检索结果-内蒙古大学图书馆

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

作者： Nithya, R. Venkatasalam, K. Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798331529246

When designing wireless sensor networks, security and energy efficiency are regarded as crucial considerations. Since nodes in wireless networks run on power sources, it is crucial to create an energy-efficient technique to reduce energy usage and increase the lifespan of the network. This research introduces BC-RSA, an energy-effective and reliable clustering-based data packet transmission technique that combines blockchain-enabled safe data transfer with a clustering technique based on the reptile search algorithm (RSA) in wireless sensor networks. The clustering process is carried out by the suggested BC-RSA technique employing the RSA technology after node initialization. Cluster head identification and cluster building are done via the clustering approach. Blockchain-driven protected data transmission between cluster members and heads occurs using the proposed approach. The BC-RSA technique's experimental validation is evaluated from a number of viewpoints, and the outcomes are contrasted with those of other existing approaches. The results acquired confirmed that the BC-RSA technique has demonstrated higher performance in terms of latency, productivity, efficacy, network lifespan, and distribution ratio. © 2025 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

引用

Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Behavior-Based Machine Learning Approaches for Malware Detection 2

Behavior-Based Machine Learning Approaches for Malware Detec...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Srivastava, Vivek Khare, Manya Kansal, Manasvi Harsh Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

The present research investigates the implementation of various methods of machine learning to analyze fluid information from executable files for the purpose identify contamination. We extracted feature vectors from a dataset that contained both malicious and benign samples. Standard metrics like Fl-score, recall, and precision were used to train and evaluate a range of classifiers, including Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression. Our test findings show that the models successfully distinguish between harmful and benign files, with Random Forest providing the highest level of consistency in all evaluation measures. These Results demonstrate the promise of machine learning methods to improve systems for detecting malware. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Object Detection Robustness in Adverse Weather Conditions 4

Enhancing Object Detection Robustness in Adverse Weather Con...

引用

4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Yacoob, Shaik Kumar, D. Phani Harshitha, G.G.S. Teja, V.Y. Sai Veni, C.H. Satya Reddy, K. Mahidhar Department of Computer Science and Engineering Godavari Global University Rajahmahendravaram533296 India Department of Computer Science and Engineering Godavari Institute of Engineering & Technology Rajahmahendravaram533896 India

Fundamental applications in autonomous cars, robots, and surveillance systems include object detection for safety reasons such as recognizing vehicles, pedestrians, trees, street poles, and buildings. However, one of the major challenges it faces from adverse weather conditions like rain, haze, and fog through deterioration of accuracy and an increase in safety risks such as traffic accidents. This paper proposes an enhanced object detection system to improve its robustness and accuracy in challenging environments. The proposed approach effectively addresses the problem of poor image quality due to adverse weather conditions by using unsupervised domain adaptation and including a Quasi-Translation Network and Feature Calibration Network in the YOLO(You Only Look Once) framework. It attempts to leverage the success made in CNNs(Convolutional Neural Networks) and mass amounts of datasets in order to adapt YOLO-series detectors in the best possible way, achieving object detection under challenging environmental conditions with precision and speed. Experimental results demonstrate that the solution proposed outperforms existing models in terms of adaptability and accuracy, providing a scalable and reliable solution for autonomous systems to enhance overall safety and operational efficiency in real-world environments. © 2025 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Mental Wellbeing Among College Students Using Autogen

Enhancing Mental Wellbeing Among College Students Using Auto...

引用

2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Reddy, Chilukuri Riktha Devi, B. Madhavi Reddy, Kura Pranav Medarametla, Lahari Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529833

Mental health support is crucial for students, who are vulnerable to challenges due to academic pressures and social expectations. University culture often fosters misconceptions about mental well-being, leading to stigma that discourages students from seeking help. This project introduces an AI Therapist - a conversational agent offering a private, non-judgmental space for students to discuss personal challenges. Using Sentiment Analysis, a Natural Language Processing (NLP) method powered by machine learning, the system evaluates the emotional tone of these interactions. Through this analysis, the AI provides insights into students' mental health over time, visualized through data reports generated with tools like matplotlib. These reports allow students to track their emotional journey and promote self-awareness. The goal is to create an accessible support system that encourages self-reflection, reduces the stigma around mental health, and fosters early intervention. © 2025 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Empowering Precision Agriculture: A Comprehensive Real-Time Monitoring and Decision Support System for Farmers 3

Empowering Precision Agriculture: A Comprehensive Real-Time ...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Duraimurugan, N. Akalya, G. Aravind, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

