检索结果-内蒙古大学图书馆

Cryptography, Informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Roni Reza Abdullah Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Informatics Engineering National Institute of Science and Technology Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.

关键词：

来源：评论

学校读者我要写书评

暂无评论

LeafNet: Classification of Plant Leaf Diseases using IAO based Hybrid DL Framework

LeafNet: Classification of Plant Leaf Diseases using IAO bas...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Binju Saju V Asha Jincy C Mathew Shreya N Sangeetha SanganagoudaDambala Shruti Department of Computer Science and Engineering(Artificial Intelligence) Adi Shankara Institute of Engineering and Technology Kalady India Department of Master of Computer Applications New Horiznon College of Engineering Bengaluru India

Plant diseases can be found using an analysis of these observable symptoms. The traditional approach to disease diagnosis entails professionals visually evaluating plants. The study proposes a hybrid model to classify plant leaf diseases. Initially, images are pre-processed which includes Image resizing, Gray-scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained an an accuracy of 99.4%, sensitivity of 98.9%, specificity of 99.1%, precision of 99%, and an F1 score of 98.98%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Performance Measurements of different Classification techniques for the Alzheimer's Disease Neuroimaging Initiative

Performance Measurements of different Classification techniq...

引用

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2021

作者： Yashodhar, Archana Kini, Shashidhar Department Computer Science and Application Mangaluru India Srinivas Institute of Technology Department Head and Professor Master of Computer Application Mangaluru India

ISBN: (纸本)9781665412445

One of the neurogenerative disorders affected by many adults is Alzheimer's Disease (AD). Disease prediction is also a difficult task. Except in the healthcare domain, the principles of artificial learning and data processing are commonly used. In this research paper, using the Machine Learning method, we applied various classification algorithms on the data sets and concluded which algorithms provide the best results. Also, we have used different possible methods to evaluate the model. © 2021 IEEE.

关键词： Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

Cricket shot classification using Deep Learning

Cricket shot classification using Deep Learning

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Shanta Kallur Puneeth N. Thotad Suman Yaligar Dept. of Computer Science & Engineering KLE Institute of Technology Affiliated With Visvesvaraya Technological University Hubballi India Dept. of Master of Computer Applications KLE Institute of Technology Affiliated With Visvesvaraya Technological University Hubballi India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi India Visvesvaraya Technological University Belagavi

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters.

关键词： Deep learning Accuracy Computational modeling Neural networks Decision making Predictive models Feature extraction Real-time systems Performance analysis Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Networks for Brain Tumor Image Segmentation and Detection

Deep Neural Networks for Brain Tumor Image Segmentation and ...

引用

International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Ritesh Chandra Sadhana Tiwari Shashi Shekhar Kumar Sonali Agarwal M. Syafrullah Krisna Adiyarta Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia

Medical image analysis is a challenging and complex field these days. This discipline focuses especially on the processing of MRI (Magnetic Resonance Imaging) images. It offers multiple methods for locating brain tumors in MRI brain images and compares the precision of all the findings. Convolutional neural networks (CNN) and ResNet architectures are used to train the model. As deep learning models are highly efficient and correctly identify whether the MRI picture of a tumor is healthy or unhealthy. In this work, high-level features are extracted from the input images using the CNN architecture, which has multiple pooling layers. To create the final classification model, fully connected layers are then routed through the extracted characteristics. However, CNN has some drawbacks, and to overcome these issues, a ResNet based architecture has been used. Additionally, U-Net-based MRI brain tumor segmentation algorithms have gained popularity because they significantly improve segmentation accuracy by infusing high-level and low-level feature information via skip connections. The suitability of an attention module called Attention Gate, which was recently developed, for tasks involving the segmentation of brain tumors has also been explored in this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Autism Spectrum Disorder Detection using Autistic Image Dataset

Autism Spectrum Disorder Detection using Autistic Image Data...

引用

International Conference on Electrical Engineering, computer science and Informatics (EECSI)

作者： Ritesh Chandra Sadhana Tiwari Ashutosh Kumar Sonali Agarwal M. Syafrullah Krisna Adiyarta Information Technology Department Indian Institute of Information Technology Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention can greatly improve outcomes for people with ASD. Recently, deep learning algorithms have been applied to aid in the early detection of ASD using facial images. In this work, modifications of the commonly used VGG16 and VGG19 models for image recognition tasks are proposed to improve the performance of detecting ASD from a child’s frontal face image. The proposed model is unique, as it alters the architecture of existing models, adds an attentional mechanism, and applys transfer learning. These changes are intended to decrease the chance of overfitting and enhance the model’s capacity to capture subtle face characteristics. The performance of the updated model is assessed through accuracy, which is 82.55% for VGG19 and 80% for VGG16 model, and contrasted the outcomes of the original model. Performance of the modified model is also compared with that of the original model. The obtained results show that the modified model outperforms in detecting ASD from facial images, suggesting that the proposed modification is non-invasive for early detection of ASD and has the potential to contribute to the development of efficient tools.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Diabetic foot ulcer detection using deep learning approaches

