检索结果-内蒙古大学图书馆

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

作者： Nainwal, Ankita Almusawi, Muntather Bansal, Saloni Michaelson, Jacob Kumar, Suresh Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study investigates the accuracy of load balancing calculations in terms of edge computing for IoT. The paper compared four main calculations, namely Round Robin (RR), Weighted Round Robin (WRR), Least Connections (LC) and Ant Colony Optimization ratings in an reproduced edge computing atmosphere. As for the study, it focused on efficiency improvements of asset utilization as well as reducing inactivity and enhancing reasonableness. Weighted Round Robin showed that the predominant execution came about with an astonishing 25% decrease in idleness as compared to a depiction of noncapacity aware task load distribution. The Slightest Associations also showed extraordinary outcomes, achieving 20% decrease in idle period and demonstrated that it could be flexible with workloads. While flexible, Ant Colony Optimization demonstrated an inability to achieve steady variation improvements related with parameter tuning ranging from 15% toward 30%. The discoveries emphasize the significance of calculation choice based on the particular prerequisites of IoT edge situations. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

WoEEE: a hybrid approach for enhancement of categorical data transformation

引用

International Journal of Data science and Analytics 2025年 1-29页

作者： M, Anitha Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Applications National Institute of Technology- Tiruchirappalli Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology- Tiruchirappalli Tiruchirappalli India

Efficient encoding of categorical data is essential for enhancing the performance of machine learning (ML) models, statistical methods, particularly when handling high-cardinality features and complex relationships between variables. Traditional encoding methods have notable limitations. One-hot encoding (OHE) often results in high-dimensional feature spaces, reducing model efficiency and predictive accuracy; label encoding (LE) can introduce misleading ordinal relationships; binary encoding (BE) may create excessively large feature representations for high-cardinality categories; weight of evidence (WoE) is prone to overfitting when rare categories are present; and entity embedding (EE) is less interpretable, and may perform poorly on unseen categories. To address these challenges, this paper introduces weight of evidence–entity embedding encoding (WoEEE), a novel encoding technique that integrates the strengths of both WoE and EE. WoE, a statistical approach, encodes categorical variables based on their influence on the target variable, while EE, a DL-based technique, transforms categories into continuous embeddings to capture complex, nonlinear relationships. The proposed WoEEE encoding method first applies WoE to identify and encode significant categories, followed by EE to generate low-dimensional embeddings. Comprehensive empirical evaluations were conducted on various binary and multiclass classification datasets to assess the effectiveness of the proposed WoEEE encoding method. The results, obtained using classifiers such as random forest (RF), Decision Tree (DT), logistic regression (LR), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), demonstrate that WoEEE improves performance over the conventional encoding techniques as well as advanced methods including fuzzification-based encoding, deep-learned embedding, deep feature embedding, and regularized target encoding. On the Adult dataset, WoEEE achieves an F1-score of 0.9

关键词：

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Networks for Intrusion Detection in IoT Networks with Limited Resources

Convolutional Neural Networks for Intrusion Detection in IoT...

引用

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

作者： Mittal, Mudit Al-Fatlawy, Ramy Riad Bansal, Saloni Michaelson, Jacob Bhalla, Lalit Veena, Ch. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

In this research, we explore the adequacy of Convolutional Neural Systems (CNNs) for interruption location in resource-constrained IoT systems. Leveraging two freely accessible datasets, we compare the execution of CNNs with conventional machine learning calculations such as Support Vector Machines (SVM) and Random Forests (RF). Our tests uncover that CNNs accomplish competitive location exactness, beating SVM and RF models over both datasets. Particularly, on Dataset A, CNNs accomplish an accuracy of 0.92, a review of 0.88, and an F1-score of 0.90, whereas, on Dataset B, they accomplish an accuracy of 0.86, a review of 0.84, and an F1-score of 0.85. Also, CNNs illustrate prevalent computational proficiency, with shorter preparing times and lower memory impressions compared to conventional calculations. This inquiry contributes to progressing the state-of-the-art in IoT interruption discovery by highlighting the viability of profound learning methods in tending to cybersecurity challenges in IoT situations. Moving forward, the selection of CNNs can upgrade the versatility and security of IoT systems, clearing the way for more strong and effective interruption location frameworks. © 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

To Detect Spammer from Image-Based Spam E-mail through Adjacent-Duplicate Technique

To Detect Spammer from Image-Based Spam E-mail through Adjac...

引用

2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Patel, Kavita Kumar, Sachin Singh, Vijendra Pratap Singh, Sandeep Kumar Saha, Tapash Kumar Jaiswal, Priya Ashoka Institute of Technology and Management Computer Science and Engineering Department UP Varanasi India Computer Science and Applications Veer Bahadur Singh Purvanchal University UP Jaunpur India Computer Science and Applications Mahatama Gandhi Kashi Vidyapeeth UP Varanasi India Computer Science & Engineering Kashi Institute of Technology UP Varanasi India Computer Science and Applications Kashi Institute of Technology UP Varanasi India

