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International Conference on Mechanical engineering, INCOME 2023

作者： Sunil Kumar, K. Kamalakar, V. Palani, S. Muniamuthu, Sumathy Belinda, Carmel Mary Department of Marine Engineering Faculty of Engineering Sri Venkateswara College of Engineering Pennalur Sriperumbudur602117 India Department of Physics Faculty of Physics Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Department of Mechanical Engineering Professor and Faculty of Engineering Sri Sai Ranganathan Engineering College REC kalvi Nagar Thondamuthur Coimbatore641109 India Department of Mechanical Engineering Faculty of Engineering Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi600062 India Department of Computer Science and Engineering Faculty of Engineering Saveetha School of Engineering SIMATS Chennai600025 India

ISBN: (纸本)9789819753727

The aim of this research is to do a CFd investigation on the K-type propeller for enumerating the various performance factors. This paper focuses on the design of rotor blades with the 3d pro-E software and analyzes a K-type rotor in computational fluid dynamics (CFd) analysis to predict the performance parameters such as pressure coefficients, magnitude vector capability, axial velocities, radial velocities, static pressures, and dynamic pressures of the propeller subjected to different loads, and its effect of pitch, rake, and skew formations of the analysis with aluminum material coated with bronze optimized by the K-type inferior design is implemented in this analysis. However, this study proves that aluminum material possesses better exposure to the replacement of conventional rotors to enumerate the dynamic performance. The performance results obtained from the CFd analysis show that the implementation of aluminum bronze with a K-type design gives maximum propulsive efficiency of up to 85% and is nearly 12% better than conventional rotors in terms of noise and cavitation formations. The study also revealed that there is an improvement in thrust coefficient by 5%, torque coefficient by 7%, and improvement in efficiency by 10% as the implementation of K-cap propeller in the field of the marine industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Cavitation

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Analysis of Gait data Based on deep learning to Predict Multiple Balance Scale Scores

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IEEE Sensors Journal 2024年第1期24卷 920-930页

作者： lin, Chueh-Ho Peng, Chih-Wei lee, I-Jung Wu, Chi-Ming lin, Bor-Shing Taipei Medical University International Ph.D. Program in Gerontology and Long-Term Care Taipei City110 Taiwan Taipei Medical University Research Center in Nursing Clinical Practice Wan Fang Hospital Taipei City110 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering Taipei City110 Taiwan Taipei Medical University School of Gerontology and Long-Term Care College of Nursing Taipei City110 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237 Taiwan National Taipei University College of Electrical Engineering and Computer Science New Taipei City237 Taiwan

Unbalanced walking is increasingly common among older adults;therefore, routinely assessing the balance of older adults is crucial. The traditional method of assessing balance uses scales, requires the supervision of a physical therapist (PT), and is time-consuming. The present study, therefore, proposes a deep learning (dl) model architecture that combines a convolutional neural network (CNN) and a long short-term memory (lSTM) network model to predict the scores of three scales, namely the Berg balance scale (BBS), timed up and go (TUG), and single-leg stance (SlS). The gait data of 15 m of walking were collected from seven inertial measurement units (IMUs) and input into the CNN-lSTM model for evaluation. The BBS and TUG only require the participants to wear two IMUs on the left and right thighs, respectively, for accurate predictions. The mean absolute errors (MAEs) of predicting the scores of the BBS and TUG are 1.2562 and 1.4016 s, respectively;however, the MAE of score predictions of SlS is higher than that of the BBS and TUG, indicating that gait data cannot be used for assessing SlS;moreover, participants only wear one IMU on the right calf for BBS and TUG evaluations, which yield MAEs of 1.4334 and 1.5229 s, respectively. The proposed system can quickly and accurately predict the scores of the BBS and TUG. The proposed model can assist PTs with making clinical decisions. © 2001-2012 IEEE.

