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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arul Prasath, A. Saravanan, S. Monikapreethi, S.K. Brindha, G. Maheshwari, S. Srinivasan, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Information Technology Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

This research introduces a new method for improving Intensive Care Units (ICUs) nurse assignment using cloud computing and Decision Trees (DTs )-based patient acuity prediction models. Effective and timely care delivery is dependent on the efficient allocation of nursing resources, which is becoming more important due to the growing complexity of patients' situations in ICUs. To integrate patient data from many sources seamlessly the proposed system uses cloud infrastructure to process and analyze data in real-time. To proactively identify high-risk situations, use DTs algorithms to forecast patient acuity levels using a variety of clinical indicators. Optimizing staffing levels and improving patient outcomes which incorporates these predictions into the nurse assignment process. Through a case study performed in a real-world ICUs improves nurse efficiency and the quality of patient care. The findings of this study highlight the promise of decision support systems hosted in the cloud for improving the delivery of healthcare in ICUs. © 2024 IEEE.

关键词： Nursing

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A Constructive Role for Social science in the Development of Automated Vehicles Based on LFM-BiGRU Approach 3

A Constructive Role for Social Science in the Development of...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Srikanth, J. Nitheesha, N. Khetree, Surekha Josephson, P Joel Akila, D. Divya, V. Kommuri Pratap Reddy Institute of Technology Computer Science and Engineering Telangana Ghatkesar India Telangana Hyderabad India Bharati Vidyapeeth College of Engineering Department of Mechanical Engineering Navi Mumbai India Department of Ece Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Applications Tamilnadu Chennai India Department of Information Technology Chennai India

ISBN: (纸本)9798350381931

This proposed system delves into the transformative realm of autonomous vehicles, a technological marvel that has captivated society's anticipation for its profound societal implications. While existing social science research predominantly focuses on regulatory, safety, and efficiency considerations, this method contends that these discussions fall short in comprehensively appreciating the extensive social ramifications of autonomous vehicles. Addressing this gap, the system aims to provide a more nuanced understanding of the broader societal impacts that autonomous vehicles are poised to instigate. The proposed methodology comprises three integral modules: preprocessing, feature extraction, and model training. Preprocessing involves essential tasks such as punctuation removal, URL elimination, and batch normalization. The feature extraction stage leverages TF-IDF, contributing to the discernment of significant features. For model training, the paper employs the LFM-BiGRU architecture, a distinctive aspect of the proposed method. Comparative analysis against conventional approaches, namely BiGRU-LSTM and BiLSTM, underscores the superior performance exhibited by the proposed method. This suggests that the LFM-BiGRU approach holds promise for advancing the capabilities of autonomous vehicle research and, consequently, societal understanding of this groundbreaking technology. © 2024 IEEE.

关键词： Autonomous vehicles

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An empirical IoT and cloud-based customizable healthcare surveillance system

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International Journal of information Technology (Singapore) 2024年第8期16卷 5317-5323页

作者： Meti, Subhash Razauddin, S. Nallakumar, R. Mansingh, P. M. Benson Sameen, Aws Zuhair Pandey, Sakshi Bhatt, Sanjeev Kumar Jayabalan, Bhuvana Department of Electronics and Communication Engineering SKSVMACET Lakshmeshwar Karnataka Gadag India College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India College of Medical Techniques Al-Farahidi University Baghdad Iraq Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Software Architect and Research Scholar Digital Innovation College PDM University Bahadurgarh India Department of Computer Science and Information Technology JAIN (Deemed-to-Be University) Karnataka Bengaluru India

In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the study in question presents an empirical Internet of Things and cloud-based, customizable healthcare monitoring system. Initially, a cloud-based algorithm called particle swarm intelligence (PGI) is used in the proposed healthcare monitoring system to group recorded data, hence increasing the prediction rate. Subsequently, features are selected and extracted, and back propagation neural networks (BPNN) are employed to classify healthcare for humans. In the end, the effectiveness of the suggested system is evaluated using a healthcare dataset and contrasted with current deep learning/machine learning classifiers. The suggested surveillance system performs better than the methods employed in the previous research, according to the findings. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： BPNN Cloud computing Healthcare surveillance IoT Particle swarm intelligence (PSI)

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Cloud-Based CNN Models for Automated Pulmonary Disease Diagnosis and Risk Assessment 1

