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检索条件"任意字段=International Conference on Data Science and Network Engineering, ICDSNE 2023"

共 14948 条记录，以下是51-60 订阅

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Root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors 5th

Root Vegetable Crop Recommendation System Based on Soil Prop...

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5th international conference on data science, Machine Learning and Applications

作者： Ajagalla, Mahadev Pandey, Manish Choudhary, Jaytrilok Kumar, Lalit Maulana Azad Natl Inst Technol Bhopal Madhya Pradesh India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Traditional Agriculture has always involved the use of data. However, modern data challenges have become increasingly complex. As a result, data mining processes are now employed to construct machine learning models that analyze and extract valuable insights from this data. In the context of agriculture, data mining plays a crucial role in predicting crop yields, forecasting climate patterns and rainfall, optimizing seed and soil selections, and enhancing overall crop production efficiency. Agriculture serves as a cornerstone of India's economic framework. Looking ahead to the next generation, the global population is projected to continue its growth. By 2050, the Earth will be home to an additional 9 billion people. Consequently, there is a pressing need to increase food production by 70%, all while utilizing less land. A common issue faced by numerous farmers is the challenge of selecting the appropriate crops based on their soil composition and local environment. This predicament leads to crop diseases, elevated production costs, and significant declines in productivity. Precision agriculture has emerged as a solution to this problem. Precision agriculture, rooted in traditional farming practices, leverages soil analysis, climate conditions, and temperature data to offer refined guidance on crop selection. This approach empowers farmers to enhance their output and productivity. In the context of this paper, we proposed a solution to address this challenge: the implementation of a recommendation system using a Random Forest classifier, which falls under the umbrella of Ensemble techniques. Additionally, the model is further refined by training it using Artificial Neural network (ANN), employing the K-Fold Cross-validation technique through Keras. This strategy aims to augment the accuracy of model training, resulting in improved recommendations and outcomes.

关键词： Precision agriculture Recommendation system Ensemble model Random Forest classifier Bagging technique Artificial neural network (ANN) K-Fold Cross-validation technique with Keras

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Memristor-based Neural network Circuit Simulation for Classification Application 24

Memristor-based Neural Network Circuit Simulation for Classi...

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8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Hou, Zhuang Tao, Ye Wang, Zhongqiang Lin, Ya Zhao, Xiaoning Key Laboratory for UV Light-Emitting Materials and Technology Northeast Normal University Jilin Changchun China

ISBN: (纸本)9798400710094

With the advent of the big data era, the traditional von Neumann architecture, which has separate processing units and storage units, brings huge delay and energy consumption, and suffers from high data transmission costs. The memristor, a new principle device, has the ability to modulate the resistance based on the electrical flow passing through it, and its excellent nonlinear characteristics make it a basic component unit of a brain-inspired computing architecture. Scaling up the memristor and constructing a brain-inspired neural network architecture based on it, simulating the computing and learning process of artificial neural networks, is the fundamental solution to the von Neumann architecture bottleneck problem. In this paper, we use the LTspice circuit simulation software to build a brain-inspired neural network array based on memristor units and conduct target detection and classification application tests on typical data sets, thereby verifying the potential application of the memristive neural network architecture in the field of brain-inspired computing. © 2024 Copyright held by the owner/author(s).

关键词： Application programs

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Research on optimization scheme for maintaining consistency of high-speed railway belt benchmark station network 5

Research on optimization scheme for maintaining consistency ...

引用

5th international conference on Signal Processing and Computer science, SPCS 2024

作者： Pan, Peifen Luan, Zhong Lu, Zhiyuan Xu, Xiaolei Yang, Meihao Zhou, Zijian China Academy of Railway Sciences Co. LTD Beijing100081 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This study addresses the technical challenge of maintaining coordinate consistency within the belt benchmark station networks of high-speed railways, focusing on a specific high-speed rail line. The research involves an assessment of the current network status and the collection of data from selected stations in Shandong and Jiangsu provinces. To enhance the stability and reliability of the coordinate system, three optimization schemes have been proposed: calculations based on IGS stations, linear starting points, and peripheral national and provincial stations. Strict data processing techniques have been implemented, including synchronous loop Nrms statistics and baseline accuracy assessments, to ensure high standards and quality of baseline calculations. Simulation results indicate that all schemes meet technical specifications, ensuring the precision and reliability of coordinate calculations. The incorporation of peripheral stations significantly enhances the overall control of the network structure, effectively reducing systemic biases. The optimization schemes proposed in this study not only improve the consistency of the railway coordinate benchmarks and achieve higher positioning accuracy, but also serve as a reference for the design and data management of similar high-speed railway projects. © 2025 SPIE.

