: Proposing Request-level Scheduling 2

Self-Optimizing Memory Controllers: Proposing Request-level ...

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2nd International Conference on computer Science, engineering and Applications, ICCSEA 2022

作者： Shawky, Alaa Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Faculty of Engineering Electronics and Communications Dept. Cairo Egypt Siemens Digital Industries Software Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Dept. Cairo Egypt

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Self-Optimizing Memory Controllers present a great potential in the future of memory controllers. As they alleviate the burden of designing an optimal memory scheduling policy, while providing adaptability to different workloads and systems. Previous state-of-art controllers scheduled memory accesses on the command level. This causes the agent to lose sight of the relation between commands and can lead to the issuing of unuseful sequences of precharge/activate of rows or closing banks that should have been left open, and vice versa. In this work we propose a bank-aware self-optimizing memory controller that schedules memory references on the request-level, presenting to the agent the actual problem it needs to tackle. This simplifies the evaluation of system attributes and eliminates the need for correction mechanisms that was needed in previous controllers. With the proposed algorithm a maximum of 18% increase in the data bus utilization is achieved compared to the commonly used FR-FCFS algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Enhancing Agricultural Efficiency with IoT-Enabled Precision Irrigation Management Using LoRaWAN Technology

Enhancing Agricultural Efficiency with IoT-Enabled Precision...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： J.A.S.K. Jayasinghe T.M.H.L. Thennakoon U.G.C. Ruchira N.G.C.L. Wimalasena J.A.D.C.A. Jayakody Dinithi Pandithage Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

The Internet of Things-empowered precision irrigation management system with LoRaWAN technology is presented given the growing food requirements across the world and pressing calls for judicious use of water in agriculture. Flow Pilot, specifically designed for chili farms, optimizes water application while enhancing crop health using precise and automated irrigation. The Flow Pilot features advanced sensors such as soil moisture, temperature, humidity, and rain sensors that collect real-time environmental data transmitted over a LoRaWAN network for analysis. Flow Pilot uses the Machine learning algorithm by analyzing sensor data to make localized irrigation recommendations based on crop needs, environmental factors, and weather forecasting. LoRaWAN gateways will support this for long-ranged communications over power-consuming agricultural fields. Besides this, it can use different network architectures. There is also an automated water tank management module that regulates the supply of water and pH levels to have optimal conditions for crop growth. A visually simple dashboard interface enables farmers to inspect and operate irrigation processes remotely, hence rendering the whole process far more efficient. Finally, over the extensive period of field tests executed by the project, Flow Pilot was able to save water, improve crop yields, and help support sustainable agriculture. For example, this testing supports the potential of IoT with LoRaWAN technologies, such as the Flow Pilot implementation, for a game change in conventional irrigation practices into a modern, scalable, and reliable solution.

关键词： Temperature sensors Water Irrigation Storage management LoRaWAN Machine learning Nutrients Real-time systems Sensor systems Crop yield

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Cyber Security Posterior Distribution 13

Cyber Security Posterior Distribution

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13th IEEE International Conference on Communication systems and Network Technologies, CSNT 2024

作者： Andrews, Leo John Baptist Venkatachary, Sampath Kumar Sarathkumar, D. Alagappan, Annamalai Fathima, M. Raj, Raymon Antony Rajeshkanna, R. Botho University Faculty of Engineering and Technology Department of Information Technology Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Erode district Tamilnadu 638 060 India Grant Thornton Gaborone Botswana Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Thamirabarani Engineering College Department of Eee Tamil Nadu Tirunelveli627 358 India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350305463

Currently, the vast majority of international interactions and activities related to trade, government, culture, social interaction, and economics take place online. These interactions involve people, non-governmental organizations, governments, and government agencies at all levels. Recently, a sizeable number of commercial firms and government organizations all over the world have been presented with the challenge offered by cyberattacks as well as the danger represented by wireless communication technologies. A very little amount of time has passed since this had place. We were able to develop a model for the risk of cyber security by making use of Bayesian Networks. This model has the capability of locating the point of cyber security that is weak and merging two different cyber analysis models. Among these is the activityquality model, which shows how individuals and/or groups adhere to the legal requirements for cyber security. Evidence describing the critical component of cyber security for a research reactor's RPS may be produced via the cyber security risk model. An further model that shows the probability of a cyberattack against RPS architecture is the cyber security risk model. © 2024 IEEE.

关键词： Bayesian networks

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An Augmented Reality Application for Upper-Limb Rehabilitation Supported by a Robotic Arm 6

An Augmented Reality Application for Upper-Limb Rehabilitati...

