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arXiv 2024年

作者： Vladucu, Maria-Victoria Wu, Hailun Medina, Jorge Salehin, Khondaker M. Dong, Ziqian Rojas-Cessa, Roberto The Network and Innovation Laboratory Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States Department of Computer Science University of Portland PortlandOR97203 United States The Networking and Intelligent Systems Laboratory Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Real and effective regulation of contributions to greenhouse gas emissions and pollutants requires unbiased and truthful monitoring. Blockchain has emerged not only as an approach that provides verifiable economical interactions but also as a mechanism to keep the measurement, monitoring, incentivation of environmental conservationist practices and enforcement of policy. Here, we present a survey of areas in what blockchain has been considered as a response to concerns on keeping an accurate recording of environmental practices to monitor levels of pollution and management of environmental practices. We classify the applications of blockchain into different segments of concerns, such as greenhouse gas emissions, solid waste, water, plastics, food waste, and circular economy, and show the objectives for the addressed concerns. We also classify the different blockchains and the explored and designed properties as identified for the proposed solutions. At the end, we provide a discussion about the niches and challenges that remain for future research. © 2024, CC BY-NC-ND.

关键词： Circular economy

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Ahmed Tibermacine Djouher Akrour Rofeida Khamar Imad Eddine Tibermacine Abdelaziz Rabehi Department of Computer Science University of Biskra Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Rome Italy Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Zian Achour University of Djelfa Djelfa Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants’ favorite music, and Interstellar’s main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli.

关键词： Support vector machines Accuracy Recurrent neural networks Pattern classification Brain modeling Feature extraction Electroencephalography Telecommunications Convolutional neural networks Object recognition

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2021 Monthly Rice Production in Chinese Coastal Provinces 3

2021 Monthly Rice Production in Chinese Coastal Provinces

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Parmar, Ajeet Munasinghe, Thilanka Tubbs, Heidi Anyamba, Assaf Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering Troy United States Rensselaer Polytechnic Institute Department of Information Technology and Web Science Troy United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak Ridge United States

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations. © 2022 IEEE.

关键词： Regression analysis

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Smooth Quadrature-Inspired Generalized Choquet Integral in an Application to Anomaly Detection

Smooth Quadrature-Inspired Generalized Choquet Integral in a...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE)

作者： Paweł Karczmarek Michał Dolecki Łukasz Gałka Witold Pedrycz Dariusz Czerwinski Department of Computer Science Lublin University of Technology Lublin Poland Department of Electrical & Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia Polish Academy of Sciences Systems Research Institute Warsaw Poland

In this study, we consider a new approach to the enhancement of classic Choquet integral as a vehicle in the processes of aggregation of classifiers o r i nformation fusion. The improvement of classification result o n a b asis o f classifier ensambles is one of the most important tasks of machine learning research community. In the previous series of works, we have introduced a conception of building Choquet-like aggregation operator using the idea inspired by one of the most common numerical methods, namely quadratures. Here, we extend this technique by using the concept which we call smoothing. We use this term to express the idea of smoothing the function under the integral symbol, and thus triggering processes that increase the elasticity of the Choquet integral. In a series of numerical experiments with anomaly detection problem, we show that the new approach is better than the existing ones in terms of accuracy and f1 score.

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A comprehensive review and conceptual framework for cloud computing adoption in bioinformatics

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Healthcare Analytics 2023年 3卷 100190-100190页

作者： Banimfreg, Bayan H. Electrical Engineering and Computer Science Department Center for Cyber-Physical Systems - Khalifa University United Arab Emirates

The healthcare industry generates enormous amounts of data, particularly in genomics, which require effective handling, storage, and analysis. Cloud computing offers a promising solution to these challenges. This paper focuses on the adoption of cloud computing in bioinformatics and evaluates current applications in the field. The article also proposes a comprehensive conceptual framework for cloud computing adoption in bioinformatics, which can serve as a valuable reference for practitioners, academics, and organizations transitioning to cloud-based bioinformatics services. While cloud computing and bioinformatics can provide significant benefits and new opportunities in healthcare, data privacy, security, and management concerns are significant challenges that must be addressed. Organizations must implement robust security measures to protect sensitive patient information and ensure regulatory compliance. Additionally, proper data management practices and training for healthcare professionals are necessary to maximize the potential of cloud computing in bioinformatics. Ultimately, the adoption of cloud computing in bioinformatics will pave the way for more personalized and effective healthcare, benefiting patients and healthcare professionals alike. © 2023 The Author(s)

关键词： Big data Bioinformatics Cloud computing Comprehensive review Conceptual framework Healthcare

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Enhancing Sustainable Agriculture Through Smart Architecture: An Adaptive Neuro-Fuzzy Inference System with XGBoost Model