关键词： Railroads

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Developing Algorithms for Adaptive network Coding and Its Impact on Wireless network Performance 5th

Developing Algorithms for Adaptive Network Coding and Its Im...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Garg, Aanchal Poornima, Selvaraj Savita Nachappa, M.N. Department of Electronic & Communication Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Adaptive network coding (ANC) is a recent technique used in Wi-Fi networks that utilizes coded transmissions to boost community performance. The concept at the back of ANC is to find the ideal combination of coded packets, considering channel conditions and network topology. By adjusting encoding techniques in response to converting environmental situations, ANC helps to maximize throughput and reduce overhead and latency. This paper proposes a set of rules for ANC that considers the evolution of channel situations to alter its encoding strategy dynamically. The proposed set of rules works by using remarks from previously transmitted packets to determine whether modifications to the current transmission scheme want to be made, which will improve community performance. The algorithm's effectiveness is then tested through simulations wherein the network's overall performance is compared to widespread Wi-Fi structures. Outcomes show that the set of rules can achieve sizable upgrades in network performance… © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless local area networks (WLAN)

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Blockchain Based Efficient Pairing-Free Certificateless Authentication Scheme for Vehicular Ad-hoc network 22nd

Blockchain Based Efficient Pairing-Free Certificateless Auth...

引用

22nd international conference on Service Oriented Computing

作者： Yu, Meiju Sa, Rula Zhang, Jin Zhao, Yi An, Qi Gao, Qiaomei Inner Mongolia Univ Coll Comp Hohhot 010021 Inner Mongolia Peoples R China

ISBN: (纸本)9789819608072;9789819608089

Vehicular ad hoc network (VANET) provides short range communication for vehicles, achieving information sharing to improve road safety and traffic efficiency. However, public communication between vehicles has led to issues with vehicle communication security and privacy leakage, which seriously restricts the development of VANET. The authentication mechanism can verify the reliability of messages and the validity of identities, thereby ensuring communication security between vehicles. Therefore, this paper proposes a certificateless authentication scheme based on the characteristics of blockchain, such as decentralization, traceability, and tamper resistance, combined with a pairing-free certificateless password mechanism. Through security proofs and analysis, it is demonstrated that we achieve high security and satisfies security requirements such as integrity, traceability, anonymity, and revocability. Furthermore, simulation comparisons with existing schemes show that we have lower computational overhead and communication costs.

关键词： Vehicular ad hoc network Authentication Security Certificateless Blockchain

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Investigating Secure Information Exchange in Distributed and Cooperative Media networks 5th

Investigating Secure Information Exchange in Distributed and...

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Nachappa, M.N. Ali, Arif Jothidurai, Alamelu Mangai Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper provides a have a look at into the security of dispensed and cooperative media networks for facts change. Security is a vital issue in distributed and cooperative media networks because of the nature of the generation. The research covers an expansion of safety features associated with facts change in the network, including authentication, authorization, get admission to manage, encryption and integrity verification. The studies dives into the blessings and demanding situations of different technology used for cozy conversation. To determine the maximum suitable protection answer for a given community environment, an evaluation of the characteristics of the network is performed, which includes an analysis of the threats posed by using the environment and the safety necessities. The effects of the research imply that a combination of various safety features must be followed for sturdy safety. Also, the emergence of net of things technology is developing new security challenges, which need to be considered inside the protection solution layout. The paper presents treasured perception into the security components of a distributed and cooperative media network and highlights the importance of organizing powerful measures for at ease records trade. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data integrity

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A Hybrid Prediction Method for Lithium-Ion Battery Degradation: SMA-ARIMA-LSTM Integration 3

A Hybrid Prediction Method for Lithium-Ion Battery Degradati...

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3rd international conference on Communications, Information System, and data science, CISDS 2024

作者： Li, Haoran Bai, Yanhong Sun, Yongchao Zhi, Huixue Department of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China Department of Intelligent Manufacturing and Industry Shanxi University of Electronic Technology Linfen China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

Accurately predicting the lifespan of lithium-ion batteries is crucial for reducing maintenance costs and advancing clean energy technologies. Traditional prediction methods often to accurately estimate the lifespan due to the diverse volatility characteristics of lithium-ion battery degradation. This paper proposes a Kurtosis-driven SMA-ARIMA-LSTM method to the lifespan of lithium-ion batteries. First, the data is decomposed into low and high volatility components using a moving average (SMA). Then, the I model is applied to the low volatility part, while the LSTM network handles the high volatility part. Finally, the parallel prediction results of these two parts are combined to the remaining lifespan of the lithium-ion battery. The model is validated using four sets of CS2 series lithium-ion battery degradation data provided by the CALCE center at University of Maryland. The results show that the hybrid model significantly improves prediction accuracy compared to standalone LSTM or ARIMA models, achieving near-perfect determination coefficients while significantly mean and root mean square errors. This effectively captures the overall degradation trend of the battery and the capacity regeneration phenomenon. The experimental results demonstrate that the proposed SMA-IMA-LSTM method achieves a fitting rate of over 95%, with MAE kept within 0.9% and RMSE kept within 1.4%, thus realizing precise prediction of the remaining lifespan of lithium-ion batteries. © 2025 SPIE.

