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检索条件"机构=Be in Computer Engineering L D College of Engineering"

共 8955 条记录，以下是411-420 订阅

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IoT for Emerging engineering Application Related to Commercial System 3rd

IoT for Emerging Engineering Application Related to Commerci...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Veeraiah, Vivek Ahamad, Shahanawaj Jain, Vipin Anand, Rohit Sindhwani, Nidhi Gupta, Ankur Department of R & D Computer Science Adichunchanagiri University Karnataka Mandya India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Teerthanker Mahaveer University Uttar Pradesh Moradabad India New Delhi India AIIT Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

ISBN: (纸本)9789819986453

Present paper is focused on IOT for emerging engineering application related to commercial system. Internet of things has been considered with customer sentiment analysis in order to detect the sentiment of consumer so that relevant action could be taken considering consumer taste. lSTM model has been used for classification of sentiment. The objective of work is to consider lSTM model for classification and prediction of customer sentiment for product. dataset of Amazon consumer feedback has been considered to take decision of IoT based commercial application with support of lSTM based deep learning mechanism. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Sentiment analysis

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Enhancing Weed Recognition in Cotton Fields Through Advanced Imaging and learning Techniques 5th

Enhancing Weed Recognition in Cotton Fields Through Advanced...

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5th International Conference on Recent Trends in Machine learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Singh, Jagendra Shaik, Nazeer Sahu, dinesh Prasad Tiwari, Mohit Haque, Mustafizul Upreti, Kamal School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Dr. D. Y. Patil Vidyapeeth’s Centre for Online Learning Dr. D. Y. Patil Vidyapeeth Pune India Ghaziabad India

ISBN: (纸本)9789819788606

This research investigates the efficacy of weed recognition models in cotton fields through advanced imaging and machine learning techniques. Utilizing 10 trials, the models, namely K-NN and GBM, were evaluated across multiple performance metrics. Results reveal that GBM consistently outperformed K-NN in accuracy, precision, recall, and F1 score, with average values of 0.88, 0.89, 0.86, and 0.88, respectively, compared to K-NN's averages of 0.85, 0.87, 0.82, and 0.85. Moreover, GBM exhibited higher AUC values (0.94) than K-NN (0.92) in ROC curve analysis, indicating superior discrimination ability. Additionally, k-fold cross-validation demonstrated GBM's higher mean accuracy (0.89) and F1 score (0.88) compared to K-NN (mean accuracy: 0.86, mean F1 score: 0.85). Additionally, integrating temporal data analysis could improve the models’ ability to detect weed growth patterns over time. Real-time monitoring capabilities and automated decision-making systems could streamline weed management practices in agricultural settings. Furthermore, expanding the study to encompass diverse geographical regions and crop types would provide valuable insights into the generalizability and robustness of the developed models. Overall, continued research in this domain holds the potential to revolutionize weed management strategies and contribute to sustainable agriculture practices. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Cotton

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Modified Mobility Aware MAC Algorithm to Improve the lifetime of dynamic Wireless Sensor Network 3rd

Modified Mobility Aware MAC Algorithm to Improve the Lifetim...

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3rd International Conference on Recent Trends in Machine learning, IoT, Smart Cities and Applications, ICMISC 2022

作者： Bhandari, Rajiv Ramesh Nirmal, Khyati R. Sanghavi, Mahesh R. Rajnor, d.S. Bramhecha, Amit R. Department of Computer Science and Engineering K L Education Foundation AP Guntur Dist. Vaddeswaram522502 India Department of Computer Engineering Late Sau. K. B. Jain College of Engineering Chandwad India

