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Exploring the Impact of Herbal Remedies on Autism Spectrum Disorder Using AGLSTM Analysis

Exploring the Impact of Herbal Remedies on Autism Spectrum D...

2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Bagiyalakshmi, N. Veeramakali, T. SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350319200

Autism is a personality disorder that can cause significant emotional, intellectual, and behavioral deficits. Autism is recognized as an illness on the spectrum because, while certain kids might have mild symptoms, others may have extensive life-challenging abnormalities. However, autistic people have unique characteristics and health goals and may react differently to conventional or herbal therapy than others. Herbal medications might be a possible approach to helping autistic people's bodies and brains. However, further research is needed to determine whether herbal therapy is useful for autistic individuals. To gain insight into how people think regarding herbal medication for autism, data must be processed promptly as it is generated, which may be accomplished using sentiment assessment, which analyzes polarization in writings. This paper proposes the AGLSTM Model, a hybrid model that solves the sentiment evaluation issue by combining LSTM with agglomerative clustering. The suggested system signifies text data into numerical vectors that illustrate text features such as the frequency of words and embeddings of phrases using the TF-IDF technique and the word2vec model. Every word becomes assigned to a cluster using a method known as clustering based on how similar it is to the cluster nucleus or other texts in the region. The outcome of the experiment is a set of clusters composed of sentences with comparable emotional content. The LSTM framework can detect long-term correlations between word patterns and classify feedback as positive or negative. © 2023 IEEE.

关键词： AGLSTM Autism Clustering Herbal medicine

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You Are the First Line of Defense: Integrating Humans into Information Security Frameworks

You Are the First Line of Defense: Integrating Humans into I...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Al-Mohammed, Bashayer Abdulla Baydoun, Ali Oryx Universal College in Partnership with Liverpool John Moores University Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

This research aimed to understand the current level of security training and cultural awareness among employees across different organizations. The research draws on data collected through a comprehensive questionnaire between employees, providing insights into the relationship between demographic factors and information security awareness. The findings highlight the need for tailored training programs incorporating cultural nuances to mitigate risks effectively. This research contributes to developing adaptable and relevant information frameworks, emphasising users' critical role as the first line of defence against cyber threats. The findings of this research will benefit various stakeholders, such as Governments, semi-governments, and private and non-government organizations. © 2024 IEEE.

关键词： Awareness Culture Cyber Threats Cybersecurity Framework Human Element Information Security Training

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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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Resilience of Aviation Cyber-Physical Systems to Malicious Attacks

Resilience of Aviation Cyber-Physical Systems to Malicious A...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Abozaid, Fatemah Baydoun, Ali Oryx Universal College in Partnership with Liverpool John Moores University Department of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350367560

The inter-connectivity of critical aviation operations are the prime targets for Advance Persistent Threats (APTs) and others malicious actors due to increasingly relying on the Cyber-Physical Systems (CPS). This paper examines the current cyber resilience of aviation CPS to such cyber threats, emerging trends, and proposing new and effective resilience strategies. Using a multi-pronged approach, data were collected through survey from the aviation professionals in the state of Qatar. Quantitative analysis was conducted on survey data to assess trends in threat perception and resilience measures, while qualitative analysis of interviews provided in-depth insights into current vulnerabilities and effective mitigation strategies show that outdated software, insufficient access controls, and complex supply chains increase the risk to advanced persistent threats (APTs) and other sophisticated attacks. Our findings can assist the industry leaders, policymakers, and cybersecurity professionals to get valuable insights to safeguard the aviation ecosystem against the growing cyber threats. © 2024 IEEE.

关键词： Cyber attacks

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Robustness of Explainable AI Algorithms for Disease Biomarker Discovery from Functional Connectivity datasets

Robustness of Explainable AI Algorithms for Disease Biomarke...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Girish, Deepank Chan, Yi Hao Gupta, Sukrit Xia, Jing Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore Indian Institute of Technology Department of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350351552

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies. © 2024 IEEE.

