检索结果-内蒙古大学图书馆

Stock Indexes Community Identification Using BAT-Modified Optimization Algorithm

SN computer science 2024年第8期5卷 1-14页

作者： Aggarwal, Kirti Arora, Anuja Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock price accelerates interest and preference of the young generation to explore the stock market with elicit interest. An autopilot system is needed where users choose beneficial stocks of their choice without paying attention to the manual inspection or implication. This study aims to form highly correlated stock communities based on fluctuations in stock price which will be helpful in the prediction and decision-making of users about their expenditure in the stock market. For the enormous volume of stock price time series data, the opted data representation method is the network which is generated with the help of a data statistical correlation test. Another research challenge was to detect the community in an optimized manner due to complex stock networks. For the same, BAT optimization algorithm and a proposed BAT-modified (BAT-M) algorithm are applied for complex stock network community formation. The better community formation results showcase that BAT-M gives better performance in comparison to the standard BAT algorithm on various performance evaluation factors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BAT Community detection Correlation Modularity Nature-inspired Optimization Stock indexes Stock network

来源：评论

学校读者我要写书评

暂无评论

Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging

引用

Biomedical Materials and Devices 2025年 1-19页

作者： Shanmugaveni, V. Jotheeswari, M. Abarnaswara, R. VijayaKumar, M. Manojanani, A. Department of Computer Science and Engineering (Cyber Security) Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Artificial Intelligence & Data Science Dr. Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Computer Science and Engineering RVS Technical Campus Tamil Nadu Coimbatore India

The diagnosis of Ovarian Tumor (OT) remains a significant challenge as there is presently no practical non-invasive technique to determine true benign or malignant lesions before treatment. This study proposes a unique Deformable Dual Graph Aggregation Transformer Convolutional Network (DeDGATCNet) with Spider Wasp Optimizer (SWO) for the classification of ovarian tumors acquired from magnetic resonance imaging (MRI). In contrast to traditional deep learning models, DeDGATCNet + SWO directly integrates bending graph-based forming transformer convolutional learning and improves representation for better classification. The SWO is combined to optimize the parameters of the network, thereby improving its performance. The procedures include pre-processing the MRI data from the study of ovarian tumor TCGA-OV datasets with an Adaptive Bilateral Texture Filter (ABTF) for removing artifacts and normalization of intensity, accompanied by a segment using a Masked-Attention Mask Transformer (MAMT). Feature extraction is achieved through a One-Dimensional Quaternion Discrete Fourier Transform (1D-QDFT), which captures features from both the spatial and frequency domains. DeDGATCNet + SWO performance is way ahead and sets a new accuracy record of 99.9% for state-of-the-art models. This research introduces a new type of hybridization between deformable graph transformers, quaternion-based spectral analysis, and nature-inspired optimization techniques, setting non-invasive OT at a new height for classification and improving early diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive bilateral texture filter Deformable dual graph aggregation transformer convolutional networks Masked-attention mask transformer One-dimensional quaternion discrete fourier transform

来源：评论

学校读者我要写书评

暂无评论

Empirical techniques for effort estimation in designing effective ML models

引用

International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padmanabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine Learning Research often involves the use of diverse libraries, modules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cumulative processes is rarely discussed in the existing works and The time to reach the desired level of the model's functionality is essential to gauge the environment training for the model training and pre-deployment testing. In this study, we empirically defined the manual-cum-computational effort required for the model development in terms of 2-time factors: time-to-live (time until 1st code modification) and time-for-modification (time between 2 codebase changes) taken together in a mathematical model to compute Effort Factor(quantitative measure of determining effort needed to design ML algorithms). The study is novel in terms of how the effort required to create ML models is calculated with a particular focus on the manual effort direction. The results and the findings obtained can be used for determining the total time needed to synthesize ML models and frameworks in terms of code change cycles and implementation strategies marking a 25% performance increase in the current method above the Standard Pipeline. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pipeline codes

来源：评论

学校读者我要写书评

暂无评论

Advancing Bilingual SMS Spam Detection: A Comparative Exploration of Machine Learning and Deep Ensemble Techniques 3

Advancing Bilingual SMS Spam Detection: A Comparative Explor...

引用

3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Sultana, Hafsa Tanvin, Jamal Uddin Sharmin, Nusrat Arefin, Mohammad Shamsul Rahman, Md. Mahbubur Military Institute of Science and Technology Mirpur Cantonment Department of Computer Science and Engineering Dhaka1216 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331534400

Spam messages are increasingly becoming a major issue in digital communication, creating significant challenges for both users and organizations. With the rise in text-based interactions, especially in low-resource languages like Bangla-English SMS, there is a growing need for more effective spam detection systems. This paper presents BESpam, a unique bilingual dataset that contains 5,730 Bangla-English SMS messages, specifically designed to address this challenge. In the literature, various machine learning and deep learning techniques align with feature extraction techniques from natural language processing used for spam SMS detection. Still, no studies have been found that focus on comparing deep and machine learning approaches. As a further contribution, we propose an ensemble model that combines advanced transformer architectures with FastText to improve the accuracy of spam classification. Our results demonstrate that this ensemble approach outperforms both traditional machine learning models and individual transformer-based methods. The stacking ensemble achieved an accuracy of 99%, exceeding the performance of standalone models such as mBERT (98%) and XLM-RoBERTa (98%), as well as traditional classifiers such as logistic regression and SVM, both of which also scored 98%. This work provides insight analysis of machine learning and deep learning in terms of bilingual SMS spam detection, in addition to advancing spam detection methodologies. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Existing SDN Load Balancing Controller Using Mininet 1

