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检索条件"机构=Computer Science and Engineering Silicon Institute of Technology"

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Minimizing Latency While Transferring IoT Data to Cloud Using Gap Optimization Algorithm

引用

International Virtual Conference on Industry 4.0, IVCI4.0 2021

作者： Roy, Siddhant Saxena, Eesha Quadir Md, Abdul School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789811999888

The Internet of Things (IoT) refers to billions of smart objects that now are hooked to the internet, capturing and transmitting information throughout the world, and as a result, have to compute trillions of data in mere seconds. IoT devices have low storage and computing power of their own due to which the concept of the Cloud has emerged exponentially. Cloud Computing proves to be a prime alternative for this issue since it can provide vast amounts of data storage and processing capacity to exercise complex computation. With the adoption of this new technology comes certain outcomes among which a pronounced one is latency delays while transferring the IoT data over to the cloud, which has a huge impact on the business world as well. In this paper, we propose a gap optimization algorithm for the ongoing data delay problems faced by both cloud service consumers and providers. The algorithm minimizes the data put off through factoring latency and the use of the dependencies to shape the IoT statistics supply with a corresponding cloud server, with minimum latency put off. The paper also outlines the importance of fog and edge computing in significantly reducing latency, therefore, decreasing operational costs with it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Wireless Sensor Network and AI Boom in Classroom-A Review

Wireless Sensor Network and AI Boom in Classroom-A Review

引用

2023 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2023

作者： Ashwitha, B.M. Bharathi, P.T. Siddaganga Institute of Technology Department of Computer Science and Engineering Tumkur India

ISBN: (纸本)9798350347296

During the long evolution of the modernized education, schools have gone through many changes and accumulated a large amount of technical information. The educational board attaches a great benefit of information technology in curriculum. Students must consider IT courses to analyze the features and the implementation of today's academic environment and integrate that information into curriculum goals and teaching materials. It will support educational sectors to reach the curriculum needs of the classroom. With the initiation of educational modernization evolves the availability of educational facts. Consequently, this study makes use of a wireless sensor network (WSN) to collect and deliver data about day-to-day information. Then, it makes use of AI to evaluate the quality of the classes, analyze how it benefits students and also to direct real-time modifications to improve performance. © 2023 IEEE.

关键词： Curricula

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An Early-Stage Colorectal Cancer Detection from Colonoscopy Images Using Enhanced Res-UNET 1

引用

8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Dasari, Madhavi Appaji, Setti Vidya Sagar Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Computer science and Engineering Department Sridevi Women’s Engineering College Hyderabad India Baba Institute of Technology and Sciences Visakhapatanam India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

The segmentation of the polyps during colonoscopy is one of the most crucial factors for successful colon cancer diagnosis. Due to the wide variety of sizes and shapes of these polyps, this process can be challenging. The introduction of deep learning models in biomedical image analysis has shown a great impact on disease detection and segmentation. Most of the deep learning models concentrated on larger size polyps only, smaller size polyps may not segmented accurately during the procedure and can cause cancer in 5–7 years. This can have a significant impact on the early-stage detection of colon cancer. This chapter proposes an enhanced Res-UNET-based deep learning–based model that can improve the performance of segmentation on small-size polyps. We evaluated the proposed model on ETIS-LaribPolypDB, CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG colonoscopy image datasets. The results show that the proposed enhanced Res-UNET model achieves the top Dice segmentation accuracy and top Hausdorff distance over the recent state-of-the-art models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image enhancement

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Improving Diabetes Classification Accuracy by Incorporating External Factors in a Machine Learning Framework 16

Improving Diabetes Classification Accuracy by Incorporating ...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Mane, Navnath Patel, Samprit Athavale, Vijay Anant Lobo, L.M.R.J. Walchand Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India Walchand Institute of Technology Department of Information Technology Maharashtra Solapur India