ISBN: (纸本)9781665456371

An Email facility proves to be efficient and more convenient communication way. Now a day, an internet service grows continuously, so information available to user is transformed from text to enriched multimedia. Where, spammer uses a new approach in which text is embedded in images. Widely known image detection algorithm such as optical character recognition (OCR) algorithm is defeated by spam image building techniques where randomization method is used to make sure the entire image is look different to each other. Spam image e-mails are mostly forwarded in a group that have visual similarity but dissimilar due to randomization technique used. The proposed system uses adjacent-duplicate detection technique to identify image-based spam. The system uses conventional spam detection technique to identify image-based spam and then further, use of numerous image-based spam filter to identify all the images which have similarity among them. The system is implemented to gain high identification rate where other side have false positive rate (FPR) is less than 0.001%. © 2022 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 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

Innovations in Mechanical Drawing and computer Graphics: Enhancing Precision and Visualization in engineering Design 4

Innovations in Mechanical Drawing and Computer Graphics: Enh...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shrivastava, Anurag Goyal, Himanshu Rai Hussein, Layth Bansal, Saloni Sharma, Vishal Harikrishna, M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331518981

This study examines the significant advancements in computer graphics and mechanical drawing, with an emphasis on how these developments have revolutionised engineering design precision and visualisation. For engineers and designers, integrating cutting-edge computer-aided design (CAD) tools and visualisation technologies has become crucial as sectors continue to adapt to issues that are ever more complex. The study focusses on significant developments that improve design accuracy and enable detailed visual representations of engineering concepts, including augmented reality (AR), simulation methods, and 3D modelling. Professionals can streamline the engineering workflow by using CAD software to produce complex drawings that are both exact and easily customisable. Before physical prototyping takes place, decision-making based on examination of the product's performance under different conditions is made possible by the integration of simulation modelling. Furthermore, by offering immersive experiences that let stakeholders see and interact with 3D models in actual settings, augmented reality applications transform design assessments. The research emphasises the importance of these advances in enhancing engineering teams' cooperation, efficiency, and communication through a thorough examination of them. The ultimate goal of this research is to pinpoint future paths for the integration of computer graphics and mechanical drawing, opening the door for improved engineering design and development processes. © 2024 IEEE.

关键词： Precision engineering

来源：评论

学校读者我要写书评

暂无评论

Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

引用

SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Self-Supervised Transfer Learning for Medical Image Classification 2

Deep Learning-Based Self-Supervised Transfer Learning for Me...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Shaikh, M.Z. Singh, Samender Varshney, Neeraj Saraswat, Birendra Kumar Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Ajay Kumar Garg Engineering College Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

Self-supervised switch getting to know is a practical approach for the scientific image type. It includes using unlabeled statistics (from non-clinical images) to teach models to categorize medical photos with low attempts and excessive accuracy. This approach has been utilized in numerous duties, including clinical photo segmentation, computer-aided prognosis, and disease category. To summarize the research studies carried out in this discipline, a complete survey of self-supervised switch learning for scientific image type was performed in 2020. The survey evaluations numerous transfer mastering techniques often used in healthcare, which include area adaptation, multitasking studying, and zero/one-shot mastering. It additionally gives an in-intensity analysis of the modern-day challenges and capability solutions. © 2024 IEEE.

关键词： Medical Outperforms Results Unlabeled Utilizes

来源：评论

学校读者我要写书评

暂无评论

Security model to identify block withholding attack in blockchain

Security model to identify block withholding attack in block...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Keshta, Ismail Reegu, Faheem Ahmad Ahmad, Adeel Saxena, Archana Chandan, Radha Raman Mahalakshmi, V. Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Computer Applications Invertis University UP Bareilly India Department of Computer Science and Engineering Shambhunath Institute of Engineering and Technology College Jhalwa Prayagraj India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781032669663

Block interception attack, also known as block withholding attack, is an attack method in the blockchain. The attacker penetrates the target mining pool for passive mining to destroy the target mining pool. This paper briefly introduces the mining mechanism and the working principle of block interception attack, summarizes several models of block interception attack, studies the attack methods and benefits of the existing block interception attack models, and analyses their attack effects. A block interception attack model is constructed to increase the rate of return while increasing the rate of return. The completed model is verified through simulated mining experiments. The typical and made models are compared and analyzed based on the rate of return. Finally, the applicable environments of different attack models are given based on the experimental results. © 2024 The Author(s).

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Novel Ant Colony Optimization Algorithm for Dynamic Routing in Communication Networks

A Novel Ant Colony Optimization Algorithm for Dynamic Routin...

引用

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

作者： Kansal, Vipashi Al-Farouni, Mohammed Bansal, Saloni Michaelson, Jacob Kumar, Suresh Veena, C.H. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study on the evolving routing terrain of communication networks centers around improving ACO algorithms. A new method, Modified Ant Colony Optimization (MACO) is developed using dynamic pheromone updates according to current network states. The GA, PSO, and traditional ACO narrations on comparative analysis provide further evidence of MACO's superior adaptability of routing decisions in scenarios where the network conditions change quickly. We validate the proposed MACO algorithm in multiple folds using a typical dataset. Output results show a noticeable enhancement in network throughput, reaching extremely high values that are very low in terms of latency. Packet delivery ratios present better results that further point to the efficiency of MACO in providing successful information transfer. These results make MACO a strong candidate to solve the issues of unstable routing in communication networks. © 2024 IEEE.

关键词： Ant colony optimization

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：