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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Kannan, N. Sivakami, S. Senthil, S. Immaculate Rexi Jenifer, P. Murali, N. E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Trichy India School of Computing SASTRA Department of Computer Science and Engineering India

ISBN: (纸本)9789380544519

Parking congestion has become a major problem in today's metropolitan environments, resulting in lost time, higher emissions, and irritated drivers. There is a severe shortage of parking places in metropolitan areas because of the ever-growing number of the vehicles and the ever-growing urban population. This shortage has exacerbated traffic congestion and environmental pollution in addition to making it difficult and time-consuming to find a parking space. Conventional parking management systems are finding it harder and harder to handle these issues;they frequently depend on physical barriers or manual ticketing. Innovative approaches that incorporate automation and technology are becoming more prevalent to address this problem. Conventional parking management systems frequently depend on human intervention and physical infrastructure, which leads to inefficiencies and constrained scalability. In contrast, new opportunities for a more intelligent, effective, and user-friendly experience are made possible by the incorporation of deep learning methods and cutting-edge computer vision technology into parking management. This system makes use of two essential elements: automatically recognizing license plate numbers for vehicle identification and facial recognition technology for person identification. Through the smooth integration of these technologies, the system not only makes parking easier, but it also improves security and maximizes parking space usage. We will examine the essential elements, advantages, and possible effects of the Face and Number Plate-Based Smart Parking system in this survey. According to experimental findings, employing a face- and number-plate verification method can increase the efficiency of smart parking systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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Optimizing Health through Nutrition: Machine Learning Insights for Menopausal Women

Optimizing Health through Nutrition: Machine Learning Insigh...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Logapriya, E. Surendran, R. NavaneethaKrishnan, Sundara Rajulu Appathurai, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Dhanalakshmi Srinivasan University Tiruchirappalli India Department of Computer Technology KG College of Arts and Science Coimbatore India

ISBN: (纸本)9798331533137

This research provides a detailed and sequential framework for constructing a recommendation system focused on women's health, specifically for menopause management and dietary guidance tailored to individuals. These sources encompass medical records, structured questionnaires, and health monitoring programs that facilitate the tracking of essential indicators such as symptom intensity, pulse rate, body temperature, sleep duration, and dietary habits. The management of missing values is executed through forward filling, whereas normalisation of numerical values is performed via standard scaling subsequent to the application of the Standard Scaler. Ultimately, binary and category data are encoded using a one-hot encoder. Feature engineering enhances the predictive capacity of the model by generating new variables from the existing data. Linear regression, logistic regression, support vector machines, and random forest models are constructed and evaluated based on the characteristics of the data utilising performance metrics such as R-Squared (R2) and AUC-ROC, achieving maximum accuracies of 0.92 for linear regression and 0.95 for logistic regression. The system provides women with health and lifestyle advice through the application of collaborative and content-based filtering ensuring that the suggestions are highly accurate and based on information. ©2024 IEEE.

关键词： Collaborative filtering

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A Novel Hybrid Recommendation system for Riyadh Restaurants: Integrating Content-Based and Context-Aware Techniques 4

A Novel Hybrid Recommendation System for Riyadh Restaurants:...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rohilla, Vinita Kaur, Mandeep Chakraborty, Sudeshna Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India Galgotia University Department of Computer Science and Technology Greater Noida India

ISBN: (纸本)9798350364729

Recommendation systems are often used to deliver consumers a customised set of services. Their main purpose is to provide recommendations or concepts (such as restaurants, tourist attractions, medicines, movies, etc.) that effectively address consumer issues and may be used across several sectors. In today's environment, when there are so many eating alternatives, selecting the ideal restaurant to meet our interests may be difficult. Saudi Arabia's tourist industry has lately emerged as an important economic engine. It is becoming more difficult for customers to choose among the vast array of restaurants available in Riyadh due to the industry's fast expansion in recent years. This research presents a comprehensive recommendation system for Riyadh restaurants that integrates content-based filtering and context-aware location analysis to provide personalised suggestions. Utilising a dataset from Kaggle with information on 20,000 restaurants, the system employs a hybrid approach combining TF-IDF and cosine similarity for content-based filtering with geographic distance calculations for location-based recommendations. The hybrid model balances content relevance with geographic proximity, enhancing user satisfaction by delivering accurate and contextually relevant restaurant recommendations. The outcomes demonstrate an effectiveness of a hybrid method, outperforming individual techniques by generating more relevant and practical recommendations. The uniqueness of this study resides in the use of hybrid filtering, which has not before been used in comparable investigations. A more detailed and easily digestible list of suggested models is what this study report is aiming to provide. According to the findings and conclusion, the proposed strategy is effective in recommending restaurants in Riyadh. © 2024 IEEE.

