检索结果-内蒙古大学图书馆

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Padmavathi, A. Yasaswy, K. Rahul, K.Jayesh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331528201

In Today scenario , Android is one of the most frequently used mobile operatings systems, thus it is a priority for advanced threat actors and hackers. Malicious code is often found in Android applications to which security solutions have a problem of identifying the threats. Today's Android malware is sophisticated enough that it can avoid most ***, machine learning is more efficient in stopping a large number of diverse contemporary threats to Android devices. These machine learning methods essentially first rely on the detection of sequential patterns of malicious behaviors, and secondly, distinguish between existing and new types of threats that have previously exhibited typical and atypical behavior. Our proposed approach builds on Ensemble learning where the methods include Support Vector Machines, Decision Trees, Logistic regression and XGBoost to bolster the detection abilities of the model. This methodology serves to enhance the specific model performance on identifying known malware. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Infield disease detection in citrus plants: integrating semantic segmentation and dynamic deep learning object detection model for enhanced agricultural yield

引用

Neural computing and Applications 2024年第35期36卷 22485-22510页

作者： Rani, N. Shobha Krishna, Arun Sri Sunag, M. Sangamesha, M.A. Pushpa, B.R. University Bengaluru India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Chemistry The National Institute of Engineering Mysuru India

Simultaneous and dynamic detection of diseases from infield images of citrus plants is the primary objective of this investigation. Refraining from discarding the disease-related issues would reduce agricultural yield and production, resulting in significant crop loss and economic instability. computer Vision offers an inexpensive and efficient solution for detecting and predicting multiple diseases from infield images of citrus plants in various stages of plant growth. This study aims to perform simultaneous detection and prediction of cankers, mites, and nutritional deficiencies from infield images of citrus plants. For this purpose, over 4914 samples are annotated that are obtained from 441 image samples collected from orchards of citrus plants. Initially, the input image is subject to preprocessing followed by segmentation of prominent leaf regions and disease prediction. Contributions investigated in this work are two-fold: a deep semantic segmentation model named Dynamic U-Net is employed to extract prominent regions of interest, and a dynamic, lightweight object detection deep learning model for predicting diseases. From experimental outcomes, the segmentation efficiency is found to be 89.07% foreground accuracy, 0.7881 of IoU, and 0.9188 of the Dice coefficient. The object detection performance is quantified using the mapped metric, resulting in 0.85, 0.71, 0.64, and 0.27 efficiency concerning plant diseases, cankers, mites, and nutritional deficiency. As per the findings, the proposed approach is an effective solution to perform automated detection and prediction from infield images of citrus plants. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Citrus fruits

来源：评论

学校读者我要写书评

暂无评论

Bone Fracture Detection Using YoloV9

Bone Fracture Detection Using YoloV9

引用

2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Nivethitha, V. Sriram, Suthir Praadeep, G Kesava Prarthana, S. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Chennai India

ISBN: (纸本)9798350390049

This paper presents the design of a Bone Fracture Detection System at the forefront of YOLOv9-T concerning the detection and localization of the actual site of bone fracture in X-ray images. The system would help quickly and reliably in giving a fracture diagnosis, and that is a critical side of health care sometimes it has the power to actually determine the result of some treatment. This included elbow, finger, forearm, humerus, shoulder, and wrist fractures. Efficiency and accuracy of object detection by the model were improved with the use of the YOLOv9-T on object detection tasks. With system bounding box annotation, the testing accuracy reached up to 96.50%. This study, therefore, highlights the needs for developing more advanced models of machine learning into the workflow of medical imaging probably that could be leading to lesser numbers of diagnostic errors and faster time lines in treatments in the clinic. Results The system proposed by this study is likely to be helpful in a medical practice where the efficiency as well as the accuracy about the diagnosis may be augmented. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Secure ML Based Image Encryption Framework with Recursive Splitting and Linear Feedback Shift Register 5

Secure ML Based Image Encryption Framework with Recursive Sp...

