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检索条件"机构=Dept. of Computer Engineering and Application"

共 251 条记录，以下是81-90 订阅

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Detection of Brain Tumor using Convolution Neural Network through Detection of Metastasis

Detection of Brain Tumor using Convolution Neural Network th...

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Devices for Integrated Circuit (DevIC)

作者： Soumen Santra Suman Sardar Swarnadeep Sil Rudra Sarkar Arpan Deyasi Dept. of Computer Application Techno International New Town Kolkata India Dept. of Electronic Science Acharya Prafulla Chandra College New Barrackpore India Dept. of Electronics and Communication Engineering RCC Institute of Information Technology Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

A novel model using convolution neural network has been developed for near accurate detection of brain tumor by carefully preprocessing the data and addressing class imbalance. Dataset was gathered between December 2017 and March 2018, consists of 7,001 JPG MRI pictures, 3,800 of which show a healthy brain, and 3,201 of which show a brain with tumors; results 94.14% accuracy in training, 94.33% accuracy in testing and 95.12% accuracy in validation. Insignificant losses are reported in all the cases, speaks in favor of quality of segregation between pathological and normal tissues. Present study provides 92.39% sensitivity, 96.63% specificity and 97.03% precision which clearly outperforms peer-reviewed works carried out using CNN; and therefore, justifies novelty of the work.

关键词： Training Pathology Accuracy Sensitivity Convolution Neural networks Brain tumors Brain modeling Integrated circuit modeling Testing

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Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of Machine Learning Algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Sujatha Kamepalli Veeranjaneyulu N Jyostna Devi Bodapati Dept. Department of Information Technology And Computer Application Vignan's Foundation for Science Technology and Research Guntur India Dept. Department of Advnced Computer Science and Engineering Vignan's Foundation for Science Technology and Research Guntur India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes.

关键词： Cataracts Deep learning Training Accuracy Computational modeling Prevention and mitigation Medical services

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Analysis and Research on Students' Learning Behavior Based on Lag Sequence Analysis 4

Analysis and Research on Students' Learning Behavior Based o...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Han, Jiazheng Wei, Zhengang Ocean University of China Dept. of Computer Application Technology Qingdao China Ocean University of China Dept. of Software Engineering Qingdao China

ISBN: (纸本)9781665406925

with the effective practical application of data mining technology in other fields, many researchers have used data mining technology to analyze the learning process data in the field of education and teaching, and explore the hidden and potentially valuable information and knowledge. This paper mainly mines the association in students' learning behavior data through lag sequence analysis algorithm (LSA), and shows learners' learning trajectory, willingness and cognitive process in the learning process. Help to grasp the potential behavior mode of students' learning and effectively guide the development and implementation of follow-up teaching work. © 2021 IEEE.

关键词： Data mining

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Unmasking the Reality: A Comprehensive Study of Deepfake Identification Techniques and Their Expanding Terrain

Unmasking the Reality: A Comprehensive Study of Deepfake Ide...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Shubh Sharma Disha Mohini Pathak Dhyanendra Jain Somya Srivastava Prashant Vats Govind Murari Upadhyay Department of Computer Science ABES Engineering College Ghaziabad U.P India Department of Computer Science Engineering-AIML ABES Engineering College Ghaziabad U.P India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The swift progression of deepfake-generating technologies has elicited much apprehension about their potential misuse in digital media, necessitating the development of efficient detection methods. This research aims to improve the precision of deepfake detection by the application of spatial-temporal feature engineering methods. A comparative comparison of advanced preprocessing techniques is performed to enhance the extraction of spatial and temporal indicators essential for detecting deepfake abnormalities. The study investigates the amalgamation of sophisticated machine learning and deep learning models with designed characteristics to get enhanced detection efficacy. Experimental assessments are conducted on benchmark datasets to verify the effectiveness of the suggested methodologies. The findings highlight the importance of effective preprocessing strategies in enhancing model generalizability and dependability, hence supporting the overarching goal of preserving digital authenticity.

关键词： Deep learning Deepfakes Accuracy Computational modeling Refining Media Benchmark testing Feature extraction Real-time systems Resilience

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Trishla Kumari Rakesh Kumar Rajendra Kumar Dwivedi Dept. of Computer Science and Engineering Madan Mohan Malviya University of Technology Gorakhpur India Dept. of Information Technology and Computer Application Madan Mohan Malviya University of Technology Gorakhpur India

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent).

关键词： Computational modeling Memory management Insurance Medical services Consensus protocol

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Quantifying uncertainty in Bayesian Networks structural learning

Quantifying uncertainty in Bayesian Networks structural lear...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Vitor O. Barth Henrique O. Caetano Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

The key goals in learning Bayesian networks (BNs) from data are to identify significant statistical relationships between variables and to build a Directed Acyclic Graph (DAG) that represents these relationships through Joint Probability Distributions. Most research relies on score-based or conditional test methods for model selection. However, when using real-world data, it can be challenging to identify whether the learned DAG represents the underlying relations inherent in the limited datasets, particularly when evaluating data obtained from multiple independent sources. This study presents a methodology to assess the credible interval for both the existence and direction of each edge within Bayesian networks derived from data. Furthermore, it explores the fusion of models acquired from distinct and independent datasets. This approach enables the Bayesian learning of Bayesian Networks (BNs) from data by treating the uncertainty associated with the existence and orientation of each edge as a random variable. By evaluating the probability of the orientation of each edge, it is possible to suggest the existence of a potential latent variable within the dataset. If an edge exhibits equiprobable directions and is verified to exist, it becomes a plausible hypothesis for a latent variable. The Fast Causal Algorithm, originally introduced by [1], is the foundation of this approach. Finally, by employing a maximum a posteriori estimation, the most prominent edges and their respective orientations are identified and employed to create the leading DAG. We present our findings in simulated datasets with different length sizes. By comparing the structure of the learned DAGs with existing structures and evaluating the inference capabilities of the final BN, we establish that our approach achieves results comparable to the most recent studies in the field, while offering insights into the model’s reliability and improving the use of the data.

