检索结果-内蒙古大学图书馆

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

作者： Kiruthika, V.R. NirmalaDevi, J. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379945

Alzheimer Disease is a progressive disorder that affects memory and other significant mental actions. This gives an immense challenge to the care takers and patients and gets worsen over time. This work is proposed in a way to support Alzheimer patients in carrying out their routine activities independently and improve their quality of life. The proposed system integrates machine learning algorithms with mobile application for assisting the patients and supporting the care takers as well. The first part includes the AI algorithms such as Long Short-Term Memory (LSTM), Random Forest, Logistic Regression, Decision Tree, Support Vector Classifier (SVC) and Light Gradient-Boosting Machine (LightGBM) employed in predicting their routing activities and the results are compared to elevate the best algorithm. The second part consists of support system in which the mobile application plays a role in various tasks such as location tracking, personalized reminders, scheduling the medication and anomaly detection in human behavior. The study aims to present a support system in assisting the Alzheimer's patients with routine activities and scheduling their significant actions. This paves a way for improvising the patient safety and eases the burdens of care givers. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Usability Evaluation of Online Food Delivery Applications in Bangladesh: A Comparative Study

Usability Evaluation of Online Food Delivery Applications in...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Islam Arif, Md. Ariful Kabir, Shahriar Islam, Zohirul Hussain Khan, Md Faruk Islam, Iyolita Islam, Muhammad Nazrul Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Professionals Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331535476

The online food order and delivery service is becoming a very popular web-based application in Bangladesh. These days, a growing number of individuals are interested in ordering a variety of foods online. In addition, people are looking for quality food that they can purchase whenever and wherever they want. Online food delivery services are required to be intuitive, user-friendly, and accessible to increase their acceptability in Bangladesh. Therefore, the objective of this study is to explore and assess the existing online food delivery apps in Bangladesh and identify the best application from a usability perspective. To attain these objectives, four online food delivery apps (Food Panda, Hungrynaki, Pathao Food, and efood) were evaluated through Heuristic Evaluation, Cognitive Walkthrough, and System Usability Scale (SUS). The study's findings showed that each app has a number of usability issues requiring to be resolved in order to improve acceptance. The results indicated that from heuristic evaluation, Food Panda had the most number of problems and Hungrynaki had the least number of usability issues;from the cognitive walkthrough perspective, Food Panda and eFood was the mostly usable application;and from SUS evaluation score, Food Panda achieved best score from the users. Overall, Food Panda is the best application from the usability perspective. © 2024 IEEE.

关键词： Food ingredients

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Sentiment Analysis of Amazon Fine Food Reviews Using Fuzzy Logic 2nd

Unsupervised Sentiment Analysis of Amazon Fine Food Reviews ...

引用

2nd International Conference on Computing, Communication, and Learning, CoCoLe 2023

作者： Sharaff, Aakanksha Rajput, Nandini Papatla, Sai Rohith Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

ISBN: (纸本)9783031569975

Sentiment analysis has been increased popularity among the social media. The sentiment classification with machine learning identifies the positive and negative sentiment but the problem is that it doesn’t identify the duplication of reviews by the same individual. In this study, the sentiment of food reviews is assessed by employing a set of fuzzy rules and this suggested fuzzy system uses NLP (Natural Language Processing) techniques accompanied by an experimental unsupervised fuzzy rule-based system consisting of nine rules to classify the reviews into positive and negative. Unsupervised fuzzy rule-based systems are particularly advantageous in handling intricate and disorganized data, where the interrelationships among variables may not be precisely defined or readily apparent. This research took the dataset from Amazon fine food reviews and explores the more insight of food reviews. The proposed method considers the unlabeled text that utilizes the VADER tool with Fuzzy based system that outperforms among the other state-of-art algorithms with 86.3% of accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Detection of Cyber Attacks using AI 10

Detection of Cyber Attacks using AI

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Saravanan, Gurunishal Dhas, Julia Punitha Malar Devapriya, I. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

