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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

Annals of Data science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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6G service-oriented space-air-ground integrated network: A survey

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Chinese Journal of Aeronautics 2022年第9期35卷 1-18页

作者： Nan CHENG Jingchao HE Zhisheng YIN Conghao ZHOU Huaqing WU Feng LYU Haibo ZHOU Xuemin SHEN School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Cyber Engineering Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada School of Computer Science and Engineering Central South UniversityChangsha 410083China School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China

As an indispensable component of the emerging 6G networks,Space-Air-Ground Inte-grated Networks(SAGINs)are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks,aerial networks,and terrestrial *** 6G SAGINs,a wide variety of network services with the features of diverse requirements,complex mobility,and multi-dimensional resources will pose great challenges to service provisioning,which urges the develop-ment of service-oriented *** this paper,we conduct a comprehensive review of 6G SAGINs from a new perspective of service-oriented ***,we present the requirements of service-oriented networks,and then propose a service-oriented SAGINs management *** categories of critical technologies are presented and discussed,i.e.,heterogeneous resource orchestration technologies and the cloud-edge synergy technologies,which facilitate the interoperability of different network segments and cooperatively orchestrate heterogeneous resources across different domains,according to the service features and *** addition,the potential future research directions are also presented and discussed.

关键词： Mobile Edge Computing(MEC) Network Function Virtual-ization(NFV) Network slicing Service-oriented network Software Defined Network-ing(SDN) Space-Air-Ground Inte-grated Networks(SAGINs)

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Online Examination and Evaluation System

Online Examination and Evaluation System

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2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Kumar Dandage, Harshad Uplaonkar, Deepak S. Shete, Ankita Shete, Avani Bodele, Lavanya Jadhav, Shruti Southern Taiwan University of Science and Technology Department of Computer Engineering Taiwan Department of Computer Engineering I2IT Pune India Department of It I2IT Pune India

ISBN: (纸本)9781665498975

Currently, online test systems have adapted easily to today's technologically advanced world. Examinations are an intrinsic part of the educational process. Even though the test are conducted online the teacher has to do manual evaluation. The examinations can be classified into two main types of evaluation, objective answer and subjective answer. As of now, online evaluation is available for the objective questions, hence the manual assessment of the theory answer, is a tedious task for the teacher. The teacher checks the answer manually and gives the marks. In this paper, the literature survey of existing solution is analyzed. © 2022 IEEE.

关键词： Online systems

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Robotic Perception of Transparent Objects: A Review

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2547-2567页

作者： Jiang, Jiaqi Cao, Guanqun Deng, Jiankang Do, Thanh-Toan Luo, Shan King's College London Department of Engineering LondonWC2R 2LS United Kingdom University of Liverpool SmARTLab Department of Computer Science LiverpoolL69 3BX United Kingdom Imperial College London LondonSW7 2AZ United Kingdom Monash University Department of Data Science and AI ClaytonVIC3800 Australia

Transparent object perception is a rapidly developing research problem in artificial intelligence. The ability to perceive transparent objects enables robots to achieve higher levels of autonomy, unlocking new applications in various industries, such as healthcare, services, and manufacturing. Despite numerous datasets and perception methods being proposed in recent years, there is still a lack of in-depth understanding of these methods and the challenges in this field. To address this gap, this article provides a comprehensive survey of the platforms and recent advances for robotic perception of transparent objects. We highlight the main challenges and propose future directions of various transparent object perception tasks, i.e., segmentation, reconstruction, and pose estimation. We also discuss the limitations of existing datasets in diversity and complexity, and the benefits of employing multimodal sensors, such as RGB-D cameras, thermal cameras, and polarized imaging, for transparent object perception. Furthermore, we identify perception challenges in complex and dynamic environments, as well as for objects with changeable geometries. Finally, we provide an interactive online platform to navigate each reference. © 2020 IEEE.

