检索结果-内蒙古大学图书馆

Dementia diagnosis in young adults: a machine learning and optimization approach

Neural Computing and Applications 2024年第34期36卷 21451-21464页

作者： Talaat, Fatma M. Ibraheem, Mai Ramadan Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt Information Technology Department Faculty of Computers and Information Kafrelsheiksh University Kafrelsheikh Egypt

Individuals who are younger and have dementia often start experiencing its symptoms before they turn 65, with cases even documented in people as young as their thirties. Researchers strive for accurate dementia diagnosis to slow or halt its progression. This paper presents a novel Enhanced Dementia Detection and Classification Model (EDCM) comprised of four modules: data acquisition, preprocessing, hyperparameter optimization, and feature extraction/classification. Notably, the model uses texture information from segmented brain images for improved feature extraction, leading to significant gains in both binary and multi-class classification. This is achieved by selecting optimal features via a Gray Wolf Optimization (GWO)-driven enhancement model. Results demonstrate substantial accuracy improvements after optimization. For instance, using an Extra Tree Classifier for "normal" cases, the model achieves 85% accuracy before optimization. However, with GWO-optimized features and hyperparameters, the accuracy jumps to 97%. © The Author(s) 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

To Kill a Mockingbird: Cryptanalysis of an Authenticated Key Exchange Scheme for Drones 15

To Kill a Mockingbird: Cryptanalysis of an Authenticated Key...

引用

15th International Conference on information and Knowledge technology, IKT 2024

作者： Toghraee, Neda Mala, Hamid Dept. of Information Technology Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Drones have evolved into sophisticated autonomous systems with a multitude of applications, including military operations, environmental monitoring, traffic oversight, data transmission, package delivery, fire targeting, and film production. As the Internet of Drones (IoD) ecosystem expands, ensuring secure and real-time access for authorized users becomes increasingly vital. User and drone authentication is crucial for this reason. In response to these challenges, Srinivas et al. proposed a lightweight three-factor authentication protocol designed for the IoD. While the authors assert that their protocol is resilient against known cyber threats, our research identifies critical vulnerabilities that challenge this claim. Although this scheme has already been cryptanalyzed in previous studies, in this paper we propose two new attacks on this scheme. Firstly, we present a concrete attack against the perfect forward secrecy of this scheme, and then we show that it is vulnerable to unauthorized access attack by a valid user to an unauthorized area's information. These weaknesses highlight the pressing need for the development of more secure authentication mechanisms in the IoD environment. Moving forward, addressing these vulnerabilities will be essential for fostering trust and ensuring the safe integration of drones into various applications, ultimately contributing to the advancement of IoD technology. © 2024 IEEE.

关键词： Authentication Protocol

来源：评论

学校读者我要写书评

暂无评论

Multi-strategy Hybrid Coati Optimizer:A Case Study of Prediction of Average Daily Electricity Consumption in China

引用

Journal of Bionic engineering 2024年第5期21卷 2540-2568页

作者： Gang Hu Sa Wang Essam H.Houssein Department of Applied Mathematics Xi’an University of TechnologyXi’an710054China School of Computer Science and Engineering Xi’an University of TechnologyXi’an710048China Faculty of Computers and Information Minia UniversityMinia61519Egypt

The power sector is an important factor in ensuring the development of the national *** simulation and prediction of power consumption help achieve the balance between power generation and power *** this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity ***,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning *** improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the ***,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test ***,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting *** experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.

关键词： Forecast of average daily electricity consumption in China Coati optimization algorithm Three-parameter combination optimization model Circle chaotic mapping

来源：评论

学校读者我要写书评

暂无评论

Fine-grained Energy-efficient Data Center Traffic Scheduling Mechanism Based on Hierarchical Control

Journal of Network Intelligence

引用

Journal of Network Intelligence 2023年第3期8卷 742-757页

作者： Niu, Tianyu Li, Honghui Li, Weidong Por, Lip Yee College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Hohhot China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

With the continuous expansion of the data center, its energy consumption is also increasing. Aiming at the problem that the high redundancy of modern data center network causes low energy-consumption utilization, this paper proposes an energy-saving traffic scheduling algorithm (IMV-MLF) based on improved multi-layer-virtual-topology combined with maximum link-utilization path priority, which realizes more fine-grained control of each layer of virtual topology in data center network. Firstly, the relevant shap-ing linear programming mathematical model is established according to the optimization goal of energy conservation, which can minimize the energy consumption of the whole network as far as possible under the premise of meeting all network-flow requirements;Then, according to the optimization goal, the existing SDN based energy saving multi-layer-virtual-topology (EMV-SDN) method is improved. Whether the network devices in other layers are enabled is determined by the overall traffic level rather than the traffic level of a single link. On the basis of the opened level, the newly arrived network flows are preferentially allocated to the path with the highest link utilization, and overload link set is introduced, we can effectively use the available link routes with sufficient residual bandwidth to reduce congestion when the topology changes by marking the links whose link utilization reaches the critical value, and further count and close the links and switches which no network flows pass through. The experimental results show that the performance of IMV-MLF algorithm is better than multi-layer virtual topology energy saving (EMV-SDN) algorithm and the equivalent multipath (ECMP) algorithm in saving network energy. This result is helpful to provide another new idea for energy conservation of data center. © 2023, Journal of Network Intelligence.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