引用

5th International Conference on Circuits, Control, Communication and computing, I4C 2024

作者： Satya Balaji, Bandaru Rohan Varma, Alluri Jayanth Ashwini, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331528539

With the development of advanced communication ensuring the secure transmission of images has become more and more important. This research suggests a novel approach for digital image encryption and decryption that combines Linear Feedback Shift Register (LFSR) encryption, recursive image splitting and shuffling in order to increase security. Test image is divided into blocks and each block is encrypted using a different LFSR key. The encrypted blocks are then joined to create a single encrypted image that is ready to be sent. The receiver uses a machine learning model that has been pre-trained to identify the specific LFSR key used for each block after breaking the image back up into its component blocks. This allows for precise decryption. The viability of the encryption process is assessed using entropy analysis, histogram comparison and pixel relationship etc. The results show that the encrypted images have a notable disruption of spatial relationships, a high degree of unpredictability, and significant interference with spatial relations. Furthermore, evaluation is done on the performance of multiple Support Vector Machine (SVM) models with different kernel functions. The results indicate that the SVM with a radial basis function (RBF) kernel performs best in terms of key identification accuracy. This thorough analysis demonstrates the suggested encryption method's resilience and its potential for safe image transfer across a range of applications. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Advancing of Microservices Architecture with Dockers 15

Advancing of Microservices Architecture with Dockers

引用

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

作者： Sah, Kriti Priya Jain, Nikunj Jha, Priyanshu Hawari, Jawed Beena, B.M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

Docker has revolutionized web hosting. It offers efficiency, scalability, security, and automation. With Docker, businesses and individuals can simplify their hosting. With the help of Docker, the development process is simplified via managing to package the application into standardized units called containers. They can enhance website performance and contribute to a sustainable digital ecosystem. Micro-service architecture might be adopted for smooth flow for the development process. Resource optimization might get improved. Security measures could be bolstered. Multi-cloud deployment strategies may be explored. Server-less computing integration is a possibility. Containerized data storage solutions might be explored. . In the future, Docker based hosting could involve Kubernetes for container orchestration. Machine learning capabilities could be incorporated. Green computing initiatives might be implemented. ©2024 IEEE.

关键词： Plastic bottles

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Approach to Streamline Resume Categorization and Ranking 5

Deep Learning based Approach to Streamline Resume Categoriza...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ramyar, R. Nagarani, Gundapaneni Natarajan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

In today's world, a lot people are searching and applying for jobs. When there is a need for company to recruit new employees, it is difficult for them to select the correct resume according to their job description from the lakhs of resume that they received. This project will help the recruiters to select the accurate resume with relevance to their job description. It will produce resumes which matches the best with the recruiter's job description by matching the skills, experience of resume with the job description. Used various advanced natural language processing models and deep learning models to provide more accurate results than any other existing models. With these methods the model achieved accuracy of 98%. Also, system will send mail to those people whose resume has not matched much with the job description stating the reason like what lacks in their resume when compared with job description. This will be helpful for candidates to know what lacks in them. Also, for the HR, interview questions will be generated according to each candidate resume. This will reduce the Reduce HR's work for preparing interview questions for each person. © 2024 IEEE.

关键词： Apprentices

来源：评论

学校读者我要写书评

暂无评论

An Incremental Naive Bayes Learner for Real-time Health Prediction 2

An Incremental Naive Bayes Learner for Real-time Health Pred...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Appasani, Deepthi Bokkisam, Charan Sai Surendran, Simi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Healthcare monitoring systems have improved with the Internet of Things and machine learning prediction models. Traditional batch machine-learning approaches cannot generate an effective model since most data will be continuous and real-time. Real-time medical data processing is challenging since the entire data is unavailable during prediction. Here, a continuous model adaptation based on incremental learning is demanded. The development of such a system can significantly improve patient outcomes and reduce healthcare costs. This paper proposes an Incremental Naive Bayes Learner that can handle concept drifts in data. The algorithm keeps a sliding window of data constantly updated as new data is added. The adaptive window size determines how much data is used to train the model at any given time. After processing each chunk of data, the algorithm calculates the model's accuracy. The system detects a concept drift and dynamically updates the training set if the accuracy drops below a predefined threshold. We conducted a comparative evaluation of state-of-the-art batch and incremental learning algorithms on different medical datasets, demonstrating the impact of incremental learning. The results demonstrate the effectiveness of our approach: the Agrawal dataset achieved the highest accuracy at 66.6%, followed by Dialysis at 61.6%, while Liver and HCC achieved 50% and 58.3% accuracy, respectively. This approach ensures a sustained high level of accuracy in healthcare monitoring systems over time, even amidst concept drift. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Parser for TINY Language 8