引用

Sensors International 2023年第1期4卷 100210-100210页

作者： Thotad, Puneeth N. Bharamagoudar, Geeta R. Anami, Basavaraj S. Department of Master of Computer Applications KLE Institute of Technology Hubballi 580027 India Visvesvaraya Technological University Jnana Sangama Belagavi590018 India Department of Computer Science & Engineering KLE Institute of Technology Hubballi 580027 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

The most recurrent side effect of diabetes is diabetic foot ulcers and if unattended cause imputations. Diabetic feet affect 15% to 25% of diabetic people globally. Diabetes complications are due to less or no awareness of the consequences of diabetes among diabetic patients. technology leveraging is an attempt to create distinct, affordable, and simple diabetic foot diagnostic strategies for patients and doctors. This work proposes early detection and prognosis of diabetic foot ulcers using the EfficientNet, a deep neural network model. EfficientNet is applied to an image set of 844-foot images, composed of healthy and diabetic ulcer feet. Better performance is obtained compared to earlier models using EfficientNet by carefully balancing network width, depth, and image resolution. The EfficientNet performed better compared to popular models like AlexNet, GoogleNet, VGG16, and VGG19. It gave maximum accuracy, f1-score, recall, and precision of 98.97%, 98%, 98%, and 99%, respectively. © 2022 The Authors

关键词： Image resolution

来源：评论

学校读者我要写书评

暂无评论

An Effective Analysis of Machine Learning Models for Prediction of Heart Disease

An Effective Analysis of Machine Learning Models for Predict...

引用

Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Rudragoud Patil Shambahvi C. Kamkar Shwetha Indudhar Goudar Dept of Computer Science and Engineering K.L.S.Gogte Institute of Technology Belagavi Karnataka India Dept.of Master of Computer Applications K.L.S Gogte Institute of Technology Belagavi Karnataka India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren’t many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best.

关键词： Heart Support vector machines Radio frequency Medical services Nearest neighbor methods Data models Diabetes Decision trees Random forests Diseases

来源：评论

学校读者我要写书评

暂无评论

Spice Vision: Deep Learning Enhanced Spice Classification System

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

引用

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Laxmi Pujari Meenaz Belavatgi Mahantesh M. Sajjan Vishwanath Kamatar Pradeep Surasura Nausheen Ammanagi Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi India Dept. of Computer Science and Engineering K.L.E Institute of Technolog Hubballi India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model.

关键词： Deep learning Visualization Technological innovation Accuracy Quality assurance Neural networks Manuals Convolutional neural networks Biomedical imaging Resilience

来源：评论

学校读者我要写书评

暂无评论

Neural Network Framework based Malware Classification in Cybersecurity: A Hybrid Approach

Neural Network Framework based Malware Classification in Cyb...

引用

Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Mahmudur Rahman K. M. Sadman Sakib K B M Tahmiduzzaman Ravina Akter Youki Jerin Akther Munia Nikita Computer Science & Engineering Daffodil International University Dhaka Bangladesh Electrical & Electronic Engineering Ahsanullah University of Science & Technology Dhaka Bangladesh Information Technology University of Technology Sydney Sydney Australia Master in IT Cybersecurity King’s Own Institute Newcastle Sydney Australia

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Malware significantly endangers internet security. Criminals may get illegal activities access, expropriate confidential information, extort, and engage in further illicit activities with malware. Several criminal organizations execute targeted malware assaults. They may repurpose harmful code from their current malware copies to create more sophisticated variants that evade detection. Acquiring sufficient viral data for a custom deep learning model may be challenging. Regardless its enhancement, a distinctive deep learning model will exhibit inadequate results on testing data due to its inability to comprehend the intricate information inside the augmented data. The pre-trained model exceeds the unique model using both limited and supplemented data. Pre-trained algorithms are instructed to use augmented data to get more precise and varied information. Malware classification requires enhanced recall and precision. Our hybrid malware classification approach employs Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) to address data imbalance and scarcity. To optimize data use, we refine our pre-trained model using novel attributes. In a subsequent step, we evaluated the hybrid models using optimum weights to see which one performed best on the test data; this yielded an accuracy of 96.76% as well as a Recall, Precision, F1-Score and Specificity of 97%.

关键词： Deep learning Representation learning Technological innovation Recurrent neural networks Accuracy Scalability Malware Data models Convolutional neural networks Testing

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：