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10th International Conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Agarwal, Ambuj Kumar Ather, Danish Astya, Rani Parygin, Danila Garg, Amit Raj, Dharm Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Sharda University Department of Computer Science and Engineering Greater Noida India Volgograd State Technical University Department of Digital Technologies in Urban Studies Architecture and Construction Volgograd Russia Chandigarh University AIT-Computer Science and Engineering Department Chandigarh India

ISBN: (纸本)9781665439701

The Internet of Things also referred to as IoT plays an important role in our everyday lives by allowing us to control electrical gadgets through networks. Controlling is accomplished by meticulously watching key parameters that produce critical information about the operation of this electrical equipment. At the same time, the critical information will transfer from the transmitting device and preserved on the cloud for apps and secondary processes to access. This investigation connects the results of environmental measures such as temperature and humidity taken by making use of sensors. The data acquired might be put to good use to create activities such as faraway dominating long-term statistics, cooling, or heating devices that can be utilised to govern the situation. The observed data is transferred to a cloud storage service through the internet and linked to an Android application. Arduino UNO and sensor devices, and ESP8266 Wi-Fi module are used in the system. Using Arduino UNO. The testing and findings will disclose the live temperature and humidity of the surroundings, as well as the soil moisture of sample soil.. The sensor element is used to check the humidity and temperature using the DHT 11 sensor. The sensors are used to take temperature readings from the environment and store the data on Thinkspeak Cloud. The ESP8266 Wi-Fi module was utilized to connect with the internet and store the data on the Thinkspeak cloud in this project. © 2021 IEEE.

关键词： Data transfer

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Deep Transfer Learning for 3d Point Cloud Understanding: A Comprehensive Survey

SSRN

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SSRN 2023年

作者： Sohail, Shahab Saquib Himeur, Yassine Amira, Abbes Fadli, Fodil Mansoor, Wathiq Atalla, Shadi Copiaco, Abigail Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi110062 India College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning. However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL) has been widely investigated as it allows knowledge transfer from synthetic to real data and mitigates data annotation constraints. Additionally, it helps reduce the discrepancy between the source and target domains using domain adaptation. Despite that, a detailed survey on its contributions to 3DPC is still missing. To the best of the authors' knowledge, this paper presents the first study shedding light on this aspect. Typically, DTL's background is presented along with the datasets and evaluation metrics. Following, a well-defined taxonomy of existing frameworks is presented. Lastly, open challenges are identified before deriving future directions. © 2023, The Authors. All rights reserved.

关键词： Knowledge management

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Rolling Stock Shunt Operation Planning in Urban Rail Transit Depots

SSRN

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SSRN 2024年

作者： Wang, Dian Yao, Ling D'Ariano, Andrea Zhan, Shuguang Wang, Lisha School of Automotive and Transportation Engineering Hefei University of Technology Anhui Hefei230009 China Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome00146 Italy School of Architecture and Urban Planning Chongqing University Chongqing400030 China

This study investigates daily rolling stock shunt operation planning in an urban rail transit depot. Given the layout of a depot and the rolling stocks (that arrive at and depart from this depot within an operation day) with given maintenance schedule, the studied problem lies in determining: 1) the position where each rolling stock stops to be parked, outside washed, and/or maintained, and 2) the conflict-free shunting plan of rolling stocks to move within depot. We transform the studied depot into different multiple-layer directed graphs (to represent the infrastructures in a microscope perspective) based on depot’s layout and maintenance schedule of rolling stocks, to illustrate the specific shunting processes of each rolling stock. By means of these resulting graphs, we formulate the studied problem as a mixed integer linear programming model by incorporating several additional requirements (that are not or rarely considered in previous related works) and a specific situation of Chinese urban rail transit system, to increase the attractiveness of our solutions for rail dispatchers. We also introduce a logic-based Benders decomposition algorithm to efficiently solve real-life problem instances, where three groups of customized acceleration mechanisms (including strengthening the bounds of variables, reducing total numbers of variables and constraints involved, and designing additional feasibility cuts) are explored to improve solution performance of our algorithm. Finally, a set of realistic and realworld instances with different scales, derived from the largest depot of Chongqing Rail Transit Line 3 in China, are investigated to demonstrate the efficiency and effectiveness of our model and algorithm. Computational results reveal that our algorithm solves a real-life instance in approximately 8 seconds which is considerably shorter than the time of dispatchers to solve the studied problem manually, thus our approach can provide strong automatical computer-aided d

关键词： Integer programming

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An Improved Weighted Ensemble Clustering Based on Two-Tier Uncertainty Measurement

SSRN

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SSRN 2022年

作者： Gu, Qinghua Wang, Peipei Li, Xuexian Chen, Lu Xiong, Neal N. School of Management Xi’an University of Architecture and Technology Xi’an China Xi’ an Key Laboratory for Intelligent Industrial Perception Calculation and Decision Xi'an University of Architecture and Technology Xi’an China Department of Mathematics and Computer Science Northeastern State University OK United States

Existing local weighted ensemble clustering algorithms strive to weight on each cluster, which take into account the differences among all clusters. However, the base clustering label on each cluster is ignored. The purpose of this paper is to combine the influence of cluster level and base clustering level in a unified ensemble clustering framework. A novel two-level weighted ensemble cluster method (TWEC) is proposed, which inserts global weighting strategy into a locally ensemble cluster learning framework. First, the cluster uncertainty based an entropy criterion is refined by considering the base clustering labels for each cluster. Then, two-level uncertainty is converted to cluster reliability via improved ensemble-driven cluster validity measure. Finally, two novel consensus functions are developed. The effectiveness of the proposed framework TWEC is verified against fourteen real-world datasets and ten comparison algorithms. The results demonstrate that the TWEC framework can improve the robustness and stability of the clustering. © 2022, The Authors. All rights reserved.

