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Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

Neural Computing and Applications 2024年 1-16页

作者： Jain, Dipika Sangwan, Saurabh Raj Kumar, Akshi Department of Computer Science and Engineering Delhi Technological University New Delhi India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computing Goldsmiths University of London London United Kingdom

In the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity. © The Author(s) 2024.

关键词： Deep learning

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HealthAir: A Crowdsourcing mHealth Platform for Air Pollution Effects on Citizens Health Status 19

HealthAir: A Crowdsourcing mHealth Platform for Air Pollutio...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsidimas, Ioannis Nikoletseas, Sotiris Panagiotou, Stefanos H. Spyropoulou, Christina University of Patras Computer Engineering and Informatics Department Greece Greece

ISBN: (纸本)9798350346497

Crowdsourcing systems offer numerous advantages for acquiring data, which can contribute to the development of modern concepts such as Smart Cities. Air quality in urban areas has been identified as a crucial theme and requirement, towards ideal city of the future that can be adapted upon its citizens' needs and comfort. Thus, besides the sensing infrastructure, a complementary system must be implemented in order to enable the public authority and municipality awareness over citizens' personalised and subjective input, in a cost efficient way that can be easily upscaled. In this work, we present HealthAir, an mHealth application alongside its subsystems (namely a web application and a smartwatch version), that aims to increase and complement the sensing capacity of a Smart City, with respect to the air quality-wise health of the citizens. This is achieved based on questionnaires and surveys approved by the health community, and services and features that implement a bidirectional interaction between the system and the users. Finally, users' data and air quality measurements from a third party platform are combined and presented for the case study realization and demonstration. © 2023 IEEE.

关键词： Air quality

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Cost-effective Alternatives for Squarer in Pan-Tompkins Algorithm 24

Cost-effective Alternatives for Squarer in Pan-Tompkins Algo...

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6th International Electronics Communication Conference, IECC 2024

作者： Nagatomo, Taiki Sato, Toshinori Department of Electronics Engineering and Computer Science Fukuoka University Graduate School of Engineering Japan Department of Electronics Engineering and Computer Science Fukuoka University Faculty of Engineering Japan

ISBN: (纸本)9798400717598

Reducing power consumption in battery-powered medical edge devices is highly required. This study aims to enhancing power efficiency in the Pan-Tompkins algorithm used for electrocardiogram (ECG) feature detection by adopting approximate computing. This paper specifically focuses on the squaring step within the algorithm, investigating eight alternative approaches to replace the multiplier with a dedicated squarer. We evaluate power and area reduction rates, as well as the feature detection accuracy of the proposed approximate squarers. The best candidate achieves a 99.40% power reduction and a 99.47% area reduction while maintaining a 99.67% detection accuracy. © 2024 ACM.

关键词： Electrocardiograms

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Deep Learning Model for Human Detection-Enhanced YOLO Comparative Analysis 4

Deep Learning Model for Human Detection-Enhanced YOLO Compar...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, P. Aswathy, R.H. Soms, Nisha Niranjani, V. Saveetha School of Engineering Simats Department of Computer Science and Engineering Thandalam Chennai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri eshwar college of engineering Department of Computer science and engineering Coimbatore India

ISBN: (纸本)9798331540364

Video surveillance systems employing closed-circuit television (CCTV) are crucial for public safety. Accurately detecting humans within video frames is a core component of this technology. This knowledge is crucial for numerous uses, including detecting possible dangers, observing crowd dynamics, and examining patterns of human behavior. This research introduces a new method called YOLOv8, designed to enhance human detection capabilities within CCTV footage. The proposed algorithm, YOLOv8, is built upon the popular object recognition framework, YOLO, but with significant enhancements to improve its accuracy and efficiency in detecting humans. YOLOv8 utilizes deep convolutional neural networks (CNNs) and advanced object recognition techniques to achieve high accuracy in human detection, even in challenging environments such as low light conditions, occlusions, and varying scales of human poses. To assess the effectiveness of YOLOv8, various tests were performed using standard datasets and real-world CCTV surveillance footage. The results demonstrate that YOLOv8 outperforms existing human detection algorithms regarding accuracy, robustness, and real-time processing capability. YOLOv8 achieved an average precision of over 95% in human detection, making it highly suitable for everyday CCTV surveillance. The proposed YOLOv8 algorithm has the potential to significantly improve human detection in security camera footage and enhance public safety. This research contributes to advancing human detection technology and can benefit various domains, including law enforcement, public safety, and video analytics. © 2024 IEEE.

关键词： Convolutional neural networks

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Anonymous TollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

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computers, Materials & Continua 2024年第6期79卷 3495-3518页

作者： Jane Kim Soojin Lee Chan Yeob Yeun Seung-Hyun Seo Department of Electronic&Electrical Engineering Graduate SchoolHanyang UniversitySeoul04763Korea Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates School of Electrical Engineering Hanyang UniversityERICAAnsan15588Korea

As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation *** Toll Collection(ETC),which is one of the services included in ITS systems,is an automated system that allows vehicles to pass through toll plazas without stopping for manual *** ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system *** ensure secure and efficient toll payments,designing a distributed model for sharing toll payment information among untrusted toll service providers is ***,the current ETC system operates under a centralized ***,both toll service providers and toll plazas know the toll usage history of *** raises concerns about revealing the entire driving routes and patterns of *** address these issues,blockchain technology,suitable for secure data management and data sharing in distributed systems,is being applied to the ETC *** enables efficient and transparent management of ETC ***,the public nature of blockchain poses a challenge where users’usage records are exposed to all *** tackle this,we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of *** proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ***,malicious vehicles’identities can be traced when they attempt to reuse *** conducting simulations,we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10%(when the ring size is 50)of the fees required in previous studies.