Several cyber attacks happen globally on several commercial and industrial technological devices. If these attacks are detected early, the damages can be reverted or even prevented. Models such as Gaussian Naive Bayes, Decision Tree, SVM, Random Forest, and Logistic Regression are used to detect these attacks. After these algorithms are trained, a custom model is designed using ensemble learning methods. The ensemble learning method used is known as Stacking, which integrates many algorithms to provide a better accuracy value. The model's performance is assessed and compared to consider the best model to detect cyber attacks. The accuracy score is used to evaluate the models. Based on these results, the most suitable model is chosen. © 2024 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Integration of Explainable Artificial Intelligence (XAI) in the Development of Disease Prediction and Medicine Recommendation System 2

Integration of Explainable Artificial Intelligence (XAI) in ...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Mathew, Joel Chitra, R. Stephen, Caleb Koshy, Richie Suresh Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350360523

The contemporary healthcare landscape necessitates innovative solutions to improve transparency and understanding in medical decision-making. This paper proposes an advanced medicine recommendation system and a robust disease prediction model integrating explainable AI (XAI). Recognizing the prevalence of misinformation and the urgent need for user-friendly applications, the system empowers users to manage their health proactively. It can be extended beyond common diseases, encompassing rare diseases, and employs XAI algorithms, specifically SHAP and LIME, to enhance transparency. The system incorporates Random Forest Classifier and Decision Tree models, showcasing high accuracy and robustness. The explanation models contribute to user understanding, while performance metrics offer insights into model strengths and generalization abilities. Figures depict SHAP outputs for Decision Tree and Random Forest models, emphasizing transparency in medical predictions. This proposed system addresses critical healthcare challenges, fostering informed decision-making and user trust. © 2024 IEEE.

关键词： Transparency

来源：评论

学校读者我要写书评

暂无评论

White blood cell segmentation using U-Net and its variants to improve leukemia diagnosis

引用

Neural Computing and Applications 2025年第5期37卷 3265-3286页

作者： Joshi, Vivek C. Mehta, Mayuri A. Kotecha, Ketan Ahmedabad India Department of Computer Engineering Sarvajanik College of Engineering and Technology Surat India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

Numerous computer-aided leukemia detection methods have been introduced to overcome the limitations of clinical diagnosis procedures. The precision of computer-aided leukemia detection highly depends on the accurate segmentation of white blood cells (WBCs) from the stained whole slide image (WSI). This paper proposes WBC segmentation from WSI using U-Net and its variants. The major contributions to this paper are as follows. First, WBC segmentation is proposed using U-Net, U-Net++ and three transfer learning-based U-Net models, namely U-Net-VGG16, U-Net-VGG19, and U-Net-ResNet. Second, a comprehensive and comparative experimental analysis of the proposed WBC segmentation approaches is presented using four evaluation parameters, namely Dice coefficient, Intersection over Union (IoU), precision, and recall. Third, WBC segmentation approaches are also evaluated using four loss functions such as binary cross-entropy, focal, Dice, and IoU to identify the most effective loss function for WBC segmentation. Finally, the trained models’ ability to perform WBC segmentation is validated to assess their practical applicability. The empirical results reveal that U-Net-VGG16 and U-Net-VGG19 achieve a high Dice coefficient of 90% and IoU of 83%. Besides, U-Net++ achieves high precision and recall of 98% and 70%, respectively. Although the results reveal that transfer learning-based U-Net models perform better, deployment of the trained model shows that the U-Net segments WBC more precisely than transfer learning-based U-Net models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Optimized Model Identification for Malware Detection in a File Using Machine Learning 3

Optimized Model Identification for Malware Detection in a Fi...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Laya, N. Shruthi Shetty, J. Sarojadevi, H. Sowmya, P. Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Malware remains one of the most critical digital threats today due to the rapidly evolving landscape of online activities. Detecting malware has become a pivotal aspect of safeguarding computer system, especially as polymeric malware arises by utilizing dynamic modifications to confound traditional signature-based detection methods. To combat the surging volume of malware, Machine Learning (ML) has emerged as a key strategy. Hence, ML-based detection mechanisms are imperative. This overview underscores the diverse array of machine learning-driven detection techniques. The primary objective is to construct an optimal machine learning representation for malware detection within files. Three distinct machine learning algorithms are employed: random forest, logistic regression, and a basic neural network. From these, the superior model is determined based on rigorous evaluation metrics-accuracy, f1 score, and both normalized and non-normalized confusion matrices on both training and testing data. The selection process hinges on achieving the highest accuracy and f1 score. The random forest algorithm was depicted to be having highest train accuracy with 98.28% and test accuracy with 98.38% and f1 score was found to be 97.3% which was chosen as a final model for malware detection in a file. The chosen model subsequently serves as the foundation for malware detection. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Meal Magic: An Image-Based Recipe-Generation System 8th