关键词： autoregressive integrated moving average battery capacity prediction kurtosis optimized moving average lithium-ion battery long short-term memory network

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Controllable False data Injection Attack 5th

Controllable False Data Injection Attack

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5th international conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kokkonda, Jayasree Jahnavi, M. Department of Information Technology G. Narayanamma Institute of Technology and Science Hyderabad India Department of Computer Network and Information Security Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819780426

Yet the Internet of Things continues to grow significantly. A cyber-physical system faces a variety of security challenges due to the network connections made by the various kinds of devices and large systems that make up national infrastructures like a smart grid. Here, For the forward flow and the feedback path of a control system, respectively, we suggest two fake data injection attacks. Both are made using a variable parameter that determines the degree of deterioration. On the forward path, a linear forward assault is created together with the least squares approach. The Kalman filter is constructed on the feedback route. According to the numerical analysis, the suggested assaults govern how much the performance of the control system declines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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An Enhanced Intrusion Detection Model with FeedForward Neural network Classifier 27

An Enhanced Intrusion Detection Model with FeedForward Neura...

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27th international conference on Advanced Communications Technology, ICACT 2025

作者： Amirjon, Asadov Gain, Mrityunjoy Oh Kim, Keon Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Artificial Intelligence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9791188428137

A major obstacle in the face of increasingly complex cyberattacks is network security. Proactive security measures require effective intrusion detection systems (IDS) that can precisely classify and categorize network threats. In order to improve network attack detection and classification, this paper proposes a reliable method utilizing a Feedforward Neural network (FFNN) supplemented with Adaptive Synthetic (ADASYN) sampling. We created a model using the UNSWNB15 dataset that efficiently handles high-dimensional datasets by preprocessing data using a combination of polynomial feature transformation and one-hot encoding. The FFNN model is optimized for binary and multi-class classification tasks. It consists of layers of dense units with dropout and batch normalization. Our method’s efficacy is proven by rigorous training and validation procedures, where the model significantly increased its ability to handle class imbalances and improve classification accuracy. The synthesis of new training data by ADASYN was crucial in improving model performance, especially in underrepresented classes. Evaluation measures that highlight the potential of deep learning in network security applications are ROC-AUC scores and classification reports, which show a notable improvement in our IDS’s detection capabilities. The results show that advanced machine learning techniques can be used to enhance conventional intrusion detection systems and provide a means to build stronger network security designs. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： network intrusion

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Skin Disease Detection 5th

Skin Disease Detection

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5th international conference on data science, Machine Learning and Applications

作者： Janokar, Sagar Joshi, Kalpesh Raikwar, Rajesh Kumbhar, Sahil Kumbhar, Akshay Kumbhar, Shashank Vishwakarma Inst Technol Elect & Telecommun Engn Pune Maharashtra India Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Dermatology is a specialized field of biology focused on diagnosing and treating skin conditions. The skin's diverse range of conditions can be influenced by factors like temperature, humidity, and climatic conditions, leading to regional and seasonal variations. However, the unique characteristics of human skin, such as unevenness, tone, and hairiness, make it challenging to mechanically synthesize and assess accurately. While some studies have used computer vision techniques to identify and simulate skin diseases, only a few have addressed the medical paradigm of the problem. In many remote areas, limited access to medical services causes patients to disregard early symptoms, potentially exacerbating their condition over time. As a result, an elevated demand is emerging for precise automated techniques to identify skin conditions. We developed a deep learning model capable of differentiating between normal skin and skin with ailments to address this requirement. Furthermore, our model is capable of categorizing significant varieties of skin conditions, including melanocytic nevi, melanoma, benign keratoses-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. A subclass of machine learning a deep learning, played a crucial role in the development of our model. By utilizing large datasets, deep learning significantly reduced the number of classifiers compared to traditional machine learning techniques. Convolutional neural network (CNN) algorithm, commonly employed for image categorization, was the backbone of our model. Through this research, we aim to promote and support the expansion of dermatology by providing a fast and accurate tool for skin disease diagnosis and classification.

关键词： Convolutional Neural network (CNN) Skin Disease Deep Learning Training Testing and Evaluation Dermatoscopic pictures

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