检索结果-内蒙古大学图书馆

Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

SN Computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

来源：评论

学校读者我要写书评

暂无评论

Internet of Things (IoT) Powered Child Rescue System from Open Borewells by Using WiFi Integrated ESP32 Controller and Smart Sensors Association

Internet of Things (IoT) Powered Child Rescue System from Op...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vijayakumari, P. Mary, S. Jeya Divya, G. Vani, N. Narkhede, A. Sabri, M. Omar Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Ece department Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Currently, the negligence of individuals in society is the reason why children are falling into the Bore well. The current systems available to protect the child are both less effective and more expensive. Consequently, the society requires a novel approach that is both more effective and efficient. In the majority of cases that have been reported thus far, a parallel opening is excavated, and subsequently a horizontal path is established to access the infant. Not only is it a time-consuming procedure, but it also carries a variety of risks. The Borewells rescue system is capable of performing a variety of actions to rescue the child by moving within the same Borewells where the child has been confined. The purpose of this paper is to rescue children who have fallen into a bore well. It appears that abandoned bore wells are mortality traps for minors. The primary water framework requirements of cultivators in India are met by groundwater, as the country is primarily known for agriculture. The very significant requirement of bore wells is typically tunneled for the groundwater exploration requirement due to the increasing population, diminishing land assets, and the advancement of urbanization. In turn, these borewell have begun to claim the lives of numerous innocent individuals. The Dynamic Child Rescue Model (DCRM) is a novel rescue system that has been developed in this research. It is designed to easily protect children from the borewell and is cross-validated with the conventional approach, the Pipeline Navigation Model (PNM), to assess the efficacy of the proposed scheme. The system is supported by the Internet of Things (IoT). This work suggests a novel design that incorporates a sensor located at the top of the borewell opening to detect the presence of a child if they fall inside. © 2024 IEEE.

关键词： Abandoned wells

来源：评论

学校读者我要写书评

暂无评论

Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

引用

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

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis

Predictive Modeling of Portuguese Student Performance: Compa...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the 'Student Performance in Portuguese Secondary education' dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

引用

International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

来源：评论

学校读者我要写书评

暂无评论

PRESS: Defending Privacy in Retrieval-Augmented Generation via Embedding Space Shifting

PRESS: Defending Privacy in Retrieval-Augmented Generation v...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Jiaming Liu, Cheng Hou, Guanyu Jiang, Wenbo Li, Jiachen School of Computer Science and Engineering University of Electronic Science and Technology of China China College of Computer Science and Cyber Security Oxford Brookes College Chengdu University of Technology China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798350368741

Retrieval-augmented generation (RAG) expands the capabilities of large language models (LLMs) in various applications by integrating relevant information retrieved from external data sources. However, the RAG systems are exposed to substantial privacy risks during the information retrieval process, leading to potential data leakage of private information. In this work, we present a Privacy-preserving Retrieval-augmented generation via Embedding Space Shifting (PRESS), systematically exploring how to protect privacy in RAG systems. Specifically, we first conduct proximal policy optimization (PPO) based training on pre-trained language models to generate target training samples. Then we employ a purposive shift fine-tuning on the text embedding model with the generated samples for guiding the RAG system to map potential privacy leaking queries to safe target in embedding space. Extensive experimental results on representative models and datasets demonstrate that our protection method achieves high defense performance with high efficiency while keeping the normal functionality of the RAG system. © 2025 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. These algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) Machine learning Neural Network (NN)

来源：评论

学校读者我要写书评

暂无评论

NOMA Attack Prediction in Large-Scale Networks Using Non-Linear Chaotic Belief Mechanism