关键词： long short-term memory

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Recognition of Sleep Apnea and Hypopnea Events from Single Channel Respiration Signal for Healthcare Application

Recognition of Sleep Apnea and Hypopnea Events from Single C...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Nayaka S K, Parikshith Shetty J, Shruthi Siva Kumar Reddy, A. Petli, Vishwanath divya, G. Gitam School of Technology Gitam University Department of Computer Science & Engineering India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India H. K. E. Society's S. L. N. College of Engineering Department of Electronics and Communication Engineering Raichur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

In recent years, sleep apnea has emerged as a major contributor to the epidemic of sleep disturbances that has emerged in modern society. due to the high price, unpleasant experience, and lack of available sleep clinics associated with sleep apnea diagnostics, there is a need for home-based health monitoring systems that are both affordable and convenient. Recent years have seen a lot of research into methods for detecting apnea and hypopnea, but less attention has been paid to classifying the many types of events. Since obstructive and central apnea require different treatment strategies, identifying the specific type of apnea is just as crucial as spotting the condition itself. The work makes use of both an oronasal airflow signal and an abdominal effort signal. The intricate link between these two signals throughout various stages of apnea has been studied using a simulated lung airflow model. This idea has been refined in clinical trials to include the ability to identify the nature and timing of events in a near-real-time setting. The average accuracy of detecting hypopnea and apnea during online tests was 92.4% and 95.3%, respectively, which is pretty satisfactory when equated with Polysomnography (PSG) based related directories on undetected participants. © 2023 IEEE.

关键词： Sleep research

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NMF based Speech Enhancement using CNN 15

NMF based Speech Enhancement using CNN

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Jessintha, d. Palas, P. Bini Berakhah, F. Stanley Kumar, R. Jeen Retna Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331300579

Convolutional Neural Networks (CNNs), are adept at learning complex patterns in data and can effectively denoise speech signals by mapping noisy spectrograms to clean ones. NMF, on the other hand, is a signal processing technique that can decompose a spectrogram into additive components, including a noise profile. By estimating the noise profile from the noisy speech spectrogram, NMF enables the separation of noise from the desired signal. Combining deep learning with NMF leverages the strengths of both approaches, allowing for robust and efficient speech enhancement even in challenging noisy environments. First, noisy and clean speech samples are loaded and pre-processed, including trimming or padding to a fixed length and computing magnitude spectrograms. A CNN-based deep learning model is then defined and compiled, with two convolutional layers for feature extraction from input spectrograms followed by dropout layers for regularization. This model is trained using the noisy spectrograms as input and their corresponding clean spectrograms as target output. Meanwhile, NMF is applied to estimate the noise profile from the magnitude spectrogram of the noisy speech signal, separating noise from the signal. Finally, the trained model is used to denoise the spectrogram, and the enhanced audio is reconstructed and saved. This approach combines the strengths of CNNs for learning complex mappings and NMF for noise estimation, resulting in improved speech enhancement performance. © Grenze Scientific Society, 2024.

关键词： Non-negative matrix factorization

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Machine learning Algorithms for detecting Fake Reviews - A Study 3

Machine Learning Algorithms for Detecting Fake Reviews - A S...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Shanmugha Priya, R.K. Murugeswari, K. Biju, J. libin Jacob, P. Anbarasan, R. Santhosh Krishna, B.V. Jai Shriram Engineering College Computer Science and Engineering Tirupur638 660 India Velammal Institute of Technology Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Information Science and Engineering TamilnaduSathyamangalam Erode India Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Vel Tech India KGiSL Institute of Technology Information Technology Coimbatore India New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350343274

