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Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

computers, Materials & Continua 2021年第11期69卷 2013-2029页

作者： C.Rajinikanth P.Selvaraj Mohamed Yacin Sikkandar T.Jayasankar Seifedine Kadry Yunyoung Nam Department of Electronics and Communications Engineering Erode Sengunthar Engineering College(Autonomous)Erode638057India Department of Information Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Electrical Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Faculty of Applied Computing and Technology(FACT) Noroff University CollegeKristiansand4608Norway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded *** e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and *** research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of *** this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT *** presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic *** EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data *** addition,the IoT devices forward the data to the cloud where the actual classification procedure is *** classification process,variational autoencoder(VAE)is used to determine the existence of disease or *** order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes *** obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.

关键词： Energy efficiency intelligent models decision support system IoT e-healthcare machine learning

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Energy grid management system with anomaly detection and Q-learning decision modules

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computers and Electrical Engineering 2023年 107卷

作者： Syu, Jia-Hao Srivastava, Gautam Fojcik, Marcin Cupek, Rafal Lin, Jerry Chun-Wei Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Mathematics and Computer Science Brandon University Brandon Canada Research Center for Interneural Computing China Medical University Taichung Taiwan Department of Computer Science and Math Lebanese American University Beirut Lebanon Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Stability and security issues in energy management have become widespread research topics, in which artificial intelligence techniques are often embedded in management systems to efficiently manage the smart grid. In this paper, we propose an energy grid management system with anomaly detection and Q-learning decision modules (EMSAD). The anomaly detection module is a multitask learning network that simultaneously classifies suppliers and predicts actual supply quantities. The Q-learning decision module then determines the operating reserve and subsidies to manage the energy grid. Experimental results show that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers with F1-scores from 73.3% to 100.0%. The robustness evaluation also shows that EMSAD maintains high performance even in unseen environments without fine-tuning. Thus, the simulation results demonstrate the security, efficiency, transferability, and robustness of the proposed EMSAD in smart grid energy management. © 2023 Elsevier Ltd

关键词： Energy management

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt19385 Jordan Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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Kink movement on a periodic background

arXiv

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arXiv 2024年

作者： Dobrowolski, Tomasz Gatlik, Jacek Kevrekidis, Panayotis G. Department of Computer Physics and Quantum Computing University of the National Education Commission in Krakow Podchoŗ azych 2 Cracow30-084 Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow30-059 Poland Department of Mathematics and Statistics University of Massachusetts AmherstMA01003-4515 United States

The behavior of the kink in the sine-Gordon (sG) model in the presence of periodic inhomogeneity is studied. An ansatz is proposed that allows for the construction of a reliable effective model with two degrees of freedom. Effective models featuring very good agreement with the original field-theoretic partial differential equation are constructed, including in the non-perturbative region and for relativistic velocities. The numerical solutions of the sG model describing the evolution of the kink in the presence of a barrier as well as in the case of a periodic heterogeneity under the potential additional influence of a switched bias current and/or dissipation were obtained. The results of the field equation and the effective models were favorably compared. The effect of the choice of initial conditions in the field model on the agreement of the results with the effective model is also discussed. © 2024, CC BY.

关键词： sine-Gordon equation

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Machine Learning For An Explainable Cost Prediction of Medical Insurance

arXiv

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arXiv 2023年

作者： Orji, Ugochukwu Ukwandu, Elochukwu Department of Computer Science Faculty of Physical Science University of Nigeria Enugu State Nsukka Nigeria Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Wales CardiffCF5 2YB United Kingdom

Predictive modeling in healthcare continues to be an active actuarial research topic as more insurance companies aim to maximize the potential of Machine Learning (ML) approaches to increase their productivity and efficiency. In this paper, the authors deployed three regression-based ensemble ML models that combine variations of decision trees through Extreme Gradient Boosting (XGBoost), Gradient-boosting Machine (GBM), and Random Forest (RF) methods in predicting medical insurance costs. Explainable Artificial Intelligence (XAi) methods SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots were deployed to discover and explain the key determinant factors that influence medical insurance premium prices in the dataset. The dataset used comprised 986 records and is publicly available in the KAGGLE repository. The models were evaluated using four performance evaluation metrics, including R-squared (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that all models produced impressive outcomes;however, the XGBoost model achieved a better overall performance although it also expanded more computational resources, while the RF model recorded a lesser prediction error and consumed far fewer computing resources than the XGBoost model. Furthermore, we compared the outcome of both XAi methods in identifying the key determinant features that influenced the PremiumPrices for each model and whereas both XAi methods produced similar outcomes, we found that the ICE plots showed in more detail the interactions between each variable than the SHAP analysis which seemed to be more high-level. It is the aim of the authors that the contributions of this study will help policymakers, insurers, and potential medical insurance buyers in their decision-making process for selecting the right policies that meet their specific needs. © 2023, CC BY-NC-SA.

关键词： Machine learning

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I Can Think Like You! Towards Reaction Spoofing Attack on Brainwave-Based Authentication 1

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13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

作者： Chiu, Wei-Yang Meng, Weizhi Li, Wenjuan Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Department of Computing Hong Kong Polytechnic University Kowloon China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China

ISBN: (数字)9783030688516

ISBN: (纸本)9783030688509

In the coming period of Internet of Things (IoT), user authentication is one important and essential security mechanism to protect assets from unauthorized access. Textual passwords are the most widely adopted authentication method, but have well-known limitations in the aspects of both security and usability. As an alternative, biometric authentication has attracted much attention, which can verify users based on their biometric features. With the fast development of EEG (electro-encephalography) sensors in current headsets and personal devices, user authentication based on brainwaves becomes feasible. Due to its potential adoption, there is an increasing need to secure such emerging authentication method. In this work, we focus on a brainwave-based computer-screen unlock mechanism, which can validate users based on their brainwave signals when seeing different images. Then, we analyze the security of such brainwave-based scheme and identify a kind of reaction spoofing attack where an attacker can try to imitate the mental reaction (either familiar or unfamiliar) of a legitimate user. In the user study, we show the feasibility and viability of such attack. © 2021, Springer Nature Switzerland AG.