关键词： Blockchain electronic toll collection smart contract traceable ring signature

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Granular classifier:Building traffic granules for encrypted traffic classification based on granular computing

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Digital Communications and Networks 2024年第5期10卷 1428-1438页

作者： Xuyang Jing Jingjing Zhao Zheng Yan Witold Pedrycz Xian Li State Key Laboratory on Integrated Services Networks School of Cyber EngineeringXidian UniversityChina Department of Electrical and Computer Engineering University of AlbertaCanada Institute for Future(IFF) Qingdao UniversityQingdaoChina

Accurate classification of encrypted traffic plays an important role in network ***,current methods confronts several problems:inability to characterize traffic that exhibits great dispersion,inability to classify traffic with multi-level features,and degradation due to limited training traffic *** address these problems,this paper proposes a traffic granularity-based cryptographic traffic classification method,called Granular Classifier(GC).In this paper,a novel Cardinality-based Constrained Fuzzy C-Means(CCFCM)clustering algorithm is proposed to address the problem caused by limited training traffic,considering the ratio of cardinality that must be linked between flows to achieve good traffic ***,an original representation format of traffic is presented based on granular computing,named Traffic Granules(TG),to accurately describe traffic structure by catching the dispersion of different traffic *** granule is a compact set of similar data with a refined boundary by excluding *** on TG,GC is constructed to perform traffic classification based on multi-level *** performance of the GC is evaluated based on real-world encrypted network traffic *** results show that the GC achieves outstanding performance for encrypted traffic classification with limited size of training traffic and keeps accurate classification in dynamic network conditions.

关键词： Encrypted traffic classification Semi-supervised clustering Granular computing Anomaly detection

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied Science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Improved Runge Kutta Optimization Using Compound Mutation Strategy in Reinforcement Learning Decision Making for Feature Selection

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Journal of Bionic engineering 2024年第5期21卷 2460-2496页

作者： Jinpeng Huang Yi Chen Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065China Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

Runge Kutta Optimization(RUN)is a widely utilized metaheuristic ***,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world opti-mization *** address these challenges,this study aims to endow each individual in the population with a certain level of intelligence,allowing them to make autonomous decisions about their next optimization *** incorporating Reinforcement Learning(RL)and the Composite Mutation Strategy(CMS),each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases,referred to as *** is,each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different *** validate the competitiveness of RLRUN,comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark *** comparative experiments with 13 conventional algorithms and 10 advanced algorithms were *** experimental results demonstrated that RLRUN excels in convergence accuracy and speed,surpassing even some champion ***,this study introduced a binary version of RLRUN,named bRLRUN,which was employed for the feature selection *** 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets,bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some *** conclusion,the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.

关键词： Runge Kutta optimization Metaheuristic algorithm Feature selection Reinforcement learning

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Beyond boundaries: Unifying classification and segmentation in wildfire detection systems

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Multimedia Tools and Applications 2024年 1-33页

作者： Singh, Swapnil Vazirani, Vidhi Singhania, Sanvika Suroth, Vaishnavi Singh Soni, Vaibhav Biwalkar, Ameyaa Krishnan, Deepa Computer Science Department Virginia Tech BlacksburgVA United States Computer Science Department Northwestern University EvanstonIL United States Computer Engineering Department Mukesh Patel School of Technology Management & ampEngineering NMIMS University Maharashtra Mumbai India

Forest fires, a dangerous natural phenomenon, cause large-scale destruction in forests and nearby communities. In this paper, we leverage the capabilities of classification and fast prediction of machine learning and deep learning to detect forest fires by proposing a dual-stage approach of identification and segmentation of burnt/burning regions. The initial identification step uses simple classification by harnessing custom convolutional neural network and AlexNet to detect satellite images displaying signs of burning, followed by the next step of segmentation, which employs UNet and SegNet architectures to precisely localize the burnt/burning areas. We chose UNet considering its ability of efficient feature preservation, and SegNet prominent use for precise boundary delineation. For training our models, we have curated a dataset using Google Earth Engine, querying latitude, longitude, and dates. Image processing techniques facilitated the extraction of segmentation masks, bounding box images, and segmented images. The post-training results portray that CNN outperforms AlexNet with an 88.19% accuracy for classification, while in segmentation, UNet excelled over SegNet with a dice score of 0.6869. If deployed, this study stands to benefit forest officials, local communities, and biodiversity. Moreover, it aids afforestation teams in identifying burnt zones for targeted reforestation efforts, ensuring a more effective response to forest fires. © The Author(s) 2024.

关键词： Landsat

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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