A smart agricultural informatics platform integrated with Internet of Things (IoT) aims to revolutionize farming practices through a decentralized communication framework, the primary goal is to establish a knowledge-driven agricultural network in which farmers can discuss precision agronomy strategies, percentage know-how, and collaborate in real time to address challenges. The device carries a meteorological analytics module, powered by way of a RESTful Weather API, handing over a one-week climatic outlook to resource in dynamic choice-making. Advanced sensing technologies, including capacitive soil moisture sensors, electrochemical pH sensors, thermistors, and the DHT11 virtual sensor array, allow continuous tracking of soil physicochemical parameters and microclimatic situations for actionable insights. These features are wrapped in a mobility platform cell software program designed using the Flutter SDK, providing farmers with a modular UI/UX architecture that provides access to real-time farm diagnostics of their machines. Visualization of all facts using sensors. and predictive analytics for resources optimization Benefits of distributed computing and analytical aspects. When taken together this innovative platform helps farmers optimize spatial and temporal crop planning. Increase the sustainability of agricultural ecosystems and promote resilient, fact-driven agriculture. © 2025 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

AniSan@NLU of Devanagari Script Languages 2025: Optimizing Language Identification with Ensemble Learning 1

AniSan@NLU of Devanagari Script Languages 2025: Optimizing L...

引用

1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Shanto, Anik Mahmud Priya, Sanjida Jamal Arefin, Mohammad Shamsul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891762015

Identifying languages written in Devanagari script, including Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit, is essential in multilingual contexts but challenging due to the high overlap between these languages. To address this, a shared task on "Devanagari Script Language Identification" has been organized, with a dataset available for subtask A to test language identification models. This paper introduces an ensemble-based approach that combines mBERT, XLM-R, and IndicBERT models through majority voting to improve language identification accuracy across these languages. Our ensemble model has achieved an impressive accuracy of 99.68%, outperforming individual models by capturing a broader range of language features and reducing model biases that often arise from closely related linguistic patterns. Additionally, we have fine-tuned other transformer models as part of a comparative analysis, providing further validation of the ensemble’s effectiveness. The results highlight the ensemble model’s ability in distinguishing similar languages within the Devanagari script, offering a promising approach for accurate language identification in complex multilingual contexts. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Thyroid detection using deep hybrid learning: The review 4th

Thyroid detection using deep hybrid learning: The review

引用

4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Poonam Kaur, Harpreet Department of Computer Science and Engineering Chandigarh Engineering College Landran India

ISBN: (纸本)9781032911571

Your thyroid is a little gland that is situated in your neck. It produces hormones that aid in the regulation of several physiological functions. Thyroid disorders come in a variety of forms, such as goiter (enlarged thyroid), hypothyroidism (underactive thyroid), and thyroid cancer. These conditions can have various symptoms and require different treatments. There are two categories of thyroid diseases: primary and secondary. When you have primary thyroid illness, your thyroid gland is the disease’s original source. When you have secondary thyroid illness, your pituitary gland is the disease’s original source. Various diagnostic methods are employed to assess thyroid health. Blood tests measure hormone levels, imaging techniques like ultrasound visualize gland structure, and fine needle aspiration biopsy analyzes tissue samples. It has been observed that machine learning approaches are applied to the prediction of TD accuracy. Many methods have been employed in the past to abstract knowledge utilizing wellestablished deep hybrid learning algorithms for thyroid detection.. Some technique is used for the early prediction of thyroid detection before. The main idea behind this research is to early detection of thyroid. The already existing techniques are efficient for the detection however there is still need to find the new path to obtain the desired accuracy. © 2025 the Author(s).

关键词： Endocrinology

来源：评论

学校读者我要写书评

暂无评论

A Review on the Use of Smart Agriculture Systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Utility for Gas/Odor Sensor Signature Analysis 2

Deep Learning Utility for Gas/Odor Sensor Signature Analysis

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Raut, Yashasvi Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra India

ISBN: (纸本)9798331505745

Gas/odor sensors are integral components of sensor systems used in diverse applications such as food quality control, environmental monitoring, medical diagnostics, odor profiling, industrial safety, agriculture, explosives detection, wine tasting, pharmaceutical quality control, and toxic gas detection. Traditional approaches in this domain of gas/odor sensing technologies face challenges related to accuracy, cross-sensitivity, and drifted real-time performance. The integration of deep learning techniques with gas sensors has shown promise in overcoming these limitations. Moreover, this amalgamation of gas sensing and deep learning strategies enables accurate identification of gases/odors, handling complex patterns, and leading optimal consumption of resources. In this paper, an overview of advancements in deep learning-enabled gas sensing has been presented that focused on methodologies, sensor types, and applications. This review also highlights the state-of-the-art techniques and identifies gaps for future directions for research. © 2025 IEEE.

关键词： Damage detection

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：