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共 65025 条记录，以下是141-150 订阅

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What Are the Barriers Hindering Smart Community Governance in China? An Analysis Using the ISM Approach

What Are the Barriers Hindering Smart Community Governance i...

引用

2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Gu, Tiantian Fang, Ran Liu, Xu Song, Xiaoyu Hao, Enyang Zhao, Aoqi Dept. of Engineering Management China Univ. of Mining and Technology Xuzhou China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Smart community governance has encountered significant barriers, hindering the digitalization process inherent to modern communities. Nevertheless, current research lacks a comprehensive analysis of the barriers to smart community governance and their inner relationships, thereby complicating the identification of targeted strategies for improving smart community governance. This study aims to clarify these barriers and their inner relationship, with the objective of proposing strategies to enhance smart community governance. To achieve this, this research initially identified 18 key barriers to smart community governance through a systematic literature review and expert interviews. Subsequently, reliable interconnections between these barriers were established using interpretive structural modeling, leading to the development of a five-layer model of the barriers to smart community governance. Then, valuable improvement strategies have been proposed, grounded in the interrelationships between these barriers. The findings of this study not only contribute to enriching the existing body of knowledge in smart communities but also hold significant practical implications for addressing these barriers to facilitate the governance of smart communities. © ASCE.

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TAPNet: Tunnelled Asymmetric Pyramid Network for Achieving a Pragmatic Semantic Segmentation Solution for Autonomous Driving 13

TAPNet: Tunnelled Asymmetric Pyramid Network for Achieving a...

引用

13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Atif, Nadeem Mazhar, Saquib Bhuyan, M.K. Ahamed, Shaik Rafi Dept. of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati India

ISBN: (纸本)9798331516147

—Semantic segmentation, vital in autonomous driving, is a pixel-level prediction task that is used to partition images into distinct regions. Effective models must capture multi-scale context to enhance segmentation quality but also need to be computationally efficient for practical deployment on resource-constrained devices. Hence, the challenge is to maximize accuracy and speed while minimizing model complexity to transform semantic segmentation into a successful practical reality. In response, we propose a Tunneled Asymmetric Pyramid Network (TAPNet) that combines short and long-range context with high efficiency. It is based on the TAP module, which extracts multi-scale short-range context in a highly efficient manner. Unlike conventional methods, our feature tunneling technique reinforces feature maps with finer details, enhancing segmentation accuracy. To efficiently capture long-range context, we propose the Pairwise Aggregated Reinforced Scale-Enlarged Spatial Pyramid Pooling (PARSE-SPP) module, designed for ultra-low computational overhead. Based on the TAP and PARSE-SPP modules, we are able to achieve a high-performing segmentation model that strikes an optimal balance between accuracy and efficiency. This makes TAPNet a strong candidate for autonomous driving in real-time scenarios. More specifically, with only 0.29 million parameters, it achieves 68.75% and 68.6% mIoU on Cityscapes validation and test set, respectively. More interestingly, it achieves 161.55 FPS on a single RTX 3090 card. © 2025 IEEE.

关键词： Semantic Segmentation

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Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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Inspection of Ethical and Privacy Concerns in Online Social Networks

Inspection of Ethical and Privacy Concerns in Online Social ...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Chandana, D.M. Sankalp, M.R. Kathavate, Sheela Aruna, S. Bms Institute of Technology and Management Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

As the prevalence of Online Social Networks (OSNs) are created, it is apparent that questions related to ethics and privacy become more ambiguous and this paper aims at giving an overview on privacy concomitants and ethical concerns surrounding OSNs, with especial attention to the susceptibility of the user data, ethical dilemmas with regard to content regulating and the impact of policies and regulations. The proposed system provides the entry to establish the enhanced, sound, and fairly protected user privacy and ethical policies for OSNs with the help of boosted privacy-related settings, decentralised identity management, the utilisation of homomorphic encryption, and federated learning. It also underlines the need to have adaptive privacy dashboards, information accessibility real-time data, and other ethical measures keeping risks like cyberbullying and unauthorized usage of data checked. The study also examines new trends which have been occasioned by artificial intelligence in content moderation and the legal development aspect of it. As much as this survey focuses on current practices and asks questions in support of proactive measures, it seeks to enlighten policy makers on the best practices to engage in and create awareness to positive handling of user data in OSNs for the safety of users, as well as promoting ethical usage of the social networking systems. © 2025 IEEE.

关键词： Tweets

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Enhanced Realtime Sign Language Detection and Multilingual Translation system

Enhanced Realtime Sign Language Detection and Multilingual T...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kakkar, Madhur Daniel, John Sudha, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331538538

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities. © 2025 IEEE.

关键词： Interactive computer systems

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Risk Assessment of Urban Renewal Projects in Old Residential Areas Based on C-OWA Operator-FCE

Risk Assessment of Urban Renewal Projects in Old Residential...