Parser for TINY Language

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Priya, Ch. Durga Raju, Ch. Vikram Daniel, A. Issac Patel, G. Rishik Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer science and Engineering Bangalore India

ISBN: (纸本)9798331505462

This study provides a novel method to generate a compiler, which translates a program using TINY language. TINY language is a simple programming language which is designed to understand the basic concepts of compiler. A compiler is a type of program which is designed to convert the code written in high-level language into machine language that is understandable by the computer. The procedure for building a parser requires various sub-tasks, there are a list of operations that include parsing or splitting the code into tokens, checking's the code's syntax using grammar rules, understanding, interpreting and optimizing the provided code and generating the execution-ready code by the performance of last assembling. In order to face the challenges of compiler construction, which includes rule-based complex grammar, error handling, and fast tokenization, the python programming language is implemented for developing pyLexer(tokenization) and pyParsing (syntactic analysis) libraries. Lexer is used for matching regular expressions to return corresponding tokens(lexemes), while parser is supposed to check whether the given syntax is according to the grammar rules or not through recursion. Web application is created for this using Streamlit, which provides users with many useful features like text rendering capability, error highlighting and code suggestions. © 2024 IEEE.

关键词： Program compilers

来源：评论

学校读者我要写书评

暂无评论

Epilepsy Classification using Multi-Sequence MRI's: An Ensemble Approach 15

Epilepsy Classification using Multi-Sequence MRI's: An Ensem...

引用

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

作者： Rajasekhar, Alamanda Hima Naga Kavya, Gurram Sriya, Kota Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Epilepsy is a major threat, when it comes to human health. Classifying brain anomalies on MR images as a pathological clue of Epilepsy is crucial for diagnosis. Electroencephalography (EEG) and Magnetic resonance (MR) imaging are only two of the diagnostic tools that have been proposed for use in screening for epileptic seizures. The diagnosis can be made with the use of many MR images taken at different times by a neuroradiologist. The dataset under consideration is comprised of axial multi-sequences of MR images, and it features both epileptic and nonepileptic aberrant brain structures from a wide range of people. This article performs a comparative study among the models - CNN, MobileNet, InceptionV3, VGG16, VGG19, LSTM, GRU and also the Ensemble models - 'CNN + MobileNet', 'VGG16 + LSTM', 'VGG16 + GRU', 'VGG19 + LSTM' and 'VGG19 + GRU'. The Epilepsy prediction for user upload images is achieved through an ensemble approach. We have demonstrated the effectiveness of these models in correctly detecting epileptic patients based on multi-sequence MRI data by achieving extremely accurate and robust classification findings through extended experimentation. The experiments demonstrated the classification best accuracy of 97.16% for 'VGG19 + LSTM', while the lowest score was 69.93 for InceptionV3 among all the twelve models. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Filters and Deep Learning Models to predict Diabetic Retinopathy 9

A Comparative Study of Filters and Deep Learning Models to p...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Muddaluru, Roshan Vasu Ravikumar Thoguluva, Sharvaani Prabha, Shruti Reddy, Tanuja Konda Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

The retina is an essential component of the visual system, and maintaining eyesight depends on the timely and accurate detection of disorders. The early-stage detection and severity classification of diabetic retinopathy (DR), a significant risk to the public's health is the primary goal of this work. This study compares the outcomes of various deep learning models, including InceptionNetV3, DenseNet121, and other CNN-based models, utilizing a variety of image filters, including Gaussian, grayscale, and Gabor. These models could detect subtle pathological alterations and use that information to estimate the risk of retinal illnesses. The objective is to improve the diagnostic processes for DR, the primary cause of diabetes-related blindness, by utilizing deep learning models. A comparative analysis between Greyscale, Gaussian and Gabor filters has been provided after applying these filters on the retinal images. The Gaussian filter has been identified as the most promising filter by resulting in 96% accuracy using InceptionNetV3. © 2024 IEEE.

关键词： Gaussian distribution

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：