ISBN: (纸本)9789811960871

Cluster head selection and data processing are two of the most significant approaches for extending the lifetime of a wireless network. The approach entails a clustering mechanism focused on the deployment of nodes and the sensor’s coverage area. Cluster head gathers data from cluster nodes and compiles it before sending it to the base station. The election of cluster head and cluster members on a regular basis is a big challenge in a mobility conscious network. This paper represents new approach for appropriate cluster head selection and its member based on probability distribution function. The one-hop connection distance to reach all of the covered sensor nodes determines the starting probability of each node. The mobility aware clustering routing algorithm (MACRON) is used to elect cluster head based on lEACH algorithm, coverage and one-hop connectivity distance in terms of packet distribution, HOP distance, throughput, obtained signal power, and other factors. Furthermore, by evaluating the ratio of the current probability value to the cumulative probability value of all sensors, the MACRON algorithm dynamically calculates the shortest distance and normalizes the predicted probability. The simulation results show that the MACRON method is more successful than MEMAC and MMAC in extending the lifetime of mobility aware wireless sensor networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Sensor nodes

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detection of Sleep Apnea using Cat Swarm Optimization with TabNet versus CNN Model

Detection of Sleep Apnea using Cat Swarm Optimization with T...

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2024 International Conference on Integration of Emerging Technologies for the digital World, ICIETdW 2024

作者： Kujani, T. Arivubrakan, P. Usha, V. Alex david, S. Umamaheswari, R. Sathya, T. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331541170

Sleep apnea is a sleep disease that is often an undetected cause of death. It is generally diagnosed with polysomnography, which can be costly and uncomfortable. To solve this issue, we suggest a website for detecting sleep apnea that uses user-provided data such as the Berlin questionnaire, Epworth Sleepiness Scale (ESS), body measurements, Body Mass Index (BMI), age, and gender. The purpose of this work is to categorize sleep apnea based on the Apnea-Hypopnea Index (AHI) as normal, mild, moderate, or severe. This classification is carried out using a deep learning model, TabNet which suits the best to process the text input. It could be useful for people who feel they have sleep apnea, allowing them to take the essential steps toward a formal diagnosis and therapy. However, this aids in early diagnosis by indicating the stage of the disease, and users are recommended to check with healthcare specialists for a definitive diagnosis. For feature selection, nature-inspired Cat Swarm optimization is applied. The TabNet is trained on a dataset that includes these characteristics as well as the AHI, a common metric for determining the severity of sleep apnea. The algorithm uses these inputs to forecast the likelihood of having sleep apnea: normal, mild, moderate, or severe. The developed TabNet Model is compared with the Convolution Neural Network (CNN) and the results are showcased. To summarize, this approach is a revolutionary technique for sleep apnea detection using Cat Swarm Optimization. It promises to give a more accessible and cost-effective solution for people who feel they have this illness by utilizing user-generated data and deep learning algorithms. © 2024 IEEE.

关键词： Sleep research

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Implementation of 3-level Security System Using Image Grid Based Authentication System

Implementation of 3-Level Security System Using Image Grid B...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kiran, Ajmeera Ben Sujitha, B. Vijayakarthik, P. Krishna, Manduri Vamsi Nikitha, Suragouni Singh, Thakur Harshvardhan MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Good Shepherd College of Engineering and Technology Department of CSE India R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

We now know that the majority of computer security relies on passwords to verify and authenticate users. There are numerous proposed authentication methods, and the majority of them still have flaws. Some of them, like voice recognition, are based on the user's physical and behavioral characteristics, while others, like textual and graphical passwords, are based on the user's knowledge. However, these plans are still insufficiently secure and make it simple for hackers to steal the data. In addition, users frequently use simple passwords that are simple for an attacker to guess. As a result, something more user-friendly and secure is required to meet this demand. level 2 authentication and a one-time password that is automatically generated and sent to the authentic user via email at level 3 authentication. Additionally, the IBA System's unique image set has been used with great diligence to thwart Shoulder, Tempest, and Brute-Force attacks on the client side. The three-level security system's main goals are to make web applications safer, keep unauthorized users from accessing them, and make them easier to use. Hopefully, the system will be able to provide the user with a more secure environment free of unauthorized access. © 2023 IEEE.