引用

computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

来源：评论

学校读者我要写书评

暂无评论

A review on spreading and Forensics Analysis of Windows-Based ransomware

引用

Annals of Data Science 2024年第5期11卷 1503-1524页

作者： Chayal, Narendrakumar Mangilal Saxena, Ankur Khan, Rijwan Department of Computer Science and Technology Faculty of Engineering and Technology Department of Computer Engineering Sankalchand Patel University Visnagar India Department of Computer Science Indus Institute of Information and Communication Technology Dean-ICT & ampDirector-IIICT Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad Affiliated to AKTU Lucknow India

Ransomware is one of the most advanced malware which uses high computer resources and services to encrypt system data once it infects a system and causes large financial data losses to the organization and individuals. There are certain automatic ransomware detection and analysis strategies available nowadays. File system analysis reveals some essential patterns and artifacts that can be very useful to understand its behavior spreading mechanism, taxonomy for malware forensics experts. Current trend explores Ransomware as a service (RaaS) and Malware as a service (MaaS) on Darknet. This paper reveals a theory of digital forensic methodology to identify the spreading/infection mechanism and attack path, the cryptographic methodology, windows services, process, APIs, persistence mechanism, and system lockdown strategies and malware analysis methodology. This review could be helpful to learn and understand malware forensic analysis for threat researchers, students, cyber experts, etc. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

High performance of Dengue shock syndrome detection using extreme gradient boosting with ANOVA feature selection

Journal of Biotech Research

引用

Journal of Biotech Research 2024年 16卷 22-31页

作者： Muflikhah, Lailil Iskandar, Agustin Yudistira, Novanto Nadlori, Isbat Uzzin Dewanto, Bambang Nur Faculty of Computer Science Brawijaya University Malang India Faculty of Medicine Brawijaya University Malang India Department of Informatics Engineering and Computer Politeknik Elektronika Negeri Surabaya India Faculty of Technology Information Merdeka University Malang India

Dengue shock syndrome (DSS) is an infectious disease that affects millions of people every year all over the world. Early detection of DSS is essential for providing effective therapy and promoting patient recovery. In this work, we proposed a method to enhance DSS detection by combining the Extreme Gradient Boosting (XGBoost) algorithm with variance (ANOVA) feature selection analysis. We used a clinical dataset that contained important information gleaned from people with dengue virus infection. The dataset used for the research was collected from patients at Syaiful Anwar Hospital and consisted of 501 instances. Of these, 401 cases were related to DSS, while the other instances were unrelated to this specific medical condition. An analysis of variance (ANOVA) evaluated the most significant factors that distinguished persons with DSS from those with other dengue diseases. After that, the XGBoost model gave the characteristics of the selected features. For evaluation, we split the data into 80% and 20% for training and testing, respectively. The experimental result showed that the created model had a high degree of performance evaluation in detecting DSS. The highest performance achieved an accuracy of 0.839, precision of 0.875, recall of 0.92, and f1-score of 0.897. Most importantly, the model could detect DSS early on, enabling more proactive therapy and faster responses for individuals at risk of developing the condition. © (2024), (Bio Tech System). All Rights Reserved.

关键词： Patient treatment

来源：评论

学校读者我要写书评

暂无评论

Bidirectional attentional mechanism for Arabic chatbot

引用

International Journal of information technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

来源：评论

学校读者我要写书评

暂无评论

Enhancing Image Security with Rossler Attractor-Based Encryption 2

Enhancing Image Security with Rossler Attractor-Based Encryp...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia University Of Petra Faculty Of Information Technology Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

we introduced image encryption algorithms with high sensitivity, such that even a single alteration in a plain-text image would result in a complete transformation of the ciphered image. The first algorithm employed pixel values in a substitution approach, whereas the second algorithm utilized pixel values to generate chaotic function keys. Additionally, we employed a Rossler chaotic function with pixel data to generate chaotic values for the shuffling and substitution processes. After subjecting these algorithms to rigorous testing using standard metrics, our experiments revealed the efficiency of the algorithms in image encryption. Notably, even a minute change in the original image resulted in a vastly different ciphered image. These proposed algorithms underscored the effectiveness of integrating chaotic functions with pixel information, significantly enhancing image security. © 2024 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

引用

5th International Conference on Advances in information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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：