检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Lakineni, Prasanna Kumar Reddy, D Jayanarayana Chitra, M. Umapriya, R. Kannan, L. Vadivel Barkunan, S.R. Dadi Institute of Engineering & Technology Department of Computer Science and Engineering Visakhapatnam India G Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India Kpr Institute of Engineering and Technology Department of Chemical Engineering Coimbatore India Psna College of Engineering and Technology Department of Mechanical Engineering Dindigul India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798350398465

Plant disease diagnosis remains a maj or unsolved problem in the changing scientific world. One of the most important agricultural sectors is growing plants with artificial intelligence support. Image analysis and classification techniques are aimed at predicting plant diseases. So far, well-versed farmers or plant specialists can only diagnose plant diseases. Similar results have been shown for almost different plant diseases in the methods thus introduced. To solve such a problem, we have developed an improved Convolutional Neural Network (CNN) method to identify plant diseases faster and better. Initially, the preprocessing task was done by using a wrapping filter. After that, the best features of the plant disease can be selected from the Logistic Decision Regression (LDR). LDR feature selection is used to reduce the classification problem to selecting features of medicinal plants. Leaves are commonly used to identify medicinal plants, branches, flowers, petals, seeds and whole plants for use in automated procedures. Automated disease detection methods are developed based on changes in plant foliar disease states. Convolutional neural networks (CNNs) are the most accurate of the complex feed- forward neural networks in image classification and recognition. By estimating the results, various images can be training have efficient image recognition functions with accuracy and strong reliability. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Improved Adaptive Fairness-Indexed Technique for Congestion Control in Underwater Networks 3

Improved Adaptive Fairness-Indexed Technique for Congestion ...

引用

3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Balaji Vijayan, V. Manojee, K.S. Mohammed Ibrahim, M. Nivethitha, R. Thamaraimanalan, T. Ranjithkumar, M. Hkbk College of Engineering Department of Information Science and Engineering Karnataka Bengaluru India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.S.R. College of Engineering Department of Computer Science and Engineering Tamilnadu Thiruchengode India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (纸本)9798331528201

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain's Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation. © 2024 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Intrusion detection system with walrus optimization algorithm (WOA) and BiGRU-CNN for securing IoT systems

引用

International Journal of Information technology (Singapore) 2025年 1-8页

作者： Appathurai, Ahilan Bachan, P. Kaushik, Ruchi Tamizhselvi, A. Jodha, Rajshree Singh, Kulwant Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Melathediyoor India Department of Electronics and Communication Engineering GLA University Mathura India Xavier Institute of Management and Entrepreneurship Bengaluru India St. Joseph’s College of Engineering Chennai India Computer Science and Engineering Indian Institute of Information Technology (IIIT) Kota India Electronics and Communication Engineering Yadavindra Department of Engineering (YDoE) Punjabi University Guru Kashi Campus (PUGKC) District Bathinda Talwandi Sabo India

Security has become a primary issue in IoT (Internet of Things) because of its complex environment, handling multiple devices, several connection methods, and transmission of a huge volume of data. Traditional techniques don’t manage datasets or learn features optimally, which degrades attack detection accuracy. To solve the issues, an effective Intrusion Detection System (IDS) is proposed for detecting security risks in IoT systems. The framework comprises three phases, namely data pre-processing, feature selection, and classification. Pre-processing is applied to the data gathered from the public repository. The best features are then selected from the pre-processed dataset by inputting the pre-processed data into the Walrus Optimization Algorithm (WOA). It is followed by data classification using Bidirectional Gated Recurrent Unit-Convolutional Neural Network (BiGRU-CNN). This framework can classify data into five categories, such as normal (benign), DoS (Denial of Service), Shellcode (U2R), Fuzzers (R2L), and Probe attacks. The effectiveness of the proposed framework is evaluated based on metrics such as intrusion detection rate (IDR %) and network accuracy. The results are highly satisfactory, leading to network accuracy and IDR as 98.20% and 98.15%, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： BiGRU-CNN IDR IDS IoT security Network accuracy WOA

来源：评论

学校读者我要写书评

暂无评论

Proposed 3D chaos-based medical image cryptosystem for secure cloud-IoMT eHealth communication services

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1-28页

作者： El-Shafai, Walid Khallaf, Fatma El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electrical Engineering Faculty of Engineering Ahram Canadian University Giza 6th October City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh84428 Saudi Arabia