关键词： Maximum a posteriori estimation Uncertainty Scalability Data models Probability distribution Bayes methods Random variables

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Intelligent Resource Allocation in Power Grids: Leveraging Variable Neighborhood Search for Optimal Switch Allocation

Intelligent Resource Allocation in Power Grids: Leveraging V...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Henrique O. Caetano Matheus de S. S. Fogliatto N. Luiz Desuó Benvindo R. Pereira J Carlos D. Maciel Marco Aiello Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo Department of Service Computing Institute for Architecture of Application Systems University of Stuttgart

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Within the paradigm of Evolving and Adaptive Intelligent Systems, the Electrical Power System (EPS) represents a Critical Infrastructure demanding robust reliability metrics. This study pioneers the application of Variable Neighborhood Search (VNS) heuristic to the Optimal Switch Allocation (OSA) problem in the EPS. Adapting neighborhood structures and local search methods, we consider the joint allocation of Manual Switches and Remote-Controlled switches, addressing interdependencies. Results highlight VNS efficacy in navigating OSA challenges, showcasing its adaptability in an evolving system. Comparative analyses endorse VNS as a valuable tool for addressing EPS reliability within the dynamic landscape of evolving and adaptive intelligent systems.

关键词： Adaptive systems Uncertainty Switches Search problems Robustness Power grids Critical infrastructure

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Analysis of Nutritional Status of Children in Karnataka Based on Machine Learning Techniques Using Indian Demographic and Health Survey Data

Analysis of Nutritional Status of Children in Karnataka Base...

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Computational Intelligence for Information, Security and Communication applications (CIISCA), International Conference on

作者： Anjali Sharma Rohitaksha K Dept. of Computer Science and Engineering JSS Academy of Technical Education Bangalore Bengaluru Karnataka India Dept. of Master of Computer Application JSS Academy of Technical Education Bangalore Bengaluru Karnataka India

Malnutrition is caused when an individual gets very little or too many nutrients, resulting in health problems. In particular, “a deficit, excess, or imbalance of energy, protein, and other nutrients” negatively impacts the body's tissues and form. Malnutrition significantly contributes to infant mortality rates in developing nations, and India, as a developing country, experiences challenges associated with malnutrition in children under the age of five. The primary objective of this study is to analyze the factors influencing the nutritional status of under-five children in Karnataka, India, using data from the 2015-2016 Indian Demographic and Health Survey (IDHS). Logistic regression (LR), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), and Naïve Bayes (NB) are five prominent techniques in machine learning that are being utilized to successfully predict the factors affecting children's nutritional status. Body Mass Index (BMI), Height for Age Z score (HAZ), Weight for Age Z score (WAZ), and Weight for Height Z score (WHZ) are the main factors considered. Additionally, accuracy is used to do a systematic evaluation of the algorithms. Finally, the key factor impacting nutritional status in children was discovered to be HAZ based on accuracy, and the Random Forest produced the greatest findings and it was moderately superior to any other ML algorithms applied in this analysis.

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Revolutionizing Hops Plant Disease Classification: Harnessing the Power of Transfer Learning

Revolutionizing Hops Plant Disease Classification: Harnessin...

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

作者： Degadwala, Sheshang Vyas, Dhairya Panesar, Sonia Ebenezer, Divya Pandya, Darshanaben D. Shah, Vandita Dipak Sigma University Dept. of Comm. Engg. Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Drs.Kiran and Pallavi Patel Global University Dept. of Comp. Sci & Engg. Kset Gujarat Vadodara India Pes University Department of Computer Science and Engineering EC Campus Karnataka Bengaluru India Scjccs Sankalchand Patel University Dept. of Comp. Sci. Gujarat Visnagar India Shreyarth University Department of Computer Application Gujarat Ahmedabad India

ISBN: (纸本)9798350313987

This research work introduces an innovative approach for the classification of hops plant diseases through the utilization of transfer learning with state-of-the-art Convolutional Neural Networks (CNNs). By leveraging pretrained models on extensive image datasets, this study has developed a customized CNN architecture, which is specifically designed to address the unique challenges posed by hop plant diseases. Their method demonstrates remarkable accuracy in classifying multiple hop plant diseases, offering significant potential for early disease detection and precise management strategies. The paper also provides an insightful comparison of the effectiveness of each CNN architecture within the context of hops plant disease classification. This research has the potential to automate and enhance hops plant disease management, ensuring the sustainability and quality of hop crops for the brewing industry while providing a valuable framework for addressing similar agricultural challenges. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Hops Plant Disease Multi-Class Classification Natural Farming Transfer Learning

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