Cloud-Based CNN Models for Automated Pulmonary Disease Diagn...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Sampoornam, M. Maria Gurulakshmanan, Gurumoorthi Sekar, Satheeshkumar Hasan, Md Ashfaqul Geetha, T. Srinivasan, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Mphasis Corporation TX United States Mphasis Corporation ChandlerAZ United States Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

This research introduces a novel method for automated pulmonary diseases detection and risk assessment using convolutional neural network (CNN) models hosted in the cloud. Pulmonary diseases are on the rise, and with them come the need for better diagnostic tools. Combining state-of-the-art machine learning methods with cloud computing infrastructure might lead to accessible and scalable healthcare solutions. The proposed CNN architecture is designed to efficiently extract features from chest radiographs, allowing for more precise illness classification and risk stratification. Using open-source data and cloud computing, the model is trained and tested on a wide variety of lung diseases and disorders, such as chronic obstructive pulmonary disease (COPD), pneumonia, and TB. Experiments show that the technique outperforms conventional diagnostics, with excellent sensitivity and accuracy across a variety of illness types. Optimizing diagnostic efficiency and enabling rapid treatments are made possible with cloud-based deployment, which also allows for real-time analysis and easy connection with current healthcare systems. This method might help with healthcare spending, patient outcomes, and the ability to provide healthcare remotely in areas with limited resources. It highlights might revolutionize automated lung disease detection and risk assessment, which in turn could improve customized medication and the management of population health. © 2024 IEEE.

关键词： Lung cancer

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Multicast Routing Technique for Augmenting Routing Efficiency in Mobile WSN 7

Multicast Routing Technique for Augmenting Routing Efficienc...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Anand, K. Rajkumar, M. Srikanth, M.S. Radhika, S. Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Chennai India School of Information Technology and Engineering VIT University Tamil Nadu Vellore India TNS India Foundation Karnataka Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile Wireless Sensor Network (MWSN) is an active network completely self-governing from a fixed structure. Also, MWSN has small channel bandwidth, great node movement, and inadequate battery energy. In WSN, flooding raises the reliability regarding successful communication of a packet with greater overhead. The flooding consumes the network's resources quickly, especially in terms of the lifetime of the node and energy, etc. Unicast routing creates an extra overhead and increases energy consumption because unicast routing mainly focuses on only minimum distance. But, multicasting is used when the desired destination is at maximum distance and increases the network's reliability in terms of throughput. This paper's objectives are to develop an efficient and reliable protocol using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Multicast Routing Technique for Augmenting (MRTA) routing efficiency in MWSN is introduced. This method aims to enhance the link stability of the route for raising the route lifetime and diminishing the routing load. This method discovers the stable path from several ways to decline the overhead and delay between the sender and receiver. Simulation results demonstrate that this approach improves the network lifetime increases the throughput, and minimizes the routing load in the MWSN. © 2023 IEEE.

关键词： Multicasting

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Automatic Gait Classification using Arm Acceleration based on Stature and Gait Speed 4

Automatic Gait Classification using Arm Acceleration based o...

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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Kitagawa, Kodai Wada, Chikamune Toya, Nobuyuki Kyushu Institute of Technology Graduate School of Life Science and Systems Engineering Kitakyushu Japan Hokkaido Information University Department of Medical Management and Informatics Ebetsu Japan

ISBN: (纸本)9781665419048

Unsuitable foot clearance and step length lead falling accidents;thus, these gait parameters should be monitored for fall prevention. According to previous study, users prefer wrist as a position to wear wearable device for daily gait monitoring. Therefore, we have been developing gait monitoring method using wearable device on a wrist. Our previous method classifies gaits with different foot clearance or step length by machine learning technique using 3-axis acceleration data obtained from wearable device on a wrist. The results of our previous study showed that this method could classify gaits for each step. However, accuracy of our previous method was insufficient because individual difference of arm acceleration due to stature or gait speed were not considered. The objective of this study is to propose the gait classification method based on individual difference. The proposed method classifies three gaits with different foot clearance and step length by machine learning technique using combination of arm acceleration, stature, and gait speed. The proposed method was tested for 1800 steps performed from ten participants. The proposed method could classify three gaits with accuracy greater than 0.9. These results indicate possibility that the proposed method can be used for gait monitoring using wearable device. © 2022 IEEE.

