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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Sasikala Devi, S. Arulkumar, V. Kumar, Saroj Priyanka, D. PPG College of Arts and Science College Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu India Chandigarh University Computer Science and Engineering Gharuan India Institute of IT Saveetha School of Engineering Department of Knowledge Engineering Chennai India

ISBN: (纸本)9781665495158

The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assistance system, we have been able to relay information about a wandering individual's whereabouts to others who are near that person. The wearable sensor is made up of a GSM module, an Arduino UNO, and a GPS module. It is possible to determine the position of the wandering individual after they have beyond a particular meter. When the old person leaves the area, the GSM transmits the position of the wandering elderly person, which is determined with the aid of GPS. The server computer sends an SMS to the caregiver to notify him or her of the situation. The caregiver may keep track of the wandering person's whereabouts on a map by connecting to the Internet. © 2022 IEEE.

关键词： Global system for mobile communications

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Altered Cloud Service Composition Techniques for Enhanced Integration

Altered Cloud Service Composition Techniques for Enhanced In...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Arya, Nancy Gupta, Priyanka Rajalakshmi, B. Nijhawan, Ginni Praveen Alrikabi, Hareth A. Shree Guru Gobind Singh Tricentenary University Department of Computer Science and Engineering Gurugram India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Science Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Create dynamic, dependable, and cost-effective cloud service combinations using the Dynamic Cloud Service Composition (DCSC) approach. Service Ranking (SR), Adaptive Composition (AC), and Cost Optimization algorithm are used in this strategy. Their combined efforts classify services by QoS, generate flexible compositions, and minimize prices. The most important factors for ranking service quality can be found using weighted numbers. It is possible to achieve dynamic flexibility by keeping an eye on service traits and using Adaptive Composition to start reconfigurations. Cutting costs without lowering the standard of service is what cost optimization is all about. Economically beneficial compositions result. Performance Score (PS), Adaptation Rate (AR), Cost Efficiency (CE), Resilience Index (RI), Resource Utilization (RU), and (ACost) are evaluated. © 2024 IEEE.

关键词： Adaptability Cloud Service Composition Cost Optimization Dynamic Composition QoS Parameters Quality of Service Resilience Resource Utilization Service Ranking

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Place Recognition Network 17th

RPR-Net: A Point Cloud-Based Rotation-Aware Large Scale Plac...

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17th European Conference on computer Vision, ECCV 2022

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031250552

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network architecture

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Revolutionizing Internet of Things with Blockchain for Enhanced Security and Data Management 7

Revolutionizing Internet of Things with Blockchain for Enhan...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Anitha, G. Gopal, K. Jain, Alok Emad, Rafal Thandra, Nagarjuna Tewari, Preeti Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Mechanical Engineeieng Bangalore India Lovely Professional University Phagwara India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9798350350067

This paper introduces a paradigm that might transform the Internet of Things. Blockchain technology makes this system workable. Five approaches comprise the framework. Each is crucial for a powerful, automated, and scalable Internet of Things ecosystem. The framework has five techniques. Making a permanent record is the first step. This record is unchangeable. The second program handles steps. The third way secures messages. The fourth program prioritizes customer privacy. The fifth technique addresses expansion and interoperability. Comparison testing and graphic presentations reveal that the technique outperforms all key qualities. The reason is: Since reviews are detailed, the technology is supposed to transform data management and security in the fast-growing Internet of Things. This modification will likely be major. © 2024 IEEE.

关键词： Smart contract

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Predicting Cyber Attacks on Integrated Energy and Gasoline Resources 2

Predicting Cyber Attacks on Integrated Energy and Gasoline R...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Jasim, Jasim Ahmede Majed, Aaied Eqab Muraay Mahmoud, Mina Ismail Al-Azzawi, Waleed Al-Dabagh, Mustafa Zuhaer Nayef Al-Nisour University College Baghdad Iraq Al Kitab University Medical Engineering Kirkuk Iraq University: Uruk University Department of Radiology Techniques Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Knowledge University College of Science Department of Computer Science Erbil Iraq

ISBN: (纸本)9798331534950

Connecting gas and electricity networks has recently been proposed as a viable solution to manage the unpredictability of renewable energy sources and increase the versatility of power operations. When critical operational communication and control inputs from both systems must be shared, the likelihood of cybercrime increases. This study introduces a novel approach, the Energy and Gasoline Integration for Cyber-Attacks (EGICA) model, which addresses cybersecurity in interconnected gas and electricity networks - a solution to manage renewable energy unpredictability and enhance operational flexibility. The model illustrates how undetected cyberattacks can compromise network operations by using power-to-gas (PtG) and gas-to-generation (GtG) methods. Two innovative detection techniques are proposed for identifying hazardous false data in PtG/GtG schedulers. The first employs fully convolutional and wavelet transformations to detect attacks in facilities planning data. In contrast, the second utilizes a hybrid neuron with unsupervised learning to detect attacks on scheduler outputs without labelled training data. These methods support a robust cyber-attack scheduling framework within integrated gas and electricity networks. Simulation tests on a bus energy system connected to a utility grid validate the model's effectiveness using historical data for evaluation. Results show that EGICA achieves high accuracy (86.3%), precision (89.1%), recall (87.2%), F-measure (90.2%), and efficiency (78.7%) compared to other methods, confirming its reliability in detecting cyber threats in complex energy networks. © 2024 IEEE.