Fake reviews are very popular today as every internet user is depending on the reviews to draw a conclusion on a particular product or organization or hotels in their respective domains. Fake reviews are the one and only thing that is affecting the present world businesses and all other things. As development of web 2.0 it's seen that customers rely more on product ratings and reviews given by customers for product information. Usually, a more positive review attracts more customers and with intern is responsible for the growth of that product. Nowadays people take advantage of this fact and give false reviews in order to mislead the customers affecting company, market efficiency and merchants. The issue which plays the major role in the field of fake review detection is that the fake review groups are adapting to the present users' expectations and creating the fake reviews which are not able to be detected by the humans. So, we are trying to solve it using some technologies like machine learning to 'Fight the machines with machines' to detect the fake reviews using these technologies. And there are many methods in which these fake reviews can be detects and taken care of using many types of machine learning methods. This article aims to achieve understanding of fake reviews detection in various domains like e-commerce and hotel websites etc. And we also work to identify the relation between the fake review and the reason behind creating them. By performing a systematic literature review we can identify and understand about the main approaches that have been previously studied and made accessible to recognize fake reviews and their applications in different situations. And also, absence of high- quality datasets limit the research of fake reviews. We have also mentioned existing research problems in data acquisition and identifying method design to help in future research on fake review detection. © 2023 IEEE.

关键词： Sales

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An Efficient Inter-Cluster Wireless Sensor Network Communication by using Priority Queues and Reliability Models

An Efficient Inter-Cluster Wireless Sensor Network Communica...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： devi, Renuka Patel, Suresh Shivaprasad Yadav, S.G. Shivakanth, Gandla divya, G. H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bangalore India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Even though Electromagnetic and Radio Interference, Reliability, Cyber Security, and Resource depletion are still concerns with Wireless Sensor Networks (WSNs), even though WSNs have become increasingly widespread and are used in a wide variety of contexts. It is possible to detect and transmit a wide variety of data using a wide variety of sensors, including electric sensors, magnetic field sensors, infrared sensors, laser sensors, navigation sensors, and many others. The requirement for increased spatial and temporal precision in all applications has led to a decrease in the price of sensors as well as a reduction in their size and weight, respectively. loss of packets and interference are extremely common in the real world because of the complexity and noise present in that environment. These sensors are extremely important for sensing and reporting data on a regular basis, in response to specific inquiries, or based on events that have occurred. In this investigation, the information that has been gathered by the leader nodes needs to be transmitted to the target node in the most effective manner possible through multiple hops. With the assistance of a number of different leader nodes located in their own distinct clusters, the data is moved to its ultimate location. Utilizing the appropriate strategy of data acknowledgments and queue thresholds is required in order to both ensure the data's integrity and reduce the amount of unneeded management overhead. Congestion limitations that are determined by wait levels contribute to helping assure dependability. © 2023 IEEE.

关键词： Radio interference

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Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Workplace Applications 3

Unveiling the Future: Exploring the Impacts and Challenges o...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Pragith, S. Supriya, S. Sasikala, S. Sateesh, Rayala Swamy, Hemanth Karpagam, P. Vellore Institute of Technology Department of Computer Science Engineering Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering India Tagore Engineering College Department of ECE Chennai India MLR Institute of Technology Department of ECE Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of Cyber Security Chennai India

ISBN: (纸本)9798350354171

Unveiling the Future: Exploring the Impacts and Challenges of Artificial Intelligence in Society' embarks on a comprehensive journey through the transformative landscape shaped by Artificial Intelligence (AI). This abstract offers a nuanced examination of AI's profound implications, ranging from its disruptive potential to its ethical complexities, and the societal imperatives it underscores. delving into the evolution of AI technologies, from foundational concepts to cutting-edge innovations, the abstract elucidates their growing significance across diverse sectors including healthcare, finance, transportation, and beyond. However, amidst the promises of efficiency and innovation, ethical concerns loom large. The abstract navigates through the ethical intricacies of AI, addressing issues of bias, accountability, and the erosion of privacy. Furthermore, it ventures into the societal ramifications of AI adoption, exploring its impact on employment dynamics, socioeconomic disparities, and the fabric of human interaction. By interrogating the dual narratives of promise and peril, this abstract offers a holistic understanding of AI's role in shaping the future of society. It underscores the imperative for thoughtful governance, ethical stewardship, and inclusive decision-making to ensure that AI serves as a force for collective progress rather than exacerbating existing divides. Through its interdisciplinary lens, this abstract serves as a roadmap for stakeholders across academia, industry, and policymaking to navigate the complex terrain of AI's integration into society, ultimately paving the way for a future where AI augments human capabilities while upholding fundamental values of fairness, equity, and justice. © 2024 IEEE.