Meal Magic: An Image-Based Recipe-Generation System

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Sravya, Pemmasani Pariga, Swetha Swetha, S. Devi, Prasanna Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789819748945

The growing popularity of food photography on social media platforms has seen a surge in demand for recipe ideas and cooking inspiration. However, creating a recipe from scratch can be a daunting task, especially for someone with little cooking experience. To address this issue, our study proposes Meal Magic, a web-based image-to-recipe generator that generates recipes from the images of dishes. The image-based recipe creation system employs Inception v3, a Convolutional Neural Network (CNN) that achieves an accuracy of 78.1% when applied to the ImageNet dataset. It has a more efficient and deeper network and is computationally less expensive than the Inception v1 and v2 models. When compared to its predecessors, the model has an extremely low error rate. The web-based application is designed to be user-friendly, allowing users to simply upload an image of a dish that they wish to recreate and receive a recipe in return. As a result, it saves users’ time and streamlines the recipe creation process by eliminating the need for manual ingredient searching and recipe browsing. The image-to-recipe generator has many potential applications, including assisting home cooks in meal planning and aiding chefs in creating new recipes. Overall, this system represents a promising approach to the generation of recipes that has the potential to transform the way we approach cooking and recipe creation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Network Intrusion Detection System Using Ensemble Methods and Deep Neural Network 9

Network Intrusion Detection System Using Ensemble Methods an...

引用

9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Bandari, Sheethal Rangdal, Aakanksha Rukmanna Manisha, Budhi Shailaja, M. Sreenidhi Institute of Science And Technology Department of Electronics And Computer Engineering Hyderabad India

ISBN: (纸本)9798350365092

Today, we find almost everybody using internet and with increase in demand of computer networking, Hackers are taking advantage of this situation and trying to intrude into the networks and disturb the networks thus by injecting malwares into it. It is very important to identify whether the network is free from malware or not in order to ensure secrecy of valuable data. Intrusion Detection Systems (IDS) are some of the most prominent technologies for administrating and looking after security issues in the network. With rapidly changing network traffic data, one classifier is not enough to fend off modern network intruders. In this work, ensemble classifiers are used to classify data based on binary and multiclass. We took this as a major challenge to increase accuracy rates of detection for individual attack types and all types of attacks. This allows us to identify attacks and specific attack categories. We've chosen to contrast other works by opting for Artificial Networks as well. Python being the most versatile programming language in recent times, we chose IDS-ML an open source code repository in order to develop IDS from public network traffic records that can protect modern networks with high efficiency and accuracy. © 2024 IEEE.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Cosine and Jaccard Similarity-Based Classification for Detecting CAN Bus Attacks

Comparative Analysis of Cosine and Jaccard Similarity-Based ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Rai, Ritu Grover, Jyoti Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350364866

Connected vehicles have recently obtained significant attention for their potential to revolutionize the transportation industry. While this increased connectivity offers numerous benefits, it also expands the attack surface by introducing more physical access points. Specifically, the abnormal behavior of Electronic Control Units (ECUs) induced by attacks can result in severe implications, including potentially lethal accidents. Thus, it is essential to develop techniques that can monitor vehicular data and find anomalies for subsequent threat evaluation. As attacks targeting the CAN bus have increased, researchers have investigated intrusion detection systems (IDSs) to identify threats against the CAN bus. In this paper, we employ the Jaccard and cosine similarity scores to classify CAN bus data as normal or attack. We have considered four attack scenarios: DoS, fuzzy, replay, and spoofing collected from real vehicle. We have also utilized three publicly available datasets. The similarity score range and CAN ID act as features for the classification task, and logistic regression is applied to obtain the results. The proposed approach demonstrates the effectiveness of utilizing similarity measure and machine learning technique for detecting intrusions in CAN bus networks by achieving significant results in the detection task. © 2024 IEEE.

关键词： Buses

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：