Enhancing Sustainable Agriculture Through Smart Architecture...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Prashantha Kumar Kemmannu RVS Praveen Saravanan B Amshavalli M Arun K. K V. Banupriya Department of Civil Engineering Nitte (Deemed to Be University) NMAM Institute of Technology (NMAMIT) Karkala Karnataka India Department of Digital Engineering & Assurance Hyderabad Telangana India Department of CSE Sri Eshwar College of Engineering Kondampatti Kinathukadavu Coimbatore Tamilnadu India Department of Computer Science and Engineering Erode Sengunthar Engineering College Tamilnadu India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur Tamilnadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Precise agricultural production forecasting is difficult due to the deviations in environmental, soil, and crop conditions, as well as the complex interactions of affecting factors. Current methodologies, such Adaptive Neuro-Fuzzy Inference systems (ANFIS) and Artificial Neural Networks (ANNs), have enhanced prediction efficacy but encounter obstacles such as substantial processing requirements, data reliance, and restricted generalisability. This study examines problems such as the necessity for enhanced model interpretability, scalability, and adaptation to varied agricultural situations. We present a hybrid ANFIS-based methodology optimised by the incorporation of sophisticated algorithms. The suggested system attained exceptional performance, evidenced by a coefficient of determination (R2) of 97.9%, a Root Mean Square Error (RMSE) of 0.017, and a Mean Absolute Percentage Error (MAPE) of 0.003, indicating its high precision and dependability. These indicators emphasise the system's capacity for accurate yield forecasting and effective decision-making in agricultural management.

关键词： Smart agriculture Adaptation models Adaptive systems Accuracy Scalability Crops Soil Forecasting Root mean square Diseases

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A preliminary study about gender gap perception in informatics studies in Spain 12

A preliminary study about gender gap perception in informati...

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12th International Conference on Virtual Campus, JICV 2022

作者： Garcia-Holgado, Alicia Gonzalez-Gonzalez, Carina S. Fernandez, Inmaculada Garcia Lopez, Lourdes Moreno Barrenechea, Edurne Rueda, Silvia Navarro, Angeles G. Martinez, Paloma Masia, Belen Navarro, Elena Fornes, Alicia Grial Research Group Universidad DeSalamanca Computer Science and Automation Department Salamanca Spain University of la Laguna Computer Engineering Department San Cristobal de La Laguna Spain Universidad de Málaga Computer Architecture Department Málaga Spain Universidad Carlos Iii de Madrid Computer Science and Engineering Department Madrid Spain Universidad Pública de Navarra Departamento de Estadística Informática y Matemáticas Pamplona Spain Universitat de València Departament of Computer Science Valencia Spain Universidad de Zaragoza Computer Science Department Zaragoza Spain Universidad de Castilla la Mancha Departament of Computing Systems Albacete Spain Computer Vision Center Universitat Autónoma de Barcelona Barcelona Spain

ISBN: (数字)9781665451062

ISBN: (纸本)9781665451062

The gender gap in STEM is an issue that affects regions and countries worldwide. Furthermore, the percentage of women in these areas depends on a range of different factors. In particular, the gender gap is critical in technology in general and, more specifically, in informatics. This problem affects not only the business sector but also society. Informatics is part of our lives;however, 50% of the world population is not represented in the teams that are developing solutions for solving society's problems. In Spain, the number of women with informatics degrees is around 15%. This work describes a case study developed by the committee of Women in Informatics at the Scientific Society of Spanish Informatics (SCIE) to analyse the perception of informatics scientists on the gender gap in informatics. © 2022 IEEE.

关键词： computers

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Deduplication of Textual Data by NLP Approaches

Deduplication of Textual Data by NLP Approaches

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IEEE Conference on Vehicular Technology (VTC)

作者： Kiana Ghassabi Peyman Pahlevani Daniel E. Lucani Department of Computer Science Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

With the increasing amount of digital data, data deduplication has become an increasingly popular method for reducing data in large-scale storage systems. Generalized deduplication is an alternative technique for reducing the cost of data storage by identifying similar data chunks. This paper proposes TL-GD, a method for improving cloud storage efficiency using generalized deduplication focusing on textual datasets. The core concept of this study is to develop an efficient deduplication system that combines an alternative technique for splitting data into smaller pieces and a new approach for transforming data pieces into bases and deviations. The performance of the system has been validated using two real-world datasets. We also compare the results to state-of-the-art deduplication methods. Our evaluation results show that TL-GD achieves nearly 67% lossless compression for textual navigation instructions datasets, which is a 25% improvement on average compared to existing deduplication techniques.

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MF-MOS: A Motion-Focused Model for Moving Object Segmentation

MF-MOS: A Motion-Focused Model for Moving Object Segmentatio...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jintao Cheng Kang Zeng Zhuoxu Huang Xiaoyu Tang Jin Wu Chengxi Zhang Xieyuanli Chen Rui Fan School of Electronic and Information Engineering South China Normal University Foshan China Department of Computer Science Aberystwyth University Aberystwyth U.K. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China School of Internet of Things Engineering Jiangnan University Wuxi China College of Intelligence Science and Technology National University of Defense Technology Changsha China College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://***/SCNU-RISLAB/MF-MOS.

关键词： Laser radar Semantics Dynamics Object segmentation Data augmentation Data models Reliability

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Signal Attenuation Modeling and Simulation for 5G and Beyond P2P Wireless Communication Networks in FR2 Bands: Comparison within M-Region in Nigeria 2

Signal Attenuation Modeling and Simulation for 5G and Beyond...

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2nd International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Olayinka Imam-Fulani, Yusuf Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Kafin Hausa Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region. © 2023 IEEE.

关键词： Location

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