检索结果-内蒙古大学图书馆

6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Akash, Rashik Shahriar Ullah, Mohammad Islam, Radiful Nahid, Sayed Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031501500

In the modern economic era, households have become one of the most significant corporate entities, and personal finances are essential management-related topics. Effective management of personal finances at a family level is really important to address all the financial related issues in an organized way. Any automated budget management system can help a family to utilize their incoming income in an efficient way. In this paper, we try to address the potential researches that were carried out considering the budget management related issues with a strong focus on family budget management. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Finance

来源：评论

学校读者我要写书评

暂无评论

Forensic Eye: Convolutional Neural Network-based Crime Scene Object Detection System 2

Forensic Eye: Convolutional Neural Network-based Crime Scene...

引用

2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sudha, I. Vadivel, M. Lakshmiramana, P. Kalpana, N. Kumar, Nellore Manoj Vinoth, R. Saveethe University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science Tamilnadu Chennai India Excel Engineering College Department of Information Technology Komarapalayam Namakkal India Madanapalle Institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh India Psna College of Engineering and Technology Department of Computer Science and Engineering India Saveetha School of Engineering Department of Mathematics Tamilnadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal India

ISBN: (纸本)9798350364699

Forensic investigations rely heavily on the accurate detection and analysis of crime related objects present at crime scenes. Traditional methods of object detection often involve manual labor and are error prone process to human error. In recent years, Convolutional Neural Networks (CNNs) have shown remarkable success in various computer vision tasks, including in object detection. In this article, we introduce ForensicEye, a novel CNN-based crime scene object detection system designed to assist forensic investigators in identifying the objects related to the crime. It utilizes a state-of-the-art CNN architecture trained on a large dataset of crime scene images, enabling robust detection even in challenging lighting and environmental conditions. We evaluated the performance of the ForensicEye on benchmark datasets with various preprocessing techniques, demonstrating its superior accuracy and efficiency compared to traditional methods. Our proposed model achieved the accuracy of 96.2%, precision of 97.9%, Recall of 96.9%, F1-score of 97.4%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Encrypted Malicious Network Traffic Detection: Leveraging Attention Mechanism and Markov Chain Sequencing

Encrypted Malicious Network Traffic Detection: Leveraging At...

引用

2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Zeleke, Sileshi Nibret Jember, Amsalu Fentie Bochicchio, Mario University of Bari Department of Computer Science Bari Italy University of Gondar Departement of Computer Engineering Gondar Ethiopia

ISBN: (纸本)9798331527792

Encrypted malware traffic presents a considerable challenge, as traditional signature-based detection methods are incapable of inspecting the contents of such traffic, while deep packet inspection systems are often criticized for compromising the privacy of the payload. In this study, we propose a novel approach for detecting encrypted malware through the application of Markov chain (MC) sequencing of packet inter-arrival times and lengths. We utilize a temporal convolutional network (TCN) integrated with a multi-head attention mechanism to effectively capture dependencies within the dynamics of network traffic. The attention mechanism allows the model to concentrate on critical state transitions. We evaluate our approach using an encrypted network traffic dataset, and the results indicate that it achieves an accuracy of 93.08%, an F1-score of 93.13%, a precision of 93.98%, and a recall of 92.29%. Our method demonstrates high accuracy, precision, and F1-score, surpassing several baseline models. These results highlight the effectiveness and practical applicability of our proposed approach in detecting encrypted malware, thereby providing a robust solution to the challenges posed by encrypted communications in the field of cybersecurity. © 2024 IEEE.

关键词： Markov chains

来源：评论

学校读者我要写书评

暂无评论

Employee Attrition Analysis Using XGBoost

Employee Attrition Analysis Using XGBoost

引用

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Isha Thapliyal, Nitin Solanki, Sheetal Pandey, Neeraj Kumar Papola, Sangita Graphic Era Hill University Department Of Computer Science Dehradun India Graphic Era Deemed To Be University Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

In today's time, employee attrition is a major problem faced by organisations. The most precious resource for a firm is its workforce. They are the ones that bring values both quantitatively and qualitatively. A steady decline in the workforce due to resignation, death or retirement is referred to as attrition. It is a matter of concern when employees leave their jobs of better opportunities, but this leads the company to face its consequences. Employee turnover and attrition are equivalent. Leaders can avoid it by implementing better retention techniques by mastering this concept. In this paper, we use dataset to analyse attrition and determine the root cause of employee resignation. We did analysis of the training dataset for effective data exploration. Machine Learning algorithms, such as Random Forest Classifier, Logistic Regression, XGBoost and ensemble stacking technique were used. On data exploration and enabling algorithm we discovered that XGBoost classifier with an accuracy of 88. % and precision 89% was the most appropriate algorithm for this dataset. We may also conclude that monthly income, age, distance from home can be a reason behind employees leaving their job and finding better opportunities. © 2024 IEEE.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Effective Deep Learning Techniques for Prediction of Energy Consumption in Smart Buildings 2

Effective Deep Learning Techniques for Prediction of Energy ...