引用

International Conference on Advanced engineering, technology and Applications, ICAETA 2024

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Agrawal, Atul Department of Computer Science and Engineering School of Engineeringand Technology IEC College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology ITS Engineering College Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Due to an increase in the load of network, load balancing service, i.e., a service that gives an equal volume of each task assignment to each of the servers in data centers, it is usually performed by the specialized hardware. Networks have to adjust due to the load and also as a result of dealing with faults and complexity which are provided in the implementation and configuration process. Network engineers or administrators must be aware of selecting controllers according to the network requirement, which can lead to performance issues, scalability challenges, reliability and fault tolerance and many more problems in the network. To tackle this problem, this research analyses four different controllers in an experiment: In-band, open flow, OVS, and remote controller, which are the key aspects. The paper shows how these controllers, which are detected through a comprehensive evaluation of network performance attributes (e.g., Throughput and Jitter), affect the overall system’s performance. This information provides beneficial information for the researchers and practitioners who wish to make informed choices whenever they select suitable controls for specific network needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Remote control

来源：评论

学校读者我要写书评

暂无评论

Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security 1

Proposed AES-based Obfuscation and Encryption Model for Clou...

引用

1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Singh, Prabhdeep Singh, Pawan Agarwal, Abhay Kumar Kumar, Anil Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow India Kamla Nehru Institute of Technology Department of Computer Science and Engineering Sultanpur India Amity University Amity School of Engineering and Technology Lucknow India

ISBN: (纸本)9798331529246

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained. © 2025 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

SAFARM: simulated annealing based framework for association rule mining

引用

International Journal of Information technology (Singapore) 2025年第3期17卷 1523-1532页

作者： Kaur, Preeti Goel, Sujal Tyagi, Aryan Malik, Sharil Shrivastava, Utkarsh Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

The research paper introduces an algorithm called SAFARM which performs association rule mining with the help of simulated annealing. It’s a multi-objective problem with vast search space. The suggested approach is independent of the database as it does not require minimum support or minimum confidence specification. In the algorithm, a fitness function is designed to fulfill the required objective and the presentation of rules is proposed with a compact structure. The correctness and efficiency of the algorithm is verified by testing it on synthetic and real databases. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Association rules Data mining Evolutionary algorithm Simulated annealing

来源：评论

学校读者我要写书评

暂无评论

Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

引用

computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

来源：评论

学校读者我要写书评

暂无评论

Optimizing Parkinson’s Disease Detection: Hybrid S-transform-EEG Feature Reduction Through Trajectory Analysis

引用

SN computer science 2025年第2期6卷 1-14页

作者： Afonso, Melina Maria Edla, Damodar Reddy Reddy, R. Ravinder Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim 403703 India Department of Computer Engineering Goa College of Engineering Farmagudi Goa Ponda 403401 India Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad 500075 India

With Parkinson’s disease (PD) becoming more common, there is an increasing demand for accurate technology-driven PD detection. This research attempts to address this requirement. Because of its accessibility, use, and non-invasiveness, electroencephalography (EEG) has shown to be a potential technique for detection. Extraction of a wide range of features from the intricate EEG signals of Parkinson’s disease patients has been made possible by utilizing deep learning algorithms and transformation methods, enabling comprehensive analysis. But this data is enormous and presents computational difficulties, particularly when developing accessible and detection systems. To address this, the research proposes a feature reduction model that uses a hybrid approach to cut down the computational complexity. This approach combines four different feature scoring methods-ANOVA F-value, Mutual Information, Chi-square, and linear regression F-value-to generate a unified ranking score for each feature extracted from S-transformed EEG images. This score is thus generated leveraging the strengths of the diverse mechanisms and used to sort the features. The model decreases the feature set depending on these scores and utilizes a deep neural network to detect PD. This methodically lowers the feature count while monitoring the impact on the accuracy of the neural network. Finding the ideal feature combination is the goal, but maintaining the capacity to detect between people who have and don’t have Parkinson’s disease is a must. By balancing diagnostic accuracy and computational efficiency, the suggested technique ultimately yields accuracy rates of 95.94% and 97.31% on two different datasets with the reduced features, which outperforms other state-of-the art optimization algorithms compared in this research. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brain computer interface Deep learning Electroencephalography Feature reduction Parkinson’s disease Stockwell-transform

来源：评论

学校读者我要写书评

暂无评论

A Novel Intelligent Task Offloading Scheme for Multi-Controller Environment in Software Defined Internet of Vehicles

引用

IEEE Internet of Things Journal 2025年第12期12卷 20018-20029页

作者： Hussain, Mir Wajahat Sangaiah, Arun Kumar Reddy, K. Hemant Kumar Roy, Diptendu Sinha Alenazi, Mohammed J.F. Javvaji, Pavan Kumar Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Anekal562106 India International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Meghalaya 793003 India College of Computer and Information Sciences Department of Computer Engineering King Saud University Riyadh11451 Saudi Arabia

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing platform meets these stringent computation requirements, but it has a significant latency that fog computing effectively handles. Software Defined Network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network pre-requisites in an agile manner. Task Offloading (TO) is a crucial issue in Software Defined-IoV (SDIoV), particularly when the vehicle's resources are insufficient. Fog nodes schedule offloaded tasks;however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this paper introduces two proactive Intelligent Task Offloading (ITO' and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO' and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7-13.9)% more than the existing scheme. They also reduce average packet loss, delay, Round Trip Time (RTT), and offloaded node time selection by (45.35-21.3)%, (50-33.3)%, (53.9-9.9)% and (43.2-26.06)% which demonstrates the efficacy of the proposed scheme. © 2025 IEEE.

关键词： Fog computing

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：