关键词： Wiener filtering

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UAV-Assisted Internet of Vehicles over Licensed and Unlicensed Spectrum: Architecture, Intelligent Resource Management, and Challenges

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IEEE Internet of Things Magazine 2023年第3期6卷 78-84页

作者： Su, Yuhan Liwang, Minghui Chen, Zhong Wang, Xianbin School of Electronic Science and Engineering Xiamen University China School of Informatics Xiamen University China Western University Department of Electrical and Computer Engineering London Canada

Benefited from their flexibility and on-demand deployment capability, unmanned aerial vehicles (UAVs) have emerged as critical aerial communication platforms in future Internet of Vehicles (IoV). However, limited spectrum resources can lead to unsatisfying data rate of IoV, which thus incur large latency, especially under congested IoV network conditions. Although UAVs and road side units (RSUs) can work within the same spectrum and increase spectral efficiency, mutual interference becomes unavoidable. To this end, this article develops a heterogeneous network architecture, in which a UAV-assisted IoV system coexists with a Wi-Fi system: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference, without affecting the performance of the Wi-Fi system. A case study of resource management over licensed and unlicensed spectrum is investigated under the proposed architecture, where time and power are jointly optimized to maximize the sum user satisfaction of the system. We further provide an intelligent solution to tackle the problem in the considered case study. Simulations demonstrate that our proposed case can efficiently improve the sum user satisfaction of the system. Key challenges and opportunities for UAV-assisted IoV over licensed and unlicensed spectrum are discussed, while recommendable future research directions are investigated. © 2018 IEEE.

关键词： Wi-Fi

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Optimized Deep Learning Framework for Personalized Nutritional Recommendations Across the Menstrual Cycle 5

Optimized Deep Learning Framework for Personalized Nutrition...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Logapriya, E. Surendran, R. Poornima, D. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This study presents a deep learning architecture designed to monitor a woman's menstrual cycle and provide personalized dietary suggestions to improve health. The system employs complex models, including CNNs, RNNs, Transformers, T-GNNs, VAEs, and GANs, to predict the intensity of symptoms, determine the phases of the menstrual cycle, and offer personalized nutritional advice. By analysing data from menstrual diaries, food consumption, and symptom tracking, the method establishes a strong association between hormonal fluctuations, symptoms, and nutritional needs. Model accuracy is enhanced through data preprocessing techniques, including imputation, normalization, and augmentation, which provide high-quality training inputs. Research shows that Transformer models outperform other approaches in phase recognition (96% accuracy) and phase-aware nutritional recommendation accuracy. This research highlights how deep learning can transform women's healthcare by providing personalized nutritional recommendations based on data that align with the phases of a woman's menstrual cycle, ultimately contributing to improved health for women. © 2024 IEEE.

关键词： Generative adversarial networks

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Generation Z’s adoption of IoT: protection motivation theory as the underlying model and gender as a moderator

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Journal of systems and Information Technology 2023年第2期25卷 133-159页

作者： Mahmud, Arif Yusoff, Mohd Najwadi Husin, Mohd Heikal School of Computer Sciences Universiti Sains Malaysia George Town Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Purpose: The factors that motivate Generation Z individuals to use the Internet of Things for security purposes have yet to be explored. Therefore, the purpose of this paper is to close a research gap by verifying the protection motivation theory using gender as a moderator. Design/methodology/approach: The authors used a purposive sampling approach to collect data from Dhaka city, in which 370 valid responses were selected. Additionally, the quantitative and cross-sectional survey used a seven-point Likert scale. Afterward, the evaluation approach included three phases: a measurement model, a structural model and multi-group analysis. Findings: Vulnerability, self-efficacy and response-efficacy were discovered to be critical predictors with a variance of 60.4%. Moreover, there was a significant disparity between males and females in two relationships, response efficacy and intention as well as response cost and intention. Practical implications: This research expands our understanding of Generation Z consumers' behavioral intentions to take measures against household threats, allowing preventative programs to be improved. Further, in the case of applying coping strategies, a practical difference between males and females has been found that must be bridged through awareness campaigns. Originality/value: This study has made a unique contribution to the information system literature. First, the role of protection motivation theory factors in addressing security concerns in homes has been assessed. Second, the coping evaluation process has a greater impact on users' intentions than the threat appraisal process. However, males and females use slightly different approaches to defending themselves against the threat. © 2023, Emerald Publishing Limited.

