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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Akshara, C.V. Aakash, S. Padma Suresh, L. Usha Nandini, D. Paul Selvan, Mercy Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The smart helmet is a kind of protection gear that improves the rider's safety while operating a bike. The primary motive is to keep the motorcyclist safe. The attributes including alcohol detection, global positioning system, accelerometer, and fall detection can be employed to achieve this. As a consequence, it acts as a smart helmet. The first condition needs to be fulfilled for the bike to ignite, is that the person should have not consumed alcohol, followed by that the eye blink sensor detects the motion of eyeballs and if the driver falls is tired and closes eye for a period of time, then the sensor alerts the driver and when the accelerometer detects the change in motion, it alerts the GPS and GSM and it posts message to their nearby hospital and relatives. The advancement in our projects includes the temperature sensor which alerts during long hours of wearing helmet. Smart helmets can also help prevent head injuries. Some smart helmets can provide real-time information about the force, frequency and severity of head impacts. © 2024 IEEE.

关键词： Global positioning system

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2024年第17期84卷 1-27页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 2024 IEEE.

关键词： Convolutional neural networks

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Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

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computer Systems science & engineering 2022年第2期40卷 735-747页

作者： K.Sangeetha D.Prabha Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning *** this,most research uses sentiment analysis to track students’*** sentence-level sentiment analysis focuses on the whole sentence *** studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a *** is a need to extract the targets in a given sentence which helps to find the sentiment towards those *** extraction is the subtask of targeted sentiment *** this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this *** evaluate the model using the Michigan dataset and we build a dataset for target extraction from student *** experimental results show that our proposed fusion model achieves better results compared to baseline models.

关键词： Feedback sentimental analysis deeplearning integrated CRF

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LKLM: A Large-Kernel Lightweight CNN Model for Monocular Depth Estimation 4

LKLM: A Large-Kernel Lightweight CNN Model for Monocular Dep...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Dang, Yuanyuan Zhang, Xianhe Liu, Bing Zhong, Zhaohao College of Computer Science and Engineering Changchun University of Technology Changchun China College of Computer Science and Engineering Changchun University of Technology College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798350326444

Depth estimation is a fundamental task in computer vision, which is increasingly widely applied in autonomous driving. However, many existing methods have often resulted in large computational overhead and high latency. To tackle this issue, we propose a lightweight CNN architecture named Large-Kernel Lightweight CNN Model (LKLM). Extensive experiments demonstrate the superiority of our approach in lightweight depth estimation. On the KITTI dataset, LKLM achieves a large margin in accuracy, with 4.254 GFlops. © 2023 IEEE.

关键词： Large dataset

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Assessment and Evaluation of Music Genre Classification by employing various AI Techniques 7

Assessment and Evaluation of Music Genre Classification by e...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Adithya, S. Aijith Prabhakar, U.T. Ankayarkanni, B. Anto Praveena, M.D. Padma Suresh, L. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Music genre classification, the task of automatically assigning audio recordings to specific genres, offers exciting possibilities for music recommendation systems, personalized playlists, and music analysis. This abstract explores the potential of (K-NN), a versatile Artificial Intelligence (AI) algorithm, in tackling this challenge- NN excels in its simplicity and interpretability. It classifies new music by identifying its closest neighbors within a labeled dataset based on predefined features, such as tempo, timbre, or spectral content. These neighbors then collectively "vote"on the genre of the new piece. The abstract delves into the feature engineering process, highlighting the crucial role of selecting relevant musical characteristics for accurate classification. Furthermore, the abstract discusses the impact of the "K"hyper parameter, which determines the number of neighbors considered in the voting process. Choosing the optimal "K"value balances accuracy and sensitivity to genre nuances. While K-NN offers advantages in ease of implementation and understanding, the abstract acknowledges its potential drawbacks. The storage requirements can be high for large datasets, and computational costs may increase with data size. It concludes by acknowledging the need for further research to address computational challenges and explore advanced feature engineering techniques to unlock the full potential of K-NN in this exciting domain. © 2024 IEEE.

