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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Aishwarya Barath, Ananya Satish Kumar, T. Khushi, L. Lavanya, N. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9789819613472

Predictive maintenance in the automotive sector has received a lot of attention because of its potential to improve operating efficiency and lower maintenance costs. This paper presents an approach for predictive maintenance that focuses on engine condition prediction and battery remaining usable life (RUL) predictions. The work intends to develop precise predictive models to detect anomalies in engine health and anticipate battery degeneration, allowing for proactive maintenance interventions. The research entails gathering and analyzing data from engine sensors and battery characteristics in order to train and test various machine learning models. XGBoost was the best algorithm for forecasting engine health, with an accuracy of 0.66, while Bagging Regressor was the best regression technique for engine prediction, with an MSE of 4443.04 and RMSE of 66.66. These models are highly accurate and efficient, demonstrating their potential for real-world use in automobile maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive maintenance

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Robust Deep Convolutional Solutions for Identifying Biotic Crop Stress in Wild Environments

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 497-508页

作者： Pal, Chiranjit Mukherjee, Imon Chatterji, Sanjay Pratihar, Sanjoy Mitra, Pabitra Chakrabarti, Partha Pratim Indian Institute of Information Technology Kalyani Department of Computer Science and Engineering Kalyani741235 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not accurately reflect field conditions. Field-based leaf image analysis poses challenges due to varying image quality and sunlight intensity. Moreover, the complexity of crop stress images, with their random lesion distribution, diverse symptoms, and complex backgrounds, further complicates the analysis. To overcome these limitations, a lightweight hybrid convolutional neural network has been developed. This system integrates the powerful three-deep blocks model with an autoencoder running in parallel to highlight regions of crop stress effectively. To support this approach, we have introduced the Indian Rice Disease Dataset (IRDD) with labeled images. The proposed system reports an average true positive rate (TPR) of 0.8766 and an average positive predicted value of 0.8720 on IRDD, which are higher than other state-of-the-art crop disease detection models. The system is validated on benchmark datasets, yielding significant results: TPR of 0.9870 (rice), 0.9985 (tomato), and 0.8559 (corn). Furthermore, the proposed model outperforms recent state-of-the-art works on the benchmark PlantDoc dataset, showing its effectiveness in generalizing plant disease identification tasks. Finally, an ablation study has been carried out to explore the importance of the two parallel branches. Overall, this study acts as a bridge between advanced science and practical application, showcasing how interdisciplinary automation could revolutionize crop disease identification, improve agricultural efficiency, and reshape broader industrial practices. © 2024 IEEE.

关键词： Convolutional neural networks

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Hybrid tri-memristor hyperchaotic map and application in Wasserstein Generative Adversarial Nets

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science China(Technological sciences) 2024年第6期67卷 1855-1865页

作者： GU Yang BAO Han YU XiHong HUA ZhongYun BAO BoCheng XU Quan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou213159China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Inspired by basic circuit connection methods,memristors can also be utilized in the construction of complex discrete chaotic *** investigate the dynamical effects of hybrid memristors,we propose two hybrid tri-memristor hyperchaotic(HTMH)mapping structures based on the hybrid parallel/cascade and cascade/parallel operations,*** the HTMH mapping structure with hybrid parallel/cascade operation as an example,this map possesses a spatial invariant set whose stability is closely related to the initial states of the *** distributions and bifurcation behaviours dependent on the control parameters are explored with numerical ***,the memristor initial offset-boosting mechanism is theoretically demonstrated,and memristor initial offset-boosting behaviours are numerically *** results clarify that the HTMH map can exhibit hyperchaotic behaviours and extreme multistability with homogeneous coexisting infinite *** addition,an FPGA hardware platform is fabricated to implement the HTMH map and generate pseudorandom numbers(PRNs)with high ***,the generated PRNs can be applied in Wasserstein generative adversarial nets(WGANs)to enhance training stability and generation capability.

关键词： memristor hybrid tri-memristor map spatial invariant set hyperchaos extreme multistability generative adversarial nets

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A Hybrid Approach for Lung Cancer Detection Using Relevant Feature and Neural Network Techniques

A Hybrid Approach for Lung Cancer Detection Using Relevant F...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Arya, Mithlesh Rajpoot, Arvind Singh Pathak, Dhruv Garg, Bhagyansh Gupta, Megha Jain, Abha Swami Keