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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bawistale, Khushi Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384369

Yoga during pregnancy is recommended for various reasons. It has been shown to help boost defenses, strength, and mental health by reducing stress, promoting better sleep, and enhancing the immune system. Furthermore, engaging in exercise, such as yoga, during pregnancy can help strengthen the immune system, reduce the risk of certain health conditions Hyperthyroidism, Hyperemesis gravidarum, white discharge, Disturbed sleep, Threatened Abortion etc. Moreover, yoga can help pregnant individuals maintain flexibility and muscle tone, alleviate common discomforts during pregnancy, and prepare the body for childbirth. Due to local constraints and employment responsibilities pregnant women cannot commit to specific times for regular yoga courses. To overcome the disadvantages of traditional offline prenatal yoga classes, especially for working or city women who are unable to travel long distances in city traffic, online yoga recommendations work best for their health. This study aims to create a machine learning (ML) algorithm that predicts the probable practices a woman might employ to alleviate her condition by taking into account her medical history, sickness, weariness, mood swings, and food cravings. White discharge, hyperthyroidism, hyperemesis gravidarum Disrupted sleep, Abortion Threat, Miscarriage in the Family Leg cramps, vaginal discharge, and dental cramps diarrhea, constipation, headache Vaginal itching, chest pain, exhaustion, and vomiting. The proposed Macqueen algorithm model has achieved an accuracy of 93.79% in the yoga pose recommendation ML system. © 2024 IEEE.

关键词： Obstetrics

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Preventing Spinach Stems Wastage Part Reusing Through RNN and Collaborative Filtering 8

Preventing Spinach Stems Wastage Part Reusing Through RNN an...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lekhya, K. Surendran, R. Raveena, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The proposed research work is to Prevent Spinach stem wastage Part using a Recurrent neural network in comparison with collaborative filtering for better accuracy. Materials and Methods: The Prevention of Spinach stems is carried out by the Recurrent neural network(RNN) and collaborative filtering(CBF), both of which have a sample size of 10. The iteration value was determined using G-power values of 0.8 with a 95% confidence interval. Results: The RNN method is documented to possess an accuracy of 84.10, which is higher than the accuracy of the CBF approach, as it is observed to be 70.09. The two algorithms exhibit a statistically significant difference, as indicated by a 2-tailed p-value of 0.000 (p © 2024 IEEE.

关键词： Wiener filtering

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Upgrading Coffee Bean Quality Using K-nearest Algorithm over Future Selection and Extraction to Reduce Dimensionality of Data 8

Upgrading Coffee Bean Quality Using K-nearest Algorithm over...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chandu, B. Raveena, S. Surendran, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

This research study explores the capacity of digital image processing and machine learning to assess the inherent quality of unprocessed coffee. The proposed approach includes the process of obtaining images, preparing them (which involves filtering and selecting relevant features), and analyzing them (which involves segmenting, extracting features, and classifying them). To categorize coffee quality according to morphological and color criteria, researchers used ANN, SVM, and K-Nearest Neighbors (KNN) classifiers. With an accuracy rate of 89.45% compared to 83.75% for the Support Vector Machine (SVM) algorithm and 77.85% for the k-Nearest Neighbors (KNN) strategy, the Artificial Neural Network (ANN) approach dominates the experimental results. To accurately evaluate the quality of unprocessed coffee, our results highlight the significance of careful image processing and classifier selection. © 2024 IEEE.

关键词： k-nearest neighbors

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An Investigation on Open-RAN Specifications:Use Cases,Security Threats,Requirements,Discussions

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computer Modeling in engineering & sciences 2024年第10期141卷 13-41页

作者： Heejae Park Tri-HaiNguyen Laihyuk Park Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Republic of Korea Faculty of Information Technology School of TechnologyVan Lang UniversityHo Chi Minh City70000Vietnam

The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure ***,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional *** disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network ***,this openness introduces new security challenges compared to traditional *** existing studies overlook these security requirements of the O-RAN *** gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G *** then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities *** providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.

关键词： O-RAN architecture use cases security issues security requirements security discussions

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Hand-Written Bramhi Script Recognition using Deep Learning

Hand-Written Bramhi Script Recognition using Deep Learning

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Swaroop, Bandi Vishnu Prasad, Angala Hari Reddy, Busa Thanuj Sathwik Vardhan, Puvvadi Harsha Singh, Rimjhim Padam Palaniswamy, Suja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350374933

This work involves the classification of handwritten script using deep learning with a special interest in the comparison of different pre- trained models. As one of the oldest writing systems in the world, derived from the Dravidian and southeast Semitic scripts that were developed 4,500 years ago in India and Central America, Brahmi script also has variability due to the hand-written accented forms. To address these obstacles, different pre-trained models viz., Vision Transformer (ViT), MobileNetV2, VGG16, ResNet50, DenseNet, and EfficientNet with the CNN are implemented and compared. The result of the implementation infers that all of these pre- trained models yield a higher accuracy than the custom CNN traditional CNN approach, with a peak improvement in performance shown by MobileNetV2. This lays the ground for future utilization of deep learning architectures which have the capability of preserving and understanding handwritten symbolic scripts. © 2024 IEEE.