关键词： Security systems

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YOlOv8 Based An Automated Segmentation And Classification Of Thyroid Nodules Using USG Images

YOLOv8 Based An Automated Segmentation And Classification Of...

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

作者： Gondi, Savita Nagaral, M.U. Department of Computer Science and Engineering B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijaypur586103 India Affiliated to Visvesvaraya Technological University Karnataka Belagavi590018 India

ISBN: (纸本)9798331505134

Thyroid nodules are a common clinical observation that needs to be accurately classified and segmented in order to be managed and treated. The use of the sophisticated object detection model You Only look Once (YOlO) version 8 to the issue of nodule of the thyroid identification and categorization is investigated in this work. We gathered and interpreted 701 thyroid ultrasound images for this study, identifying two different nodule types as benign (315 images) and malignant (344 images). To assess the model's performance thoroughly, the dataset was divided into training (70%), validation (20%), and test (10%) sets. After training YOlOv8 on these images, metrics including Precision (78.1%), Recall (81.9%), F1 score (80%), and mean average precision (mAPall) (88.1% mAP@50) are used, with Benign (91.8% mAP) and Malign (84.5% mAP. The findings show that YOlOv8, outperforms earlier model YOlOv5, achieves good accuracy in both detecting and categorizing thyroid nodules. The ability of the model to accurately distinguish between benign and malignant nodules is critical for clinical decision-making. © 2024 IEEE.

关键词： Image annotation

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Enhancing Weld Quality Assessment Using a Comprehensive Study on Combined Thermal and Residual Stress Through lenet Pattern Recognition 15

Enhancing Weld Quality Assessment Using a Comprehensive Stud...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： deepana, G. Raja, V.l. Priya, A. Parvathi Priya, J. Geetha Rajendiran, M. Babu, M. dinesh S.A. Engineering College Department of Civil Engineering Thiruverkaadu Tamilnadu Chennai India Loyola Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India R. M. K. Engineering College Department of Chemistry Tamilnadu Kavaraipettai India R.M.D. Engineering College Department of Cse Tamilnadu Kavaraipettai India Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Raja Lakshmi Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

This study aims to conduct a thorough examination of improving the evaluation of weld quality. This will be achieved by utilizing a mix of thermal and residual stress measurement, along with a pattern recognition approach employing the leNet neural network architecture. The primary aim of this study is to provide a reliable and precise approach for assessing the quality of welds. In order to enhance the evaluation of weld quality, we propose the utilization of the leNet convolutional neural network, which is a deep learning framework specifically designed for problems involving image identification. Photographs of weld cross-sections are analyzed using the network, which lets visual patterns of both thermal and residual stress distributions be captured. The findings of our research indicate a notable enhancement in the precision of weld quality evaluation when compared to conventional approaches. We use thermal and residual stress data along with deep learning techniques to make our method much better at finding and classifying defects. The proposed methodology exhibits significant potential for enhancing the overall safety and performance of welded structures across many sectors. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of Intrusion detection in Cyber Attacks using Machine learning Neural Networks

Analysis of Intrusion Detection in Cyber Attacks using Machi...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Amutha, S. Maheswari, G. Uma Nandhini, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India P.S.R Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India

