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IEEE Open Journal of Vehicular Technology 2024年 5卷 1-22页

作者： Bere, Praveen Sai Khan, Mohammed Zafar Ali Hanzo, Lajos Department of Electrical Engineering Indian Institute of Technology Hyderabad Hyderabad India School of Electronics and Computer Science University of Southampton Southampton United Kingdom

V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a $\mathscr {C}(n,k)$ code having a minimum distance ${d_{\min }}$, the proposed decoder incurs a worst-case complexity order of $2^{({d_{\min }}-1)}-1$. Notably, for codes having low ${d_{\min }}$, this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder. Authors

关键词： Vehicle to vehicle communications

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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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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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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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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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Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 486-500页

作者： Xiaoyao Zheng Baoting Han Zhen Ni IEEE the School of Computer and Information Anhui Normal UniversityWuhu 241002China the Department of Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA

Tourism route planning is widely applied in the smart tourism *** Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism *** by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on *** method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage *** crowding degree mechanism between extreme and intermediate populations is used in the two-stage *** neighborhood is determined according to the weight of the subproblem for crossover ***,Pareto layering is used to improve the updating efficiency and population diversity of the *** two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same *** with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)*** the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better *** proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

关键词： Evolutionary algorithm multi-objective optimization Pareto optimization tourism route recommendation two-stage decomposition

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Secure Student Record Management using Visual Cryptography in Educational systems 9

Secure Student Record Management using Visual Cryptography i...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： Cloud security

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Time-delay Correction Control Strategy for HVDC Frequency Regulation Services

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CSEE Journal of Power and Energy systems 2024年第5期10卷 2027-2037页

作者： Yuqing Dong Kaiqi Sun Jinning Wang Shunliang Wang He Huang Tianqi Liu Yilu Liu School of Electrical Engineering Shandong UniversityJinan 250061China School of Electrical Engineering Shandong UniversityJinan 250061China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxville 37996USA.

With the advancements in voltage source converter(VSC)technology,VSC based high voltage direct current(VSCHVDC)systems provide system operators with a prospective approach to enhance system operating stability and *** addition to long-distance transmission,the VSC-HVDC system can also provide multiple ancillary services,such as frequency regulation,due to its power ***,if a time delay exists in the control signal,the VSC-HVDC system may bring destabilizing influences to the system,which will decrease the system resilience under the *** order to reduce control deviation caused by time delay,in this paper,a small signal model is first conducted to analyze the impact of time delay on system *** a time-delay correction control strategy for HVDC frequency regulation control is developed to reduce the influence of the time *** control performance of the proposed time-delay correction control is verified both in the established small signal model and the runtime simulation in a modified IEEE 39 bus *** results indicate that the proposed time-delay correction control strategy shows significant improvement in system stability.

关键词： Frequency regulation control HVDC small signal analysis time delay time-delay correction control strategy

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