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Beura, Sonali Mohanty, Sanjukta Mohapatra, Sunil Kumar Panda, Namita Acharya, Arup Abhinna Computer Science and Engineering Odisha University of Research and Technology Bhubaneswar India Centurion University of Technology and Management Department of Computer Science & Engineering Bhubaneswar India School of Computer Engineering Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350370188

Energy consumption is an important aspect of the energy management system. Over the past few decades, it has been recorded that there has been a drastic increase in global energy use. As a result, every country develops different approaches to reducing energy consumption in different sectors. Recently deep learning models are the most effective in the field of prediction. For estimating power consumption, this work employs the LSTM model to identify an accurate pattern of time series data. After the model has been trained, it is finally put to examine the future trend. Applying a standard hourly energy consumption data set, we conducted the analysis and used the R2-Score and Mean Square Logarithmic error (MSLE) metric to find the error. © 2024 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Exploration of Structural Virality and Influencing Elements in Online Information Diffusal 1

AI-Driven Exploration of Structural Virality and Influencing...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Kaur, Ripandeep Sahu, Rakesh Sandhu, Jasminder Kaur Chandigarh University Department of Computer Science & Engineering Punjab Mohali140413 India Galgotias University Department of Computer Science & Engineering Uttar Pradesh Greater Noida203201 India

ISBN: (纸本)9798350361780

Structural virality is a measure for how much information spreads across multiple user generations. It is important because it can help us better understand how information spreads online and develop new, useful techniques for influencing and limiting it. The application of recent developments in artificial intelligence to the analysis and understanding of the structural virality of online information diffusion is discussed in this paper. In the paper, the proposed techniques are verified as well and compared against other state-of-the-art approaches. The study's conclusions suggest that artificial intelligence (AI) possesses the potential to significantly expand our understanding of how information diffuses online and to develop novel, useful strategies for regulating and influencing the diffusion. © 2024 IEEE.

关键词： Information dissemination

来源：评论

学校读者我要写书评

暂无评论

Corrections to "Reed Solomon Codes Against Adversarial Insertions and Deletions"

引用

IEEE Transactions on Information Theory 2025年第4期71卷 3237-3238页

作者： Con, Roni Shpilka, Amir Tamo, Itzhak Technion – Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Tel Aviv University Blavatnik School of Computer Science Tel Aviv6997801 Israel Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv6997801 Israel

The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced CNN-SVM Fusion for In-Depth Facial Acne Analysis 3

Enhanced CNN-SVM Fusion for In-Depth Facial Acne Analysis

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Prabha, Chander Monga, Sakshra Srivastava, Prakash Singla, Gagan Saluja, Nitin Kumar, Vinod Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science & Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350384598

The purpose of this work is to present and access a comprehensive analysis of an advanced facial acne classification method that is focused on machine learning. Hormonal acne, pustules, papules, cysts, blackheads (open comedones), and whiteheads (closed comedones) were all included in the dataset that was used to train and evaluate the algorithm. Accuracy, recall, and F1-Score provide evidence that the model correctly recognizes a variety of acne types, with scores ranging from 93.98% to 95.86%. Other evaluation metrics also suggest that the model displays this accuracy and efficiency. As an additional point of interest, the support percentage demonstrates how samples are spread within each acne class, hence demonstrating the balanced distribution of classes within the dataset. The model demonstrated that it was very accurate, obtaining a total score of 98% for its performance. In addition, aggregate performance metrics such as micro, weighted, and macro averages provide further evidence that the algorithm is capable of providing a diagnosis of acne that is both accurate and trustworthy across all types of acne. The research contributes to the development of automated systems for detecting acne on the face, and dermatologists may find it beneficial to utilize the data acquired to improve their ability to diagnose and treat patients who suffer from facial acne and health issues. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

引用

Materials Today: Proceedings 2023年 80卷 3769-3776页

作者： Gite, Pratik Chouhan, Kuldeep Murali Krishna, K. Kumar Nayak, Chinmaya Soni, Mukesh Shrivastava, Amit Department of Computer Science and Engineering IPSA-IES Indore AB Road MP Indore India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Faculty of Emerging Technology Sri Sri University Odisha751001 India Department of Computer Science & Engineering Jagran Lakecity University Bhopal India Department of Computer Science Engineering Swami Vivekanand College of Engineering Khandwa Road Near Tolnaka Vivekanand Knowledge City Indore India

A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network's harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR). © 2021

关键词： Base stations

来源：评论

学校读者我要写书评

暂无评论

List 3-Coloring on Comb-Convex and Caterpillar-Convex Bipartite Graphs 29th

List 3-Coloring on Comb-Convex and Caterpillar-Convex Bipa...

引用

29th International Computing and Combinatorics Conference, COCOON 2023

作者： Baklan Şen, Banu Yaşar Diner, Öznur Erlebach, Thomas Computer Engineering Department Kadir Has University Istanbul Turkey Department of Computer Science Durham University Durham United Kingdom

ISBN: (纸本)9783031491894

Given a graph G= (V, E) and a list of available colors L(v) for each vertex v∈ V, where L(v) ⊆ { 1, 2, …, k}, List k -Coloring refers to the problem of assigning colors to the vertices of G so that each vertex receives a color from its own list and no two neighboring vertices receive the same color. The decision version of the problem List 3-Coloring is NP-complete even for bipartite graphs, and its complexity on comb-convex bipartite graphs has been an open problem. We give a polynomial-time algorithm to solve List 3-Coloring for caterpillar-convex bipartite graphs, a superclass of comb-convex bipartite graphs. We also give a polynomial-time recognition algorithm for the class of caterpillar-convex bipartite graphs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Color

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：