关键词： Clustering algorithms

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TensorHive: management of exclusive GPU access for distributed machine learning workloads

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 9766-9770页

作者： Paweł Rościszewski Michał Martyniak Filip Schodowski Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

TensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing resources and monitors their utilization. Reservations granted on the basis of flexible access control settings are protected by pluggable violation hooks. The job execution module includes auto-configuration templates for distributed neural network training jobs in frameworks such as TensorFlow and PyTorch.

关键词： GPU reservation access control hardware monitoring job orchestration

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PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

arXiv

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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

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Comparison of Metamerism Estimates in Video Paths using CAM's Models

Comparison of Metamerism Estimates in Video Paths using CAM'...

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International Scientific-Practical Conference Problems of Infocommunications Science and technology

作者： Aqeel Mahmood Jawad Nameer Hashim Qasim Volodymyr Pyliavskyi Department of Computer Technology Engineering Al-Rafidain University College Baghdad Iraq Department of Computer Science Cihan University of Sulaimaniya Sulaymaniyah Iraq Department Information Technologies and Apply Mathematics Odesa State Academy of Civil Engineering and Architecture Odesa Ukraine

The paper deals with the issues of metamerism in multimedia and telecommunication paths. Attention is focused on the end devices of the end-to-end path. A comparison is made in the assessments using traditional coordinate systems for representing colors, and in the coordinates of modern color models CAM16, CAM20u, ZCAM. Numerical differences in the obtained data are shown under the same input conditions. Statistical data of metamer estimates using Mac Adam ellipses are presented. A study was conducted in the work, showing that the spectral compositions of colors can be perceived as one color (based on McAdam's data), but when processed, their number can be different. Based on research, we can say that the differences can reach to 15%. When processing ZCAM models, the number of colors taken into account during transmission will be greater, which makes it possible to reduce the degree of influence of metamerism on color reproduction.

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

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Computing, Sciences and Communications (ICCSC), International Conference on

作者： Mohammad Bdair E D Kanmani Ruby Prachi Juyal Rakheeba Taseen A. Abirami A Soujanya School of Architecture Computing & Engineering University of East London London UK Department of ECE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Allied Science (Mathematics) Graphic Era (Deemed to be University) Dehradun Uttarakhand India School of computer science and Engineering Presidency university Bangalore India Department of Mathematics Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering CVR College of Engineering Ibrahimpatnam (M) Telangana India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study.

关键词： Training Accuracy Lungs Computed tomography Lung cancer Sensitivity and specificity Feature extraction Data models Stability analysis Convolutional neural networks

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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A Hybrid Intrusion Detection System Approach Using PCA and SMOTE with Advanced Ensemble Models

A Hybrid Intrusion Detection System Approach Using PCA and S...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Khaled Mahmud Sujon Nusrat Jahan Someyo Kamal Utsho Mahadi Hasan Shuvo Md Najmus Sakib Razin Department of Software Engineering Universiti Teknologi Malaysia (UTM) Johor Bahru Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Department of Software Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia Department of Architecture American International University-Bangladesh Dhaka Bangladesh Department of Electrical & Electronics Engineering Universiti Teknologi Malaysia Johor Bahru Johor Malaysia

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A persistent issue of class imbalance in intrusion detection systems (IDS) is addressed in this research, particularly in the context of cyber-physical systems and IoT devices. Current IDS approaches often struggle to detect rare attack patterns due to the dominance of normal traffic in imbalanced datasets. To address this, we present a unique strategy that combines Principal Component Analysis (PCA) for selecting features with the Synthetic Minority Oversampling Technique (SMOTE) to deal with class imbalance, applied to four powerful classifiers: Random Forest (RF), Decision Trees (DT), XGBoost (XGB), and AdaBoost. By reducing the feature space with PCA and balancing the dataset with SMOTE, our methodology significantly enhances model performance. Evaluating on the KDDCup ’99 dataset, we observe notable improvements: for instance, Random Forest achieves 96.42% accuracy, 98.52% precision, 94.68% recall 96.55% F1-score, and 93.87% MCC. Similarly, XGBoost achieves 94.78% accuracy, 96.72% precision, 92.94% recall, 94.81% F1-score, and 92.36% MCC. These results demonstrate that the integration of SMOTE and PCA effectively addresses class imbalance and enhances the detection of minority class attacks. Our findings contribute to the advancement of machine learning techniques in IDS and provide a foundation for further research into combining oversampling methods and dimensionality reduction for robust cybersecurity solutions. Future work will focus on exploring alternative oversampling methods, incorporating deep learning models, and assessing the practical deployment of the proposed methodology in real-time network environments.

关键词： Dimensionality reduction Deep learning Accuracy Computational modeling Intrusion detection Real-time systems Decision trees Random forests Principal component analysis Testing

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