ISBN: (纸本)9798331505264

Diabetes is a chronic condition that significantly increases the risk of developing serious health complications, including heart disease, renal failure, vision impairment, and nerve damage. Early prediction and accurate diagnosis are crucial to preventing these complications. In current clinical practices, a variety of tests are used to collect data for diagnosing diabetes. However, the large volume of available datasets makes it difficult to efficiently extract meaningful insights, often limiting the accuracy of predictions. Machine learning (ML) algorithms have shown promise in analyzing these complex datasets to uncover hidden patterns and predict diabetes-related health risks. This study proposes a novel diabetes prediction model that leverages a Random Forest Classifier to improve classification accuracy. Traditional factors such as age, body mass index (BMI), glucose levels, and insulin resistance are considered, but the study introduces an innovative approach by incorporating external factors (such as lifestyle and socio-economic factors) that could influence diabetes progression. An enforced pipeline model is used in this study, streamlining data preprocessing, feature selection, and algorithm optimization, which results in a more precise prediction framework. This approach brings the prediction accuracy for diabetes classification closer to 77%, a significant improvement over current models. © 2024 IEEE.

关键词： Diabetes Diabetes Prediction Accuracy Disease Prediction Health Care Machine Learning Random Forest Classifier

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Evaluation of Machine Learning Classifiers for Choroiditis Prediction from Fundus Images 14

Evaluation of Machine Learning Classifiers for Choroiditis P...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Raj, Srishti Keshri, Anup Kumar Birla Institute of Technology Mesra Computer Science & Engineering Ranchi India

Choroiditis is a group of inflammatory eye diseases where inflammation occurs in the eye’s choroid, and Fundus images help in its diagnosis and tracking over time. Inflammatory eye diseases are one of the significant reasons for blindness all over the globe. Hence, it is crucial to develop frameworks that can proficiently diagnose the presence of such diseases. For this work, we collected Fundus images from a tertiary eye hospital and applied machine learning algorithms to classify them into the two classes of normal and choroiditis. At present, we have evaluated four machine-learning techniques on the image data set on basis of accuracy and F1-score. SVM has reported highest classification accuracy of 83% and F1-score of 0.82. © Grenze Scientific Society, 2023.

关键词： Support vector machines

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Compact and Secure Image Encryption for IoT Systems Employing ECC and AES Hybrid Cryptography

Compact and Secure Image Encryption for IoT Systems Employin...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Wandile, Piyush S Singh Kirar, Bhupendra Jain, Saurabh Sahu, Yatendra Computer Science and Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal India Electronics and Communications Engineering Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331529833

This study focuses on development of a secure and efficient image cryptosystem optimized for IoT devices. The proposed technique addresses the and resource constraints of IoT devices, it introduces a hybrid encryption approach where an amalgam of algorithms such as Arnold's Cat Map, ECC, Diffie-Hellman (ECDH), AES is used for optimized encryption for the image data. This combination ensures compact, secure, and resource-efficient encryption suitable for IoT systems. The system is evaluated through various performance metrics demonstrating its effectiveness in lightweight cryptography applications while ensuring high levels of security against common cryptanalytic attacks. © 2025 IEEE.

关键词： Encryption algorithms

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Efficient Classification of Rice Varieties Using Optimized Convolutional Neural Networks 8

Efficient Classification of Rice Varieties Using Optimized C...

引用

8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Pathak, Abhishek Kumar Singh, Ankit Kumar Bhatia, Vimal Singh, Puneet Indian Institute of Technology Department of Electrical Engineering Indore India Motihari College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Rice type classification is a crucial task in agri-cultural automation, aimed at improving quality control and ensuring market standards. This study presents a deep learning-based approach using a optimized Convolutional Neural Network (CNN) model implemented with PyTorch to classify five rice varieties: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. Our proposed model diverges from conventional methods that rely heavily on complex machine learning algorithms or pretrained CNN architectures like MobileNetV2, VGG16, EfficientNetB0 etc., which require a large number of parameters and layers, re-sulting in increased computational complexity. Through proposed approach, we achieved a remarkable 99.81% validation accuracy and a minimal validation loss of 0.01 % with a significantly reduced model size 30.76 MB. The simplicity of the proposed CNN architecture not only enhances computational efficiency but also maintains high accuracy, making it a viable solution for rice type classification in practical applications. This study demonstrates that a fine-tuned CNN model can outperform more complex models, providing an efficient and effective alternative for agricultural classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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DeepPose: An Integrated Deep Learning Model for Posture Detection Using Image and Skeletal Data 14