关键词： Internet of things

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Interoperability and Standardization in the Internet of Things 9

Interoperability and Standardization in the Internet of Thin...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Saidala, Ravi Kumar Haseeb, V.V. Padmapriya, G. Jayabalan, S. Priya, V. Kalai Shaikh, Anwar Ahamed Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Nam College Department of Computer Science Kerala Tamil Nadu Kallikkandy 670693 India School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Panimalar Engineering College Department of Computer Science And Business System Tamil Nadu Chennai600123 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Puduvoyal601206 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350365092

This research highlights the importance of standards and interoperability in the IoT. An essential component of IoT ecosystems is interoperability, which refers to the ability for various systems and devices to converse and share data. Formats for data, methods of communication, and safeguards are all outlined by standards. Widespread adoption and scalability are made possible through interoperability and standards, which allow devices from many manufacturers to communicate with one another. This research investigates the alliances, regulatory authorities, and commercial associations that cooperate in the creation and advancement of open standards that can be embraced by other entities. The main objective of the research is to showcase the construction of an Internet of Things device capable of communicating with various embedded peripheral protocols utilizing Plug and Play (PnP) technology. Moreover, the research examines the impact of regulatory frameworks on the IoT ecosystem and their role in fostering interoperability. An IoT ecosystem necessitates a standardized and open environment to provide optimal performance in terms of connectivity, data exchange, and innovation. This research contributes to the progress of harmonizing and achieving interoperability among World Wide Web Things. © 2024 IEEE.

关键词： Interoperability

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Securing ResNet50 against Adversarial Attacks: Evasion and Defense using BIM Algorithm 7

Securing ResNet50 against Adversarial Attacks: Evasion and D...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Lourdu Mahimai Doss, P. Gunasekaran, M. Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Deep neural networks, such as ResNet50, have shown remarkable performance in image classification tasks. However, susceptibility to adversarial attacks, where small perturbations to input images can result in misclassifications, is a concern. The BIM algorithm is a popular technique for generating adversarial examples. The objective of this research is to explore the vulnerability of ResNet50 DNNs trained on the ImageNet Stubs dataset to evasion attacks using the BIM algorithm. Additionally, the effectiveness of various defense strategies, including adversarial training, defensive distillation, and input transformations, is examined. The results reveal that ResNet50 DNNs are vulnerable to BIM-based evasion attacks and that these defenses can enhance their robustness. Overall, this work underscores the importance of defending against BIM-based evasion attacks to ensure the security and reliability of ResNet50 DNNs. © 2023 IEEE.

关键词： Iterative methods

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Interactive Learning with AlphAR: A Symbol-Recognition Aid for Dyslexic Children Through Augmented Reality

Interactive Learning with AlphAR: A Symbol-Recognition Aid f...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Shilbi, Susan Chhirolya, Vaidik Basha, Niha Kamal Surendiran, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering Karikal India

ISBN: (纸本)9798331540685

Dyslexia is a neurological condition that affects a person's ability to read, write, and spell. It is characterized by difficulties in processing phonological information, leading to challenges in acquiring fluent and accurate reading skills. Early assistance fosters a positive learning trajectory, enhancing the overall well-being and confidence of dyslexic individuals. AlphAR is an innovative augmented reality (AR) web system specifically tailored to address the challenges of symbol recognition in dyslexic children, developed using cutting-edge A-Frame and *** technologies. It harnesses marker-based AR to create an immersive learning environment. The novelty lies in its seamless integration of marker patterns with corresponding 3D models, facilitating interactive learning experiences for children aged 5 to 10. Through intuitive user interface elements and audio cues, AlphAR enhances engagement and promotes auditory learning. Ethical design principles ensure inclusivity and user-friendliness, prioritizing participant comfort and engagement. Users interact with the system through intuitive gestures, such as flipping down and left, further enhancing skill development. The iterative nature of AlphAR allows for adaptability to individual learning paces, fostering a positive and inclusive experimental environment. To validate AlphAR's effectiveness, we will benchmark it against established AR interventions like LitAR, AHA system and many others. © 2024 IEEE.

关键词： Contrastive Learning

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