关键词： Deep learning

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MediLocker: Centralized Electronic Health Records System 2

MediLocker: Centralized Electronic Health Records System

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Harini, N. Karishma, R.S. Pranav, M. Duvvuri, Ananya Akhil, Marrivada Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350371314

The transition from traditional paper-based patient records to advanced digital formats has not been without its challenges. While Electronic Health Records (EHRs) offer instantaneous access to detailed patient health information, there are drawbacks too. One significant drawback that was identified from the literature survey was the lack of interoperability and uniformity in patients' medical information across different healthcare facilities. This fragmentation hampers communication and collaboration among healthcare providers, leading to inefficiencies and gaps in patient care. Additionally, concerns about the security and privacy of patient data in EHRs persist, with instances of data breaches and unauthorized access raising alarm within the healthcare community. In response to these challenges, this paper proposes the development of a secure, privacy-ensuring Medilocker system aimed at investigating electronic health management systems comprehensively by assessing the status of EHR systems and efficiently managing electronic health records while safeguarding patient data. The implementation of role-based access control, coupled with data anonymization for preserving confidentiality, results in the development of a cohesive solution that facilitates the systemization, integration, and fortification of the healthcare system, while ensuring prompt access to information in compliance with privacy and security standards. © 2024 IEEE.

关键词： Authentication

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Inference of Missing Attributes in Multilevel Complex Network 5

Inference of Missing Attributes in Multilevel Complex Networ...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nettem, Nikhil Chowdary Nukala, Aashish Gopal Kaushal, Siruguppa Vishnu Sai Deepthi, L.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

Social networks exhibit a hierarchical structure where users are grouped at multiple levels based on communities, interests, or behaviors. The attributes associated with users provide additional information for various social network analysis tasks. However, the attributes may be missing for multiple reasons: incomplete data collection, or privacy concerns. Inferring missing attribute values within a hierarchical social network poses a challenge for accurate analysis and modeling. The state-of-the-art algorithm infers the attributes in a single level and less focus is given to infer the attributes at multiple levels. In this paper, the missing attributes are inferred by employing hierarchical clustering followed by CNN and Hierarchical Graph Neural Network. Our method's accuracy of attribute inference is compared with traditional machine learning models, including Logistic Regression, Random Forest, XGBoost, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes. The results demonstrate that the hierarchical clustering with the CNN approach consistently outperforms traditional machine learning models in terms of attribute inference accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Adaptive Real-Time Malware Detection for IoT Traffic Streams: A Comparative Study of Concept Drift Detection Techniques 5

Adaptive Real-Time Malware Detection for IoT Traffic Streams...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bharani, D. Lakshmi Priya, V. Saravanan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

As IoT expands dynamically, cybersecurity threats are evolving with time;thus, classical malware detection systems are no more appropriate, due to the phenomenon of concept drift and the necessity of handling large amount of data while detecting malware. Concept drift is used to refer to the statistical properties of malware that change over time as the malware behavior changes. We henceforth propose a real-time system to detect malware within a large-scale network traffic streams in the presence of concept drift. The system is capable of processing continuous large volumes of IoT traffic in real-time as it is implemented using Apache Spark and Apache Kafka. The proposed system also compares the performance of six different concept drift detection techniques: KS test, CUSUM, ADWIN, KSWIN, DDM, Page Hinkley on NSL-KDD and IoT -23 datasets. The comparison results show that KS test correctly identified concept drift in both the datasets. The proposed system makes an IoT environment with better malware detection capabilities by integrating the adaptive mechanism in real-time data processing tools apart from achieving high performance and scalability. © 2024 IEEE.

关键词： Data integration

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RARoK:Retrieval-Augmented Reasoning on Knowledge for Medical Question Answering

RARoK:Retrieval-Augmented Reasoning on Knowledge for Medical...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhan, Buchao Li, Anqi Yang, Xin He, Dongmei Duan, Yucong Yan, Shankai Hainan University School of Computer Science and Technology China Wuyi University School of Environment and Chemical Engineering China

ISBN: (纸本)9798350386226

Although large language models (LLMs) perform impressively in natural language tasks, they face several challenges, such as conventional new knowledge, generating accurate responses, and explaining their reasoning. To address these issues, we propose a new approach, Retrieval-Augmented Reasoning on Knowledge (RARoK), which combines Chain of Thought (CoT) prompts with Retrieval-Augmented Generation (RAG). By leveraging external information from knowledge graphs (KGs), RARoK iteratively refines the CoT to further optimize the reasoning process of the model. Our approach significantly outperformed the baseline in various Q&A tasks, especially in the medical domain. Compared to the SOTA method, RARoK improves Hits@1 by 3.3% on the WebQSP dataset, while improving the Hits@1 and F1 scores by 18.6% and 10.4% respectively on the CWQ dataset. The experimental results on the Knowledge Graph Question Answering (KGQA) datasets and the Medical Q&A datasets show that our method has better reasoning ability and interpretability compared to the vanilla LLMs and other retrieval-enhanced methods. Our code and data are publicly available (https://***/cskyan/RARoK). © 2024 IEEE.

关键词： Question answering

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Multimodal Sentiment Analysis Integrating Text, Audio, and Video for Emotion Detection 5

Multimodal Sentiment Analysis Integrating Text, Audio, and V...

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

作者： Jayanthi, S. Arumugam, S.S. Saveetha School of Engineering Saveetha Institite of Medical and Techinical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

This research proposes a novel cross-modal approach to sentiment analysis that integrates textual, audio, and visual modalities to enhance the accuracy and depth of emotion recognition. By combining textual features (sentiment words, syntactic features), audio features (tone, speech rate, pitch), and visual features (body language, facial expressions, hand gestures), the proposed system aims to capture the nuanced and multi-faceted nature of human emotions. This cross-modal approach provides a more comprehensive understanding of sentiment by considering the interplay between different modalities, leading to more accurate and informative sentiment analysis results. This research has the potential to significantly improve the accuracy and effectiveness of sentiment analysis applications across various domains, such as customer service, social media monitoring, and human-computer interaction. © 2024 IEEE.

关键词： Human computer interaction

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