关键词： Cameras

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Outage Performance Analysis in Cognitive RIS-NOMA System under Imperfect CSI 3

Outage Performance Analysis in Cognitive RIS-NOMA System und...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Xue, K. Li, M. Chen, W. Han, Z. Yuan, H. Taiyuan University of Science and Technology School of Electronics Information Engineering China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing China University of Houston Department of Electrical and Computer Engineering United States Kyung Hee University Department of Computer Science and Engineering Korea Republic of Kingston University School of Computer Science and Mathematics KT1 2EE United Kingdom

ISBN: (纸本)9798350333732

Cognitive non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have been envisioned as two promising technologies for vehicle to everything (V2X) networks. This paper considers an RIS-aided V2X network with cognitive NOMA while considering the imperfect channel state information (CSI) due to the mobility of the vehicles. The new approximate outage probability (OP) expressions of the supposed system are derived over double Rayleigh fading channels. Numerical and Monte Carlo simulation results are presented to corroborate the analysis. The results demonstrated that imperfect CSI has a significant but different impact on the outage performance of the far and near users, which are also different under different numbers of reflecting units. © 2023 IEEE.

关键词： Monte Carlo methods

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Dynamic Analysis and Stability Enhancement of a Weak Grid-Tied Hybrid System Integrating PV, Full-Scale Wind Turbine, and Battery Storage

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2025年 6卷 325-349页

作者： Magableh, Mohammad Adnan K. Radwan, Amr Mohamed, Yasser Abdel-Rady I. Jordan University of Science and Technology Electrical Engineering Department Irbid22110 Jordan University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G2V4 Canada Western Washington University Engineering and Design Department BellinghamWA98225 United States

Hybrid generation systems integrating photovoltaics (PV), full-scale wind turbines (WT), and battery energy storage systems (BESS) have garnered substantial interest for utility-scale grid-tied applications due to their enhanced efficiency and reliability. However, in a hybrid system integrating these resources at the dc-side of a voltage-source converter, the dc-link stability remains largely unexplored in existing literature, particularly under weak grid conditions and across different operational regions of the PV, WT, and BESS. This article presents a comprehensive small-signal stability analysis of a hybrid PV-WT-BESS system using detailed state-space and incremental impedance models. The analysis reveals that the hybrid system exhibits low-frequency oscillation instability in weak grid conditions when the PV operates in the current-limited region. A novel active damping strategy is introduced to mitigate these instabilities, effectively suppressing negative interaction dynamics and improving overall system stability;this is achieved by repositioning unstable eigenmodes and reshaping the dc-link dynamics to comply with the Nyquist stability criterion. The proposed compensation technique offers several key advantages: 1) it is simple yet effective and can be designed using linear analysis methods;2) it ensures stable operation under varying grid conditions without altering steady-state performance;3) it enhances the low-voltage ride-through capability;and 4) it eliminates the need for additional measurement sensors, thereby simplifying the implementation and reducing costs compared to alternative stabilization methods. Offline and real-time software-in-the-loop simulations validate the accuracy of the developed models and dynamic interactions, as well as the effectiveness of the proposed active stabilization approach across typical operating conditions. © 2020 IEEE.

关键词： Battery storage

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Intelligent FaultEdge: AI-Driven Fault-Tolerant Edge Framework for Smart Grid Monitoring in IoT 12

Intelligent FaultEdge: AI-Driven Fault-Tolerant Edge Framewo...

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12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Singh, Tushar Kumar, Sudhakar Singh, Sunil K. Chilkoti, Vanshika Bansal, Shavi Chui, Kwok Tai Gupta, Brij B. Chandigarh College of Engineering & Technology Department of Cse Chandigarh India Insights2Techinfo United States Hong Kong Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan

ISBN: (纸本)9798350350982

Smart grid monitoring in IoT environments demands robust fault tolerance mechanisms to ensure uninterrupted operation and data accuracy. The integration of advanced machine learning with fault-tolerant strategies in the proposed Intelligent FaultEdge framework represents a significant innovation. Unlike traditional reactive systems, Intelligent FaultEdge adopts a proactive approach, leveraging predictive analytics, anomaly detection, and adaptive reconfiguration to anticipate and manage potential faults in real-time. This proactive framework not only enhances the reliability and resilience of smart grid operations but also minimizes downtime, improves safety, and optimizes energy efficiency. By continuously monitoring grid conditions and dynamically adjusting to evolving scenarios, Intelligent FaultEdge supports sustainable and secure smart grid management. This paper presents a comprehensive framework aligned with the goals of modern intelligent systems, offering an efficient solution for enhancing the reliability and performance of IoT-enabled smart grids. Through its proactive fault management capabilities, Intelligent FaultEdge contributes to advancing smart infrastructure, ensuring dependable energy supply, and supporting the evolution towards more resilient and efficient smart grids. © 2024 IEEE.