Medical image encryption is a mandatory process in various healthcare, Internet of Medical Things (IoMT) and cloud services. This paper provides a robust cryptosystem based on a 3D chaotic map for the medical image encryption in secure IoMT and cloud services. The proposed encryption method depends on a 3D chaotic map. With this map, a nonlinear ciphering process is implemented for pixel value diffusion and position permutation. Five operations are conducted in the suggested cryptosystem on the medical images to be transmitted in order to attain a high security level. These operations are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. To validate the suggested cryptosystem, different medical images with different characteristics are tested. Moreover, the suggested cryptosystem and more recent state-of-the-art security systems are discussed and analyzed in a comparative study. Different assessment parameters are adopted to assess the suggested cryptosystem, which reveals high security performance. The simulation outcomes show that the suggested cryptosystem is trustworthy. Furthermore, it offers high robustness levels and recommended security levels for utilization in healthcare, IoMT and cloud service applications. It is observed that the amount of average entropy obtained by the proposed 3D chaos cryptosystem is 7.95 which is very close to its best value of 8, and the average NPCR value is 99.62% which is also much close to its supreme value of 99.60%. In addition, all other results scored for the test evaluation parameters on the suggested cryptosystem are superior to those of the traditional security systems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Attention Mechanism for Domain-invariant Spoken Language Identification via Inter-domain Alignment and Intra-domain Language Discrimination

Hierarchical Attention Mechanism for Domain-invariant Spoken...

引用

2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Goswami, Urvashi Muralikrishna, H. Kumar, Sujeet Dileep, A.D. Thenkanidiyoor, Veena Indian Institute of Technology Mandi School of Computing and Electrical Engineering Mandi India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal576104 India Indian Institute of Technology Dharwad Department of Computer Science and Engineering Dharwad India National Institute of Technology Goa Department of Computer Science and Engineering Goa India

ISBN: (纸本)9798331505776

Modern methods for spoken language identification (LID) have demonstrated promising results when trained on large datasets. However, their effectiveness is considerably impacted by the discrepancies between the training and testing distributions. Unsupervised domain adaptation (UDA) addresses the domain discrepancies by aligning feature distributions across source and target domains without using labeled target data, thereby ensuring the model performs consistently across both domains. This alignment, however, may come at the cost of reduced model discriminability. To enhance performance under domainmismatched conditions, a LID system should learn domaininvariant representations of speech while maintaining language discriminability at every level, from initial segment-level representations to final utterance-level representations. To address this issue, in this paper, we propose a hierarchical attention network that simultaneously enhances model domain invariance through inter-domain alignment and improves model's language discriminability using intra-domain contrastive discrimination. The performance on the target domain shows that the proposed method exceeds the effectiveness of the top-performing existing approaches. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

Enhancing Pervasive Developmental Disorder Interventions for Children through Deep Learning Algorithms in Virtual Reality 2

Enhancing Pervasive Developmental Disorder Interventions for...

引用

2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Reethika, A. Priyadharsini, S. Vijaykumar, G. Rasheedha, A. Devi, G. Kanaga Priya, P. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Biomedical Engineering Sri Shanmugha College of Engineering & Technology Salem India Department of Biomedical Engineering Sri Ramakrishna Engineering College Coimbatore India Department of computer science and engineering SNS college of technology Coimbatore India Department of computer science and engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350340235

Pervasive Developmental Disorders (PDDs) represent a complex set of neurodevelopmental conditions in which a children has a challenges in social communication, repetitive behaviors, and restricted interests. Early intervention is serious in improving the lives of children with PDDs. Autism spectrum can be identified at the age of 2 years based on the behavior symptoms. This abstract introduces a novel approach to PDD intervention that leverages deep learning algorithms within a Virtual Reality (VR) environment. This research explores the Deep learning algorithms are utilized to create adaptive and personalized experiences, which adapt in real-time to the child's unique needs, preferences, and progress. These algorithms harness vast datasets of behavioral and physiological information, allowing for precise and individualized interventions. The VR environment serves as a safe and controlled space for children with PDDs to practice social interactions, communication skills, and sensory integration. It provides immersive scenarios and simulations designed to mimic real-life situations, facilitating gradual exposure and desensitization to sensory challenges. © 2023 IEEE.

关键词： behavior deep learning intervention neurodevelopmental conditions Pervasive Developmental Disorders

来源：评论

学校读者我要写书评

暂无评论

Improving Online Education in Educational Institutions through the Integration of Internet of Things (IoT) 8

Improving Online Education in Educational Institutions throu...

