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11th International Conference on Recent Trends in computing, ICRTC 2022

作者： Sharma, Pinki Kumar, Santosh Computer Science and Engineering ABES Engineering College Ghaziabad India School of Computing Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789811988240

A quick and easy technique to gauge public opinion on a political party and its leaders is through the use of sentiment analysis on Twitter. Prior sentiment analysis algorithms have struggled to identify the best classifier for a given classification challenge. As long as a single classifier is selected, there is no guarantee that it will perform the best on unknown data. We're combining the results of several classifiers in order to lessen the chance of picking the wrong one. Our method is to classify the twitter data on the basis of sentiments using a ML classifier that is combined with a lexicon-based one. We've added the numerous classifier such as SentiWordNet, Naive Bayes, and Hidden-Markov Model (HMM) to new set of classifiers. In this case, the majority voting principle is used to assess whether or not a tweet is positive or negative. Consequently, we have enhanced the accuracy of sentiment analysis utilising the classifier ensemble approach. In order to achieve high accuracy, we also make use of our method like word sense disambiguation and negation handling. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Achieving Seamless Semantic Interoperability and Enhancing Text Embedding in Healthcare IoT: A Deep Learning Approach with Survey

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SN Computer science 2024年第1期5卷 99页

作者： Purushothaman, Sony Shanmugam, G. Siva Nagarajan, Sureshkumar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science Govt. Model Engineering College Kerala Kochi India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnakovil India

Achieving semantic interoperability in healthcare is one of the significant challenges in the rapidly expanding healthcare sector. On the other hand, IoT in healthcare can provide patient-centric treatment mechanisms to help resolve these issues. When several healthcare organizations are generating heterogeneous medical information, semantic interoperability becomes essential to develop better treatment plans and better medical research. In this article, we discuss the applications of healthcare IoT, a comparative study of existing solution paradigms for achieving semantic interoperability and its enabling technologies. The comprehensive analysis of semantic interoperability has versatile dimensions to meet this, and it establishes deep learning-based directions. In continuing the background work, we proposed a new deep learning model called MedRelBERT with six embeddings to remove linguistic ambiguities in medical documents. The survey also examines the bibliographic evaluation of Web of science articles discussing semantic interoperability solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Internet of medical things Privacy Semantic interoperability

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Topology-Aware Keypoint Detection via Skeleton-Based Shape Matching

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 367-378页

作者： Li, Yushi Li, Pengfei Xu, Meng Wang, Yunzhe Ji, Chengtao Han, Yu Chen, Rong Xi'an Jiaotong-Liverpool University Department of Intelligent Science China Dalian Maritime University College of Information Science and Technology China Suzhou University of Science and Technology School of Electronic and Information Engineering China Xi'an Jiaotong-Liverpool University Department of Computing China Shenzhen University School of Mathematical Sciences China

3D keypoint detection endeavors to identify well-aligned and semantically consistent elements that reflect object shapes within point clouds, which plays a significant role in wide-ranging applications such as navigation and object tracking based on mobile devices. While existing approaches prioritize either salient features or statistic distributions for alignment, they overlook the underlying spatial topology of shapes. Although some recent methods take potential skeletons into account, they fail to associate this representation with local and global topology, thus reconciling comprehensive coverage and semantic awareness. To address this, we reckon keypoint detection as the skeleton-based shape matching and propose a two-branch framework that explicitly localizes the keypoints with broad coverage and semantic coherence in an unsupervised manner. Specifically, one branch incorporates the keypoint detector with a skeleton generator to infer the coarse skeletons that represent the global topology. Meanwhile, another branch leverages skeletal sphere estimation to generate the skeletal point set that sustains the local structures, serving as the foundation for optimizing the skeletons formed by keypoints. Since these skeletal representations capture both the structural essence and semantic attributes of a shape, our model is capable of extracting semantically rich keypoints with good alignment. We extensively evaluate our method on different datasets to demonstrate its effectiveness and competitiveness in 3D keypoint detection. © 1975-2011 IEEE.

关键词： Musculoskeletal system

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An Empirical Investigation of Docker Sockets for Privilege Escalation and Defensive Strategies 5

An Empirical Investigation of Docker Sockets for Privilege E...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Rajyashree, R. Mathi, Senthilkumar Saravanan, G. Sakthivel, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

Cloud-based infrastructures often leverage virtualization, but its implementation can be expensive. Traditional coding methods can lead to issues when transitioning code from one computing environment to another. In response, the container paradigm emerged to offer cost-effective and agile delivery. Containers differ from full machine virtualization by compactly encapsulating the entire software and its dependencies. Leveraging containers, developers can create more secure and efficient applications. Docker, a prominent containerization platform, facilitates the execution of docker images. The Docker Hub serves as a popular repository for various images. Given the importance of application security, especially in the face of threats like malware, ransomware, and data breaches, ensuring robust security is imperative. The paper investigates vulnerabilities within Docker containers and proposes defensive strategies to mitigate potential breaches. In addition, it investigates attacks involving Docker sockets and suggests preventive measures for non-root users. © 2024 The Authors. Published by Elsevier B.V.

关键词： Containers

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Web Based Application to Recognize Diseases and Providing Consultant to Patients 3

Web Based Application to Recognize Diseases and Providing Co...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ajitha, P. Sathish Kumar, P.J. Navaneethakrishnan, Sundara Rajulu Sivasangari, A. Surendran, R. Sathyabama Institue of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Trichy India Sathyabama Institue of Science and Technology School of Computing Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

One of the most important aspects of civilization is human wellness. The best and most accurate condition analysis seeks to offer the necessary assistance as soon as practical. The clinical research factor necessitates knowledge from other fields, such as computer science and statistics, as this comprehension is frequently difficult. The need to pursue novel approaches places a heavier burden than normal on these fields. The variety of new strategies allows for a comprehensive investigation while avoiding specific elements. As a solution to this issue, a systematic evaluation of human illnesses with system mastering is proposed. This study centers on current techniques for system learning advancement applied to the prognosis of human diseases in the medical field in order to discover exciting trends, make light predictions, and assist in decision-making. This initiative includes a scientific chatbot that uses synthetic intelligence to answer patients' questions. This article explains a one-of-a-kind set of device learning algorithms that are used in healthcare programmes to provide excellent preferred choice assistance. This article is intended to cover a research gap in the development of practical choice aid structures for scientific applications. © 2024 IEEE.

关键词： Clinical research

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Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model 5

Precaution Chatbot for Epilepsy Patients using Natural Langu...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Kasthuri, E. Subbulakshmi, S. Sreedharan, Rajasree Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amritapuri India Division of Nephrology Department of Pediatrics Medical College of Wisconsin MilwaukeeWI United States

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model. © 2024 The Authors. Published by Elsevier B.V.

关键词： Deep neural networks

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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Computer Modeling in engineering & sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th International Conference on Information science and Systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies between Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. The study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. Through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. The core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. The practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. The model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. The paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. This research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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