引用

2024 International Conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Wang, Zhikai Fang, Jun Dept. of Civil Engineering and Architecture Wuhan Univ. of Technology Wuhan China

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

As an important part of urban renewal, old residential renovation projects have been gradually promoted throughout the country. However, due to the complex and diverse objects of renovation and the influence of a large number of people involved, there are high reconstruction risks. In order to manage and control risks reasonably and effectively, this paper firstly identified 22 risk factors through literature research and expert interviews and constructed a risk evaluation index system containing 6 risk dimensions. The Fuzzy comprehensive evaluation model (FCE) and C-OWA operator are introduced to weigh the risk indicators, and the fuzzy comprehensive evaluation model of old residential urban renewal project based on C-OWA is constructed to determine the risk level. Taking the reconstruction project of Rongdong Community in Qiaokou District of Wuhan as an example, the risk measure evaluation is carried out, and the scientific rationality of the model is verified. The risk evaluation results show that the overall risk evaluation of the project is medium risk, and the main risk influencing factors are urban renewal policy risk, financing risk, contract risk with the owner, land price risk, and enterprise competition risk, which points out the direction for risk management. © ASCE.

关键词： Risk management

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Real-Time Gaze Tracking for Online Examination

Real-Time Gaze Tracking for Online Examination

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Preyanshu Kumar, Suresh Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a student's eye gaze and head movements during an exam. This system leverages Dlib's pre-trained frontal face detector and 68-point facial landmark predictor to detect facial features and track the direction of eye movement and head position. By analyzing these metrics, the system can flag behaviors associated with potential cheating, such as looking away from the screen for extended periods. This paper discusses the design and implementation of the proctoring system, details the detection algorithms, and evaluates its effectiveness for real-time monitoring. © 2025 IEEE.

关键词： Online systems

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Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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Driver Drowsy Detection system Using Machine Learning and Open CV

Driver Drowsy Detection System Using Machine Learning and Op...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Akash, G.S. Palati, Madhu Rajesh Deepak, H.S. Bms Institute of Technology & Management Dept. of Electrical and Electronics Engineering Bengaluru India

ISBN: (纸本)9798331538538

Driver fatigue is another significant risk factor that claim a fairly good proportion of the accident rates globally. Fatigue affects the important human functions including decision making, ability to concentrate and reaction time making it of paramount importance to have good counter measures in place. That is why, the proposed Driver Drowsiness Detection system in this paper focuses on real-time video surveillance and machine learning to identify symptoms of drowsiness. The drowsiness detection of the system is made possible by integrating OpenCV and other computer vision algorithms that take pictures of the face via a camera and examine for patterns that are characteristic of drowsiness, which include yawning, blinking of the eyes frequently, or the closing of the eyes for too long. That is, the use of OpenCV in facial landmark detection and other computations such as EAR and MAR with the help of two well trained models on deep learning. The system uses adaptive thresholds and sophisticated preprocessing to ensure its performance stays high regardless of the conditions which include light, camera angle, and facial expressions. Such consistencies of both the methodologies in convergence help the system to assess the driver's alertness level and immediately produce an alert the moment the driver looks sleepy. Intended to be deployed in semi-autonomous vehicles, vehicles in commercial fleets, as well as individual automobiles, the proposed system can be implemented at a large-scale to help increase road safety. Thus, eliminating the risks related to fatigue-related accidents, it opens an opportunity to minimize fatality rates and change the future environment in transport. The technical issues, the difficulties and the realistic uses of the system are also described This paper focuses on the contribution of the system to enhance road safety and reduce the number of deaths. © 2025 IEEE.

关键词： Human reaction time

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Enhanced Super-Resolution with EDSR: Optimizing Image Quality across Multiple Scaling Factors 3

Enhanced Super-Resolution with EDSR: Optimizing Image Qualit...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Venkat, Anika Kirana Kulkarni, Keerti Bnm Institute of Technology Dept. of Electronics and Communication. Engineering Bengaluru India

ISBN: (纸本)9798331515911

In today's world where high resolution visual content is preferred in domains like medical imaging, satellite imagery, and digital entertainment, effective image super resolution is crucial for success. This paper focuses on not just upscaling the resolution but restoring the lost details to improve the overall quality of images. The enhanced deep residual network (EDSR) is utilized to significantly improve image resolution and reduce blurring and outperforming traditional methods. While EDSR does not eliminate all forms of quality degradation, it effectively improves image sharpness, clarity and balancing computational efficiency with enhanced detail construction. The model's performance is elevated using traditional metrics like PSNR and SSIM to quantify the quality enhancement. In the end comparisons are drawn across the scaling factors of 2x, 3x and 4x to show the models ability to perform across various domains. Here, the approach was evaluated across various scaling factors of 2x, 3x, and 4x showing consistent improvement in image clarity, sharpness and structural integrity and surpassing traditional interpolation methods. © 2025 IEEE.

关键词： Medical imaging

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