DeepPose: An Integrated Deep Learning Model for Posture Dete...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Manvendra Alam Ansari, Md. Sarfaraj Chandra Govil, Mahesh National Institute of Technology Sikkim Computer Science and Engineering Sikkim India

ISBN: (纸本)9798350335095

Identifying human actions and postures presents significant challenges for computerized systems. The categorization of these tasks holds particular relevance in the fields of health and robotics. Leveraging artificial intelligence technologies, it becomes feasible to define and classify recurring physical movements and postures accurately. Proper posture is further essential for the rehabilitation of patients because it affects the effectiveness of exercise training. Unfortunately, patients fail to follow the correct sequence when performing the exercises. To pursue the problem, a new method is proposed for posture recognition and pose estimation that does not require wearable devices. The proposed model utilizes 2D coordinates derived from the 2D poses as inputs with 18 joints of the human body as key points, along with an image dataset, to accurately classify various postures. This study involved training a custom CNN named DeepPose using both image and keypoint datasets and conducting a comparative analysis with the performance of two other pre-trained models. The result shows that the proposed model with the keypoint dataset outperforms over image dataset. © 2023 IEEE.

关键词： Patient rehabilitation

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A Differential Constrained Optimization Algorithm Based on Component Model 4

A Differential Constrained Optimization Algorithm Based on C...

引用

4th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Chen, Yuan Nie, Xin Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798350373233

The resolution and implementation of constrained optimization problems consistently remain a prominent subject in the realm of contemporary complex engineering applications. In this paper, the amalgamation of extraction and partitioning technology with the fundamental differential evolution algorithm is achieved, facilitating the transplantation of feasible component information through the algorithm's inherent mutation operation and the segmentation technology's feasible component breeding pool. Simultaneously, the adroit integration of adaptive constraint processing technology occurs within the original selection operation. In this paper, a component-based differential constraint algorithm is proposed and substantiated through numerical experiments conducted on common test functions. The experimental findings showcase the algorithm's comparable or superior performance in terms of effectiveness, robustness, accuracy, and ease of implementation when compared to certain existing algorithms. © 2023 IEEE.

关键词： Constrained optimization

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Validating User-Centric Business Process based Service Composition 4

Validating User-Centric Business Process based Service Compo...

引用

4th International Conference on Computing and Communication Systems, I3CS 2023

作者： Mishra, Sugyan Kumar Sarkar, Anirban National Institute of Technology Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350323771

Now-a-days, service composition is an interesting mechanism in various fields, such as the Internet of Things (IoT), Cloud Computing, and Software-defined networks. Service composition aims to allow interactions among user requirements and their components. Several research studies have been performed on the formal verification to validate the service composition. However, less attention is paid to the service composition validation (completeness and correctness) in the IoT environment using business processes (BPs). Therefore, this paper provides a validated service composition (VSC) approach in the IoT environment. This approach considers the look-up table and Triple Graph Grammar (TGG) rule for the service composition. Look-up table enables to establish a one-to-one relationship between business process (BP) and service composition. This table has limitations in-terms of representation. So, the TGG rule is used to establish the m-to-n relationships. The BP elements are mapped with the service composition, but the backward transformation is not possible. An algorithm is designed for validation purposes. The case study related to healthcare, i.e., clinical decision support system (CDSS), is used to evaluate the proposed approach. Various dynamic features are considered to compare the VSC with the existing approaches. The novelty of this paper is to prove the VSC is complete but not correct. The effectiveness of the VSC is shown in terms of validation and execution time with respect to BPs. © 2023 IEEE.

关键词： Internet of things

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