关键词： Acceleration

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An Analysis of Signature-Based Components in Hybrid Intrusion Detection Systems

An Analysis of Signature-Based Components in Hybrid Intrusio...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Agoramoorthy, Moorthy Ali, Ahamed Sujatha, D. Michael Raj, T.F. Ramesh, G. Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India Simats Department of Computer Applications Sclas Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai - Tamilnadu Pottapalayam India

ISBN: (纸本)9798350394580

In cybersecurity, Intrusion Detection Systems (IDS) protect against emerging cyber threats. Combining signature-based and anomaly-based detection methods may improve IDS accuracy and reduce false positives. This research analyzes hybrid intrusion detection systems' signature-based components' performance and limitations. The paper begins with a detailed history of signature-based detection methods responding to changing threat situations. This research analyzes signature databases to determine their capacity to identify and guard against current threats and cover known vulnerabilities. The paper also examines the intricate relationship between signature-based detection and anomaly-based techniques in hybrid IDS systems. This investigation examines how these two methodologies work together to uncover old and new attack strategies, focusing on zero-day vulnerabilities and polymorphic malware. A diverse dataset of network traffic and attack scenarios is used to test. Detection, false positives, and response times assess signature-based components. Comparative examinations investigate how signature-based detection affects system accuracy and efficiency. This research illuminates the role of signature-based aspects in hybrid intrusion detection systems. This study recommends integrating signature-based detection techniques with anomaly-based methods to improve hybrid intrusion detection systems (IDS) at recognizing and mitigating various cyber threats. © 2023 IEEE.

关键词： Anomaly-Based Detection Cybersecurity False Positives Hybrid IDS Intrusion Detection Systems Network Security Polymorphic Malware Signature-Based Detection Threat Detection Zero-Day Exploits

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A Bibliometric Analysis and Research Landscape of Artificial Intelligence in Education 14

A Bibliometric Analysis and Research Landscape of Artificial...

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14th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2024

作者： Ajibade, Samuel-Soma M. Issa, Bayan Jasser, Muhammed Basheer Hassan, Farrukh Aldharhani, Ghassan Saleh Al-Hadi, Ismail Ahmed Al-Qasem Akintoye, Kayode Akinlekan Tabuena, Almighty Cortezo School of Engineering and Technology Sunway University Department of Computing and Information Systems No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey University of Aleppo Faculty of Informatics Engineering Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia UKM University Faculty of Information Science and Technology Selangor Bangi Malaysia The Federal Polytechnic Department of Computer Science Ado Ekiti Ekiti State Nigeria University of the City of Valenzuela College of Arts and Sciences Philippines

ISBN: (纸本)9798350364507

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or 64.5% of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or 6.44% of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students' learning patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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Quantum-Inspired Neural Network For Enhanced Image Recognition

Quantum-Inspired Neural Network For Enhanced Image Recogniti...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Michael, Raj. T.F. Rmesh, G. Agoramoorthy, Moorthy Ahamed Ali, S. Sujatha, D. Sclas Simats Department of Computer Applications Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India

ISBN: (纸本)9798350394580

The field of quantum computing has seen significant progress in recent times, leading to the emergence of innovative methodologies across several areas, particularly in machine learning and neural networks. This research study introduces a quantum-inspired neural network (QINN) that has been particularly developed to improve image recognition capabilities. In contrast to conventional neural networks, the Quantum-inspired Neural Network (QINN) utilizes concepts derived from quantum mechanics, including superposition and entanglement, to process and encode information effectively. The paradigm we propose incorporates quantum bits (qubits) as computing units instead of traditional binary nodes, simultaneously enabling the representation of many states. Moreover, the use of the intrinsic parallelism of quantum mechanics is harnessed to provide expedited and more effective data processing, particularly within the concealed levels of the network. The researchers ran benchmark tests using well-recognized image datasets, including CIFAR-10 and ImageNet. The study's findings indicated that QINN displayed superior accuracy rates compared to traditional methods and revealed faster convergence throughout the training process. Additionally, our quantum-inspired methodology demonstrated significant efficacy in analyzing pictures containing nuanced characteristics, highlighting its potential in practical scenarios where precise identification is of utmost importance. In summary, integrating quantum principles with neural network topologies has the potential for groundbreaking advancement in image recognition skills. QINN is a groundbreaking advancement in this domain, emphasizing the unexplored capacity to use quantum phenomena for sophisticated machine learning objectives. Potential areas for future study are the expansion of network scalability to accommodate bigger datasets, exploration of security measures that are resilient against quantum threats, and investigating hybrid mo

关键词： image recognition neural network Quantum-inspired Qubits

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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