关键词： Audio recordings

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Analysis and Performance Evaluation of various Artificial Intelligence techniques for Disease Prediction 7

Analysis and Performance Evaluation of various Artificial In...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Refonaa, J. Jany Shabu, S.L. Rishi, S. Padma Suresh, L. Praveen, S. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

The framework of the proposed system utilizes AI, machine learning procedures and data handling methodologies to analyze, gather data and reports from clinics or patient facilities, and prognosticate reports that assist the outcomes used to foresee various kinds of infection and disease. The most recurrently occurring disease and its infection is the dominant source for the human's death. Clinical endlessly reports from different documents have been taken out to analyze and diagnose breast malignant growth or cancer, coronary or heart illness, and diabetes which are very frequent in present day people, and their wellbeing is significant in the finding. Today, propels in clinical innovation are assisting individuals with improving on the manner in which they give medical clinics and doctors' the most straightforward method for treating, oblige, and treat any infection they need. We have brought this sort of framework into our application, which can give positive and negative outcomes to make individuals' lives simpler to anticipate the sickness by bringing explicit data into their reports, and backing the theory of the infection with the choice of looking for exhortation good clinics and great specialists close to clients or guardians in our future enactments. © 2024 IEEE.

关键词： Logistic regression

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Human Detection in Low-Resolution Depth Images 2

Human Detection in Low-Resolution Depth Images

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2nd IEEE International Conference on Electrical, Automation and computer engineering, ICEACE 2024

作者： Huang, Ling Luo, Huan Mo, Jiwei Guo, Xiaoyu Lu, Qiu Li, Shuiwang Guilin University of Technology College of Computer Science and Engineering Guilin China Sichuan University College of Computer Science Chengdu China

ISBN: (纸本)9798350368208

Human detection is essential in areas like security surveillance, healthcare, and behavior analysis. While traditional RGB-based methods are effective, they raise significant privacy concerns by exposing personal identities. Depth images, in contrast, provide a privacy-preserving alternative but are often hindered by the limited availability of low-resolution datasets. Despite their potential, human detection in low-resolution depth images remains an underexplored area, primarily due to the challenges posed by poor spatial resolution and insufficient depth data. These images often lack sufficient detail, leading to blurred human contours and making detection more difficult. To address this gap, we design and construct a novel low-resolution depth human detection dataset, offering a rich resource of labeled data. To tackle the issue of insufficient detail in low-resolution depth images, we introduce a spatial perception network module that captures and leverages spatial relationships between regions. This module significantly enhances detection accuracy by improving the model's understanding of spatial context. Our proposed method offers a promising solution for real-world applications where privacy and low-resolution data are key considerations. © 2024 IEEE.

关键词： Differential privacy

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Machine Learning Based Alzheimer’s Disease Detection: A Comprehensive Approach 7th

Machine Learning Based Alzheimer’s Disease Detection: A Com...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Periya Nayaki, A. Vidyabharathi, A.K. Krishnaveni, S. Thanabal, M.S. Department of Information Technology Velammal College of Engineering and Technology Madurai India Computer Science and Engineering PSNA College of Engineering and Technology Madurai India

ISBN: (纸本)9783031699818

Alzheimer’s disease typically leads in the death of neurons and the connections between them in memory-related parts of the brain, such as the entorhinal cortex and hippocampus. Following that, it affects the areas of the cerebral cortex responsible for language, cognition, and social interaction. Several other regions of the brain get harmed at some time. Patients with Alzheimer’s disease gradually lose their capacity to function normally and go about their daily lives. Currently, age is regarded as one of the most important risk factors;the majority of patients with dementia are 65 or older. Symptoms of the condition include increased disorientation, memory loss, including the inability to recall recent events or personal history, and learning difficulties. As of yet, there is no treatment for Alzheimer’s. A vast quantity of data will be acquired from the previously affected person, and the user input data will be compared to past data using machine learning techniques to determine whether they are still impacted. Finding the best categorization approach is difficult because it necessitates adapting to regional datasets. The methods LightGBM, AdaBoost, Random Forest Classifier, Support-Vector-Machine, Decision-Tree, and XGBoost provided accuracy rates of 78%, 86%, 81%, 73%, and 84%, respectively. In this work, the Voting Classifier algorithm, which may be more accurate than other methods, will be employed. © IFIP International Federation for Information Processing 2024.

关键词： Decision trees

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