引用

8th International Conference on electronics, Communication and Aerospace technology, ICECA 2024

作者： Ramaraj, Kottaimalai Mathews, Ann Lynsha Helena Pratheeba, Hp Ramani, U. Sujitha, K.L. Thilagaraj, M. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India K Ramakrishnan College of Engineering Department of Electrical and Electronics Engineering Trichy India Mvj College of Engineering Department of Computer Science and Engineering Bengaluru India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350367904

A key facilitating infrastructure for building intelligent structures that enable efficient in-person and virtual learning environments is the Internet of Things (IoT). The shift to smart learning, that includes IoT and Artificial Intelligence (AI) into the educational system, is enticing since it directly affects the motivation of learners, enrolment, participation, and in-depth understanding. Teaching and learning, management, examination, and class monitoring are only a few of the numerous issues that afflict conventional education. Many ingenious approaches to many facets of our lives are being made possible by modern advancements in information and communication technology (ICT);nonetheless, these imaginative concepts aren't adequately incorporated into the framework of education. The COVID-19 pandemic scenario, in specific, has brought out the need for innovative, creative approaches in the field of teaching. The present research examines previous investigations in the field and discusses the following topics: (i) issues with the current educational system and potential fixes;(ii) the shift to smart learning;and (iii) scientific hurdles (such as technological and cultural barriers) in making the move to smart learning. In accordance with these outcomes, creative approaches to the issues with the conventional system-such as smart teaching, smart evaluation, smart school environment, and smart management-are presented. The business and academic community may now include IoT, AI, and ICT into smart learning owing to the aforementioned experimental research's novel developments. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Smart Home Automation with Smart Metering using ZigBee technology and Deep Belief Network 2

Smart Home Automation with Smart Metering using ZigBee Techn...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Murugesan, Sankar Cm, Avinash Bhuvanesh, A. Manoj, Sumitha Darney, P. Ebby Rajan S, Kani Siva Vel Tech Rangarajan Dr.Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India Rajarajeswari College of Engineering Department Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350327533

The smart metering in the smart home application is vital one to ensure the reduction of energy usage and become a hot topic in the research field with the increasing usage of energy in both industrial and residential. With the advance technologies the energy saving techniques are used for the smart homes. The Internet of Things (IoT) devices are used for monitoring, controlling, and detecting the devices for the smart home applications. In this study, a novel Deep Belief Neural Network (DBN) approach for analyzing the data that are gathered from smart home applications with IoT devices, and big data technologies which promises the smart metering in smart home applications is proposed. The energy consumption patterns and classifications are effectuated with the proposed DBN approach and ensures the safety of the home. Experimental study was made and analyzed the performance of the proposed wo rk in terms energy consumption of different devices of smart home applications. Our proposed approach surpasses all the other approaches for the smart metering based smart applications. © 2024 IEEE.

关键词： Deep Belief Neural Network Energy Smart Home Smart Metering Zigbee Protocol

来源：评论

学校读者我要写书评

暂无评论

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

引用

computers, Materials & Continua 2023年第5期75卷 2413-2427页

作者： Salah-ud-din Khokhar QinKe Peng Muhammad Yasir Noor Department of Automation Science and Technology(System Engineering Institute) School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anShaanxi710049China Department of Computer Science G.C.UniversityLahore54000PunjabPakistan Department of Physics(Electronics) G.C.UniversityLahore54000PunjabPakistan

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s *** a variety of non-linear techniques,a SISO FS is *** results of FS experiments conducted in comparable conditions are then *** simulated results and the results of the experimental setup agree fairly *** findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control *** using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of *** proposed fuzzy system performed far better than other modern days approaches available in the literature.

关键词： Mean absolute percentage error membership functions relative error fuzzy system

来源：评论

学校读者我要写书评

暂无评论

An empirical intelligent water irrigation system using soft computing and IoT

引用

International Journal of Information technology (Singapore) 2024年第6期16卷 3983-3989页

作者： Patel, Warish Sangeetha, Y. Gopagoni, Deepa Rani Mallik, D. M. Arvind Agrawal, Rakshal Mishra, Saket Sunil, M.P. Kolandaisamy, Raenu Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat India Department of Information Technology VR Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India PES Institute of Technology and Management Karnataka Shivamogga India Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka Bengaluru India Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Water loss from fruit and vegetable production is mostly caused by leakage in the transportation system and handling of land, as well as by using traditional processing techniques. In order to increase the wellness of plants, save power, and optimize consumption of water, information should be widely applied in designing intelligent irrigation systems, which is possible with the help of the Internet of Things (IoT). In the given paper, an IoT-based soft computing recommendation framework is proposed for performing irrigation in a smarter way. The system works in such a way that IoT devices are positioned in the field to gather information about crops and groundwater levels. The acquired data is then fed to a cloud repository, which makes use of soft computing techniques such as neural networks for analysing and providing effective recommendations to farmers on their smartphones. In this way, the framework is tuned and the farmers are also updated from time to time regarding judicious irrigation processes. Lastly, the performance of the proposed system is validated against traditional techniques by taking crop datasets into consideration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Efficient water management Intelligent devices IoT Smart irrigation Soft 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：