关键词： decision making

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A Review of Recent Advancements in Infant Brain MRI Segmentation Using deep learning Approaches 7th

A Review of Recent Advancements in Infant Brain MRI Segmenta...

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ahir, Param Parikh, Mehul Gujarat Technological University Gujarat Ahmedabad382424 India L.D. College of Engineering Gujarat Ahmedabad380015 India

ISBN: (纸本)9789819907687

In this paper, a critical analysis of recent trends and techniques for tissue segmentation of an pediatric brain Magnetic Resonance Imaging (MRI) is performed. A significant amount of research has recently been conducted in the field of medical image analysis. In medical image analysis, MRI is regarded as one of the primary imaging modalities. MRI is capable of providing a clear view of various organs in depth. Tissue segmentation is an important process in brain research, especially when the subjects are infants and defining normalcy is difficult. Our paper focuses on recent developments in this field. deep learning technological advancements are assisting this field in moving forward with dataset availability via challenges such as iseg-2017 and iseg-2019. This paper compares methodology, dataset, and results obtained by various methods. The paper also discusses the limitations of all available approaches as well as possible future directions in the field. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Magnetic resonance imaging

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Enhancing Agricultural Productivity Through Convolutional Neural Networks Based disease detection 2

Enhancing Agricultural Productivity Through Convolutional Ne...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Sathiya Priya, S. Kujani, T. Gnana Prasanna, S. Ramya Sree, K.M. Hemasri, Bachina Hussain, Shaik Khadar Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai600062 India Department of Computer Science and Engineering Aalim Muhammad Salegh College of Engineering AvadiIAF Chennai India

ISBN: (纸本)9798331541217

Plant disease detection is a cornerstone of sustainable agriculture, directly influencing crop yield and quality. Early detection and timely intervention are crucial for mitigating these impacts. This study investigates the potential of Convolutional Neural Networks (CNNs) to automate and enhance crop disease detection. CNNs, capable of learning intricate spatial patterns from image data, provide a promising alternative to traditional disease diagnosis methods. The paper delves into the technical aspects of CNN-based plant disease detection, including popular frameworks like TensorFlow and PyTorch. These tools provide a user-friendly environment for building, training, and deploying CNN models, streamlining the process of implementing AI solutions in agricultural settings. The advantages of CNNs for plant disease detection are multifaceted. Their hierarchical architecture enables automatic feature extraction, reducing the need for manual engineering. Additionally, CNNs are robust to variations in image conditions, making them suitable for real-world agricultural applications. To further improve performance, various techniques are integrated into CNN-based systems. data expansion and model adaptation methos are utilized to expand the variety and size of training datasets, resulting in more precise and generalized models. In conclusion, CNNs offer a promising approach to automating and improving plant disease detection, contributing to sustainable agriculture and food security. By harnessing the capabilities of AI, farmers and researchers can make informed decisions to safeguard crops and maximize yields. These techniques increase the diversity and size of training datasets, leading to more accurate and generalized models, with studies reporting achievements of up to 97% accuracy. The potential integration of CNN-based systems into precision agriculture frameworks to optimize resource allocation and enhance overall crop health. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancements in Environmental data Analysis for Climate-Resilient Agriculture Using Remote Sensing and deep learning

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 670-680页

作者： Nirmal, Khyati R. Reddy, Chilukala Mahender P, Jose Tulasi, Rajesh Maheswari, G. Uma Sapthami, I. SNJB’S Late Sau KBJ CoE Maharashtra Nashik India Department of Artificial Intelligence and Data Science GITAM Deemed to Be University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai Avadi India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P Vaddeswaram Guntur Dist. India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Pudhuvoyal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change deep learning model Remote sensing Smart agriculture Stacked encoder model

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