关键词： Authentication

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Open-Ended Coaxial Probe Based Chloride Concentration Prediction in Water Using Machine Learning Algorithms

Open-Ended Coaxial Probe Based Chloride Concentration Predic...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Hanima Kannan C H Veena Venugopal Deepa Gupta Parul Mathur Dhanesh G Kurup Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Physical Science Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India School of Engineering and Applied Science Benette University Greater Noida UP India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Monitoring chloride levels in water is essential for environmental health, industrial processes, and safe drinking water, as both high and low concentrations can cause harm for living being. This paper presents a novel approach to detecting chloride concentrations using Open-Ended Coaxial Probe (OECP) sensing combined with machine learning algorithms. OECP sensing offers real-time, non-destructive, and precise monitoring, enabling early detection of contaminants. Scattering parameter ( $S$ 11) data across a broad frequency range was collected with varying chloride concentrations in controlled laboratory experiments. Thirteen machine learning algorithms were trained to predict chloride concentrations, with different datasets for training and testing to ensure robustness. Among these, CatBoost demonstrated the best performance. After fine tuning and cross-validation, the CatBoost model achieved a test Mean Square Error (MSE) of 0.00627 and Coefficient of Determination (R2) of 0.9936, showcasing its accuracy for applications in water monitoring, corrosion prevention, and safeguarding public health.

关键词： Machine learning algorithms Water quality Predictive models Real-time systems Sensors Scattering parameters Probes Public healthcare Tuning Testing

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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Electrical and computer Engineering Researches (ICECER), International Conference on

作者： Taher M. Ghazal Jamshaid Iqbal Janjua Walid Abushiba Munir Ahmad Tahir Abbas Karamath Ateeq College of Arts & Science Applied Science University Manama Kingdom of Bahrain Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Al-Khawarizimi Institute of Computer Science (KICS) University of Engineering & Technology (UET) Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul Republic of Korea Department of Computer Science TIMES Institute Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah UAE

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems.

关键词： Renewable energy sources Accuracy Load forecasting Machine learning Predictive models Power system stability Data models Forecasting Sustainable development Load modeling

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Improving Optimization Prowess of Ant Colony Algorithm Using Bat Inspired Algorithm

Improving Optimization Prowess of Ant Colony Algorithm Using...

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Information Technology for Education and Development (ITED)

作者： Hakeem Babalola Akande Oluwakemi Christiana Abikoye Oluwatobi Noah Akande Rasheed Gbenga Jimoh Department of Telecommunication Science University of Ilorin Ilorin Kwara State Nigeria Department of Computer Science University of florin Ilorin Kwara State Nigeria Computer Sciene Department Faculty of Computing and Applied Sciences Baze University Abuja Nigeria

Metaheuristic algorithms such as Ant Colony Optimization (ACO) algorithm and Bat Optimization Algorithm (BOA) have been widely employed in solving different optimization problems in several fields. ACO is modelled based on the social behaviour of ants that look for appropriate answers to a given optimization issue by recasting it as the case of locating the least expensive path on a weighted graph. A set of parameters linked to graph components (either nodes or edges) whose values are changed by the ants during runtime constitute the pheromone model, which biases the stochastic solution generation process. However, the effectiveness of ACO declines as the quantity of packets rises, making them ineffective for reducing traffic congestion. As more packets are transmitted, their strength decreases, causing packet congestion, rendering them useless for reducing packet traffic congestion. On the contrary, BOA which was modelled after the behavior of bats has also been employed in fixing network routing issues by listening to every sound in a space and taking note of what is going on around it. In order to further improve ACO algorithm and decrease packet traffic congestion, packet loss, and the time it takes a packet to reach its destination in a network system, this study employs the strength of BOA. Results obtained revealed the prowess of BOA in improving the performance of ACO for network packet routing.

关键词： Runtime Metaheuristics Stochastic processes Packet loss Routing Throughput Rendering (computer graphics)

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Dynamic Routing and Post-Processing Strategies for Hybrid Quantum Key Distribution Networks

Dynamic Routing and Post-Processing Strategies for Hybrid Qu...

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International Conference on Distributed computing Systems

作者： Omar Amer Walter O. Krawec Md. Zakir Hossain Victoria U. Manfredi Bing Wang Global Technology Applied Research JPMorgan Chase Bank New York City NY United States School of Computing University of Connecticut Storrs CT United States Department of Mathematics and Computer Science Wesleyan University Middletown CT United States

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

In this paper, we consider hybrid quantum key distribution (QKD) networks with primarily quantum repeaters and a small number of trusted nodes. While the trusted nodes need to be trusted, the use of such nodes, together with efficient routing algorithms, can significantly improve the key generation rate. We show that when trusted nodes are placed at asymmetric locations relative to Alice and Bob, however, existing routing algorithms can lead to low key rate. To address this issue, we develop dynamic routing strategies that adjust routing decisions based on the current key pool conditions in the network. In addition, we investigate new and existing classical post-processing techniques that complement the dynamic routing strategies. Using extensive simulations, we show that our dynamic routing strategies can significantly outperform static strategies, and the post-processing techniques are beneficial in high noise scenarios. In addition, combining the dynamic routing strategies and post-processing techniques can further improve the overall key rate.

关键词： Heuristic algorithms Noise Routing Quantum key distribution Distributed computing Quantum repeaters

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