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6th Experiment at International Conference, exp.at 2023

作者： Rodrigues, Jose Carlos Restivo, Maria Teresa Menezes, Paulo University of Porto LAETA-INEGI Faculty of Engineering Porto Portugal University of Porto LAETA-INEGI Porto Portugal A3ES Lisbon Portugal University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350314403

This work looks at the potential use of a robotic arm and augmented reality application, with a head-mounted display, in an upper-limb rehabilitation context. A sample application using a robotic simulator and a cross-sectional study using a real robotic arm are shown. Results from the usability evaluation are positive and encourage further development, but some identified problems must be corrected before further evaluations. © 2023 IEEE.

关键词： Robotic arms

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On the performance of reconfigurable intelligent surface aided power line communication system under different relay transmission protocols

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Progress In Electromagnetics Research C 2021年 111卷 119-133页

作者： Odeyemi, Kehinde O. Owolawi, Pius A. Olakanmi, Oladayo O. Department of Computer Systems Engineering Tshwane University of Technology Pretoria0001 South Africa Department of Electrical and Electronic Engineering Faculty of Technology University of Ibadan Nigeria

In this paper, the performance analysis of a dual-hop reconfigurable intelligent surface (RIS)-aided power line communication (PLC) system is presented under different relay transmission protocols. The relay is assumed to be decode-and-forward (DF) or amplify-and-forward (AF) relaying protocol. It is also assumed that the RIS link is subjected to Rayleigh fading while the PLC link undergoes Log-normal fading with the influence of additive background and impulsive noise. To evaluate the system performance, the end-to-end cumulative distribution function for both relaying protocols are derived. Based on these, the analysis expressions for the system outage probability and average bit error rate (ABER) are derived under DF and AF relaying protocols. To gain further insight about the system performance, the asymptotic analysis for the derived expressions is obtained at high signal-to-noise ratio regime. The findings illustrate the significant impact of the number of RIS elements and impulsive noise on the overall system performance. In addition, the accuracy of the analytical results is justified through Monte-Carlo simulations. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Rayleigh fading

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Satellite-Derived NO2 Data to Predict Hospital Admissions: A Machine Learning-Based Approach

Satellite-Derived NO2 Data to Predict Hospital Admissions: A...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Bhushan Pawar Lalit Garg Vijay Prakash Ryan Stevenson Matthew Amaira Daria Soloducha Adam Kraus Computer Information Systems Faculty of Information & Communication Technology University of Malta Msida Malta Communications & Computer Engineering Faculty of Information & Communication Technology University of Malta Msida Malta

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Rising Levels of air pollution and changing environmental conditions are the global concern. There is substantial evidence linking short-term and long-term exposure to NO 2 (nitrogen dioxide) with multiple disorders such as asthma, Bronchitis, Chronic Obstructive Pulmonary Disease (COPD), and Respiratory Infections in humans. However, multiple researchers have mainly depended on time-based exposure data, possibly overlooking fluctuations in NO 2 levels across different locations. We aim to analyze the relationship between NO 2 fluctuations and emergency department hospital admissions. We adopted machine learning (ML) based regression approaches to predict hospital admissions, we obtained emergency department hospital admissions from 2018–2019, while NO 2 tropospheric concentration was collected from the Sentinel 5P satellite. The results are evaluated using various evaluation matrices such as MSE (Mean Square Error), RMSE (Root Mean Square Error), R2 score, etc. Our study demonstrated that Random Forest and ExtraTreesRegressor models gained substantial predictive accuracy compared to other applied regression models. ExtraTreesRegressor proves its significance with R2 score of 67.11. These results deliver significant insights for hospitals and healthcare authorities by offering resource management through anticipating fluctuating NO 2 concentrations. Moreover, the findings of our study highlight the potential of extending this research to include several air pollutants and meteorological factors.

关键词： Technological innovation Hospitals Atmospheric modeling Predictive models Air pollution Resource management Nitrogen Forecasting Regression tree analysis Random forests

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Automated Artificial Intelligence Empowered White Blood Cells Classification Model

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computers, Materials & Continua 2023年第4期75卷 409-425页

作者： Mohammad Yamin Abdullah M.Basahel Mona Abusurrah Sulafah M Basahel Sachi Nandan Mohanty E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)VisakhapatnamAndhra Pradesh530049India

White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. In addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.

关键词： White blood cells cell engineering computational intelligence image classification transfer learning

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Study on Prediction of Melt Pool Shape in SLM Method for Maraging Steel 10

Study on Prediction of Melt Pool Shape in SLM Method for Mar...

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10th International Conference on Leading Edge Manufacturing Technologies in 21st Century, LEM 2021

作者： Fukunaga, Taiichiro Narahara, Hiroyuki Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

The Selective Laser Melting (SLM) of the powder bed fusion is a method of laminating metal powder that are selectively melted and solidified by laser beam to build a three-dimensional object. In recent years, its use has expanded in various fields such as the automotive industry, medical industry, and aerospace industry. In this study, we propose a molten pool formation model that uses only a small number of parameters that dominate melt pool shape formation. The formula was derived and compared with the experimental values. © 2021 The Japan Society of Mechanical Engineers.

关键词： Laser beams

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