关键词： Predictive analytics

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Long range electromagnetic field nature of nerve signal propagation in myelinated axons

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Chinese Physics B 2022年第3期31卷 619-623页

作者： Qing-Wei Zhai Kelvin J A Ooi Sheng-Yong Xu C K Ong School of Electrical and Computer Engineering Xiamen University MalaysiaSelangor Darul Ehsan 43900Malaysia Department of Physics Xiamen University MalaysiaSelangor Darul Ehsan 43900Malaysia Department of Electronics School of Electronics Engineering and Computer SciencePeking UniversiyBeijing 100871China Department of Physics National University of Singapore2 Science Drive 3117551 Singapore

The nature of saltatory conduction in myelinated axon described by equivalent circuit and circuit theory is still contentious. Recent experimental observations of action potentials transmitting through disjointed nerve fibers strongly suggest an electromagnetic wave propagation mechanism of the nerve signals. In this paper, we employ the electromagnetic wave model of the myelinated axon to describe action potential signal propagation. We use the experimental frequency-dependent conductivity and permittivity values of the nerve tissues in order to reliably calculate the electromagnetic modes by using electromagnetic mode solvers. We find that the electromagnetic waves above 10 kHz can be well confined in extracellular fluid–myelin sheath–intracellular fluid waveguide and propagate a distance of 7 mm without much attenuation. Our study may serve as one of the fundamental researches for the better understanding of the nervous system.

关键词： action potential propagation and axons neuronal wave propagation neuroscience electrodynamics in the nervous system

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A 2-Colorable DODAG Structured Hybrid Mode of Operations Architecture for RPL Protocol to Reduce Communication Overhead 6th

A 2-Colorable DODAG Structured Hybrid Mode of Operations Arc...

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6th IFIP International Conference on Internet of Things, IFIP IoT 2023

作者： Mishra, Alekha Kumar Khatik, Sadhvi Puthal, Deepak Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jamshedpur India C2PS Department of EECS Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031458811

The Routing Protocol for low-power and lossy Networks (RPL) is the established standard for packet-level communications in the Internet of Things (IoT). The efficiency of point-to-point (P2P) communications in RPL relies on the mode of operation employed by the network nodes. Hybrid modes of operation (MOP) have gained attention as they combine the advantages of both standard non-storing and storing operation modes. However, existing hybrid MOPs have struggled to achieve a balance between reduced communication overhead and storage overhead. To address this, we propose the application of the 2-colorable graph property to enable a hybrid mode of operation among IoT network nodes. By mapping the two-colorable property to the standard MOPs, we conduct experiments comparing the proposed hybrid MOP with existing approaches. Results demonstrate that our proposed hybrid MOP achieves a significant balance between storing and non-storing nodes, while also exhibiting improved average path length compared to existing hybrid MOPs. This research contributes to optimizing P2P communications in RPL-based IoT networks and highlights the potential benefits of utilizing the 2-colorable graph property in achieving an efficient hybrid mode of operation. © 2024, IFIP International Federation for Information Processing.

关键词： Internet of things

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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

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Energy Storage and Saving 2024年第4期3卷 243-249页

作者： Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada Department of Chemical Engineering University of Mohaghegh ArdabiliArdabil 13131-56199Iran Department of Chemistry School of ScienceAlzahra UniversityTehran ***Iran Computer Engineering Gachsaran Oil and Gas Exploitation CompanyGachsaran ***Iran Architectural Engineering Shiraz Islamic Azad UniversityShiraz ***Iran

A Trombe wall-heating system is used to absorb solar energy to heat *** parameters affect the system performance for optimal *** study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall *** accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)*** results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and *** contrast,linear regression and decision tree showed weaker *** findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关键词： Trombe wall Solar energy Thermal storage wall Machine learning algorithms

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