关键词： Graph neural networks

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Exploring Sentiment Detection in Healthcare: An Exhaustive Analysis

Exploring Sentiment Detection in Healthcare: An Exhaustive A...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Bagiyalakshmi, N. Veeramakali, T. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350305708

In our daily lives, sentiment analysis is essential. People usually make decisions after seeking advice from others. We used to rely on our judgments before the internet on the advice of our friends and relatives. But now, we have high-performance source sentiment analysis, which collects and analyses opinions from a variety of channels, including social media. To correctly represent emotion in a phrase, we must concentrate on idioms, sarcasm, negations, and context terms. A range of deep learning and machine learning techniques can be used to overcome the aforementioned issues. The aforementioned parameter will be used by the algorithm to identify the polarity of a statement. This study proposal covers the problems with sentiment analysis as well as its numerous levels. The review talks about the challenges and particularities of sentiment analysis. These have to do with issues like textual information, peculiarities of language, emotional vocabulary relevant to particular domains, and security and confidentiality of data. The report also emphasizes how sentiment analysis might promote public healthcare initiatives, boost pharmaceutical methods for making choices, as well as improve patient care. © 2023 IEEE.

关键词： Deep Learning Healthcare Machine learning Recommendation system Sentiment Analysis

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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Evolution of Consensus Algorithms in Blockchain Technology

Evolution of Consensus Algorithms in Blockchain Technology

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Harshini Poojaa, K. Ganesh Kumar, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu India

ISBN: (纸本)9798350335835

Blockchain being a decentralised, distributed ledger, has attracted industries to implement the solutions on blockchain. The transactions are agreed in a deterministic manner because of the availability of consensus algorithms. The state of the machine is concluded based on the agreement generated by the consensus algorithm. The performance of the blockchain system can be increased to the order of magnitude when the right consensus is used. In this paper, the limitations and performance of various consensus algorithms have been reviewed along with the characteristics of the same. In addition to that, this paper compares the consensus algorithms based on various parameters such as speed, double-spending, 51% attack and many more. © 2023 IEEE.

关键词： Blockchain

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LivedataLab: A Cloud-based Open Lab for Integrating Big data Research, Education, and Applications

LiveDataLab: A Cloud-based Open Lab for Integrating Big Data...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Zhai, ChengXiang University of Illinois at Urbana-Champaign Siebel School of Computing and Data Science Grainger College of Engineering United States

ISBN: (纸本)9798350362480

We present the vision of LivedataLab and discuss the new research directions and application opportunities it opens up. LivedataLab is envisioned to be a cloud-based open lab infrastructure where research, education, and application development in big data can be integrated in one unified platform, thus accelerating research, technology transfer, and workforce development in big data. © 2024 IEEE.

关键词： Cloud platforms

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Automatic 3D modeling and exploration of indoor structures from panoramic imagery 24

Automatic 3D modeling and exploration of indoor structures f...

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2024 SIGGRAPH Asia 2024 Courses, SA Courses 2024

作者： Gobbetti, Enrico Pintore, Giovanni Agus, Marco National Research Center in High-Performance Computing Big Data and Quantum Computing Cagliari Italy Visual and Data-intensive Computing Center for Advanced Studies Research and Development in Sardinia Cagliari Italy College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798400711350

Surround-view panoramic imaging delivers extensive spatial coverage and is widely supported by professional and commodity capture devices. Research on inferring and exploring 3D indoor models from 360° images has recently flourished, resulting in highly effective solutions. Nevertheless, challenges persist due to the complexity and variability of indoor environments and issues with noisy and incomplete data. This course provides an up-to-date integrative view of the field. After introducing a characterization of input sources, we define the structure of output models, the priors exploited to bridge the gap between imperfect input and desired output, and the main characteristics of geometry reasoning and data-driven approaches. We then identify and discuss the main sub-problems in indoor reconstruction from panoramas and review and analyze state-of-the-art solutions for indoor capture, room modeling, integrated model computation, visual representation generation, and immersive exploration. Relevant examples of implemented pipelines are described, focusing on deep-learning solutions. We finally point out relevant research issues and analyze research trends. © 2024 Copyright held by the owner/author(s).

关键词： 3D reconstruction

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