关键词： Cyber attacks

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Ontology-Based Crime News Semantic Retrieval System

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computers, Materials & Continua 2023年第10期77卷 601-614页

作者： Fiaz Majeed Afzaal Ahmad Muhammad Awais Hassan Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of Engineering&TechnologyLahore54890Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular *** news exists on the Internet in unstructured formats such as books,websites,documents,and *** such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement ***-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant *** are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given ***,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the *** ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal *** semantic system retrieves information in view of the similarity of the semantics among indexed data and user *** this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two *** the first experiment,we implemented a keyword-based textual IR systemusing Apache *** the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL *** keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

关键词： Web 3.0 crime ontology semantic web knowledge representation

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A Review Paper on Face Recognition Techniques 9

A Review Paper on Face Recognition Techniques

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Rashid Al-Bakri, Ibrahim Mahmood Imran Ahmad, Muhammad Md Isa, Mohd Nazrin Zuhaer Nayef Al-Dabagh, Mustafa Universiti Malaysia Perlis Faculty of Electronic Engineering Technology Perlis Malaysia Telafer University Collage of Agriculture Telafer Iraq Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9798350397376

The study of computer vision and pattern recognition is growing because of the various commercial and practical applications of these disciplines. Identification of individuals in a multitude, access control, forensics, and human-computer interactions are only among the topics studied by these areas. However, analyzing unconstrained face recognition poses ethical issues and privacy concerns. Many recent proposals employ Holistic Methods, Geometric Approach and Local-Texture Approach, methods and databases like ORL, FERET and AR Dataset to study constrained face recognition. At least some understanding of 2D perspective was achieved. This occurred in highly controlled environments where parameters such as camera angles, lighting and distance were strictly regulated. However, significant degradation in recognition performance occurred if the environment changed or the subject smiled or frowned. This critique discusses the technology utilized in face recognition, as well as the databases of methods that utilize this technology. To help guide future research, this article discusses current knowledge and suggests future directions for study in the field of facial recognition. © 2023 IEEE.

关键词： Face recognition

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R-AT: Regularized Adversarial Training for Natural Language Understanding

R-AT: Regularized Adversarial Training for Natural Language ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Ni, Shiwen Li, Jiawen Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Currently, adversarial training has become a popular and powerful regularization method in the natural language domain. In this paper, we Regularized Adversarial Training (R-AT) via dropout, which forces the output probability distributions of different sub-models generated by dropout to be consistent under the same adversarial samples. Specifically, we generate adversarial samples by perturbing the word embeddings. For each adversarial sample fed to the model, R-AT minimizes both the adversarial risk and the bidirectional KL-divergence between the adversarial output distributions of two sub-models sampled by dropout. Through extensive experiments on 13 public natural language understanding datasets, we found that RAT has improvements for many models (e.g., rnn-based, cnn-based, and transformer-based models). For the GLUE benchmark, when RAT is only applied to the fine-tuning stage, it is able to improve the overall test score of the BERT-base model from 78.3 to 79.6 and the RoBERTa-large model from 88.1 to 88.6. Theoretical analysis reveals that R-AT has potential gradient regularization during the training process. Furthermore, R-AT can reduce the inconsistency between training and testing of models with dropout. © 2022 Association for Computational Linguistics.

关键词： Rats

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Analyzing the Employee Turnover by Using Decision Tree Algorithm 5

Analyzing the Employee Turnover by Using Decision Tree Algor...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Ahmed, Saadaldeen Rashid Ahmed, Aso Kurdo Jwmaa, Swran Jawamir College of Engineering and Science Bayan University Department Computer Science Erbil Iraq College of Administration and Economics Bayan University Department of Business Administration Erbil Iraq Knowledge University Swran Jawamir Jwmaa Department of Business Administration Erbil Iraq

ISBN: (纸本)9798350337525

In knowledge-based organizations, employee turnover is a significant challenge. Frequently, a company's competitive advantage can be traced back to the tacit knowledge of its employees, which is lost when those employees depart. If a company is sincere about remaining ahead of the competition, it must do everything possible to prevent its employees from leaving in droves. To comprehend the factors contributing to employee turnover in enterprises, this article examines employee turnover. The worker was assigned to one of several predetermined attrition categories primarily based on employee demographic and employment history information. Python was used to develop decision tree models and rule sets. A predictive model was constructed using the results of the developed rule sets and decision tree models to predict future employee turnover cases. Also proposed was a software application framework to implement the study's recommendations. © 2023 IEEE.

关键词： Decision trees

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