ISBN: (纸本)9798350313987

Three machine learning algorithms-Support Vector Machines (SVM), k-nearest Neighbours (KNN), and Random Forest (RF)-are analyzed in this research study for their potential use in the field of cybersecurity intrusion detection. With the increasing complexity of cyber threats, robust intrusion detection systems are essential to safeguard digital systems. leveraging a comprehensive dataset of network activity, this research assesses the effectiveness of these algorithms in accurately identifying intrusions while minimizing false positives. The results showcase the strengths and limitations of each algorithm in terms of accuracy, speed, and adaptability. The results of this study show that machine learning may greatly improve cyber defenses. The necessity of cyber security has grown as our society has gotten more interconnected, as cyberattacks continue to grow in sophistication and variety. Traditional rule-based intrusion detection systems are often insufficient in the face of evolving threats. This research builds upon the foundation of intrusion detection by applying three machine learning algorithms: SVM, KNN, and RF. These algorithms offer diverse approaches to identify and respond to cyber intrusions. A dataset containing a mixture of normal network traffic and known attack patterns is utilized for training and evaluation. The study aims to assess the accuracy, speed, and adaptability of each algorithm for intrusion detection. The study's results reveal the distinct strengths and limitations of SVM, KNN, and RF in the context of intrusion detection. SVM, known for its accuracy, excelled at correctly classifying network activities, minimizing false positives, and reducing the risk of overlooking intrusions. KNN, leveraging proximity-based classification, demonstrated adaptability and simplicity. RF, with its ensemble approach, showcased efficiency and robustness in handling complex data. Each algorithm displayed varying degrees of adaptability, accuracy, and compu

关键词： Cybersecurity k-Nearest Neighbors Machine learning Random Forest Support Vector Machines

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Transfer learning Approaches for Improved Thyroid detection 5

Transfer Learning Approaches for Improved Thyroid Detection

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Selvi, d. M. Kalai Joshi, Kireet Vani, V. Vekariya, daxa Natrayan, l. Patil, Harshal R.M.D Engineering College Department of Computer Science and Engineering Chennai Tamil Nadu Tiruvallur601 206 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bangalore Institute of Technology Department of Information Science and Engineering Karnataka Bangalore560004 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Post Limda Gujarat 391760 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Maharashtra Pune412 115 India

ISBN: (纸本)9798350379945

Thyroid disease is a broad term for any medical disorder that impairs the thyroid gland's ability to generate enough hormones. It is not limited to any particular age group;it can strike anyone at any time. detecting thyroid diseases usually includes blood tests, which can be difficult to interpret due to the large amount of data required for accurate predictions. To find the best algorithm for predicting thyroid risk, this study examines different Transfer learning (Tl) methods. The dataset utilized for this work was sourced from the machine learning repository at the University of California, Irvine. The distribution of thyroid class labels in this dataset is highly imbalanced. To address this, sampling techniques are used to balance the dataset. Transfer learning models like AlexNet, Inception, and MobileNet are employed. All models achieve an accuracy rate of 97%. Upon comparison, AlexNet demonstrates the highest accuracy among the three models. The research shows that proper preprocessing and data balancing can improve thyroid detection efficiency. © 2024 IEEE.

关键词： Federated learning

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Scalable Computing in Resource Allocation 2

Scalable Computing in Resource Allocation

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Kalarani, S. Sharmila, V. Suma, S. Jayakumari, A.G. Sudha, K. St Joseph's Institute of Technology Department of Information Technology Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Valliammai Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350347579

Computational humanity is enormously voluminous and complex. One of the computing industry's fastest-growing approaches is cloud computing. It is a cutting-edge method for providing IT service over the World Wide Web. Through the Internet, this concept offers computing resources to users in a pool. Resource scheduling and allocation for various aggregate web services is a crucial and challenging problem in cloud computing. This research looks at resource allocation using scalable computing. Infrastructure as a Service (IaaS), or the service of renting out computer resources through the Internet, is offered to users by cloud computing. The client can select from a variety of computing resources depending on their needs. This approach uses the IaaS model to allocate resources for real-time tasks. Real-Time jobs must be finished ahead of schedules. Elasticity or scalable computing refers to the ability to scale up the resource in this situation in accordance with the demands. The resources are scalable and open to a vast user base. In order to finish real-time work ahead of schedules, the user can choose any number of Virtual Machines (VMs) based on speed and rate. The client leases the virtual machines. Hence the fee is set just for the duration of the rental. Additionally, a method is devised to assign VMs to programs with real-time tasks. The allocation is presented as a problem of restricted optimization. © 2023 IEEE.

关键词： Virtual machine

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