NOMA Attack Prediction in Large-Scale Networks Using Non-Lin...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： K. Sangeetha M. Suguna Jebakumar Immanuel D SA Suje Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA systems let numerous users concurrently be managed. On the other hand, the intricacy of these networks leaves them vulnerable to a wide spectrum of attacks, including the more advanced and erratic NOMA attacks on the network. These strikes could produce major disturbances that would compromise the quality of service and cast questions regarding the general network security. It has been demonstrated that the effective projection of these hazards is limited by standard linear and probabilistic techniques. This is true as contemporary methods fail to adequately capture the basic non-linear dynamics of these large-scale networks. This article offers a novel method for NOMA attack prediction by means of a non-linear chaotic belief process. The results are shown here. To recreate the uncertainty and intricate interactions inside the network, the proposed method which is the logistic map which in turn generates the sequences for ensuring the accurate iterative updates which in turn provides better scalability and precision. This integrates belief networks with chaos theory. More exactly, we capture the random and non-linear aspect of network dynamics by building belief values indicating the likelihood of an attack by use of a chaotic map. After that, the belief values proliferate across the network in search of defects and project the probability of NOMA attacks. Effectiveness of the proposed method is demonstrated by test results on a simulated large-scale network simulation. With a prediction accuracy of 92.7%, the chaotic belief mechanism obtained much above the average accuracy of 78.4% of traditional linear prediction systems. Moreover, the proposed approach lowered the false positive rate to 5.3%, substantially below the rate of 12.8% applied in the standard a

关键词： Training NOMA Accuracy Uncertainty Chaotic communication Prevention and mitigation Network security Iterative methods Computer crime Standards

来源：评论

学校读者我要写书评

暂无评论

Bridging the Gap with AI-Based Semantic Search Strategies 15

Bridging the Gap with AI-Based Semantic Search Strategies

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Vennila, C. Chaudhari, Prasad B. Kantak, Ganesh Kumar Naval, Preeti Pramod, R. Nagpal, Manish Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Bridging the space with synthetic intelligence (AI)-based semantic seek strategies gives you practical techniques for navigating the ever-growing virtual facts space. With AI-pushed semantic search, users can query for valuable expertise in several databases, raising statistics discovery to a new degree of speed and efficiency. AI-pushed semantic search can identify valuable data in complicated record sets, bringing practical insights into sharper awareness while brushing off redundant content material from densely populated facts collections. With AI-pushed semantic seek, fact discovery is faster, more profound, informed, and more valuable. Lay users can enjoy the more advantageous manipulation of personal facts while expert workers receive the essential records to make more knowledgeable selections. © 2024 IEEE.

关键词： Disregarding Enhanced Information Populated Processing

来源：评论

学校读者我要写书评

暂无评论

Air Pollution Monitoring System using Machine Learning techniques for Smart cities 2

Air Pollution Monitoring System using Machine Learning techn...

引用

2nd International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2024

作者： Praveen, R.V.S. Vinoth, B. Sowmiya, S. Tharageswari, K. Vamsilala, Purushothapatnapu Naga Venkata Sathya, R. Digital Engineering LTIMindtree Telangana Hyderabad India Department of Statistics Bengaluru India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Vignan's Lara Institute of Technology and Science India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350368413

Monitoring the environment is a complex task that is crucial for all living beings. Urban and industrial areas are particularly vulnerable to pollution, which can lead to various health issues. This system utilizes data analysis and machine learning to monitor and classify regions where gas emissions exceed permissible levels. Unlike existing systems, which can only predict air quality in small areas with limited efficiency, the proposed system can accurately predict air quality across larger regions. By factoring in the temperature of a specific location - since air quality is heavily influenced by global warming - the system can predict air grade. The DBSCAN algorithm proves to be particularly effective in identifying highly polluted areas, outperforming other algorithms in terms of accuracy and efficiency. Air Quality Operational Centres (AQOCs) are crucial in identifying regions with severe pollution. These centres analyse data gathered by various units to make final assessments. The predicting air pollution relied on complex physiochemical processes and dispersion models, which were both time-consuming and required large datasets, making them less efficient for practical use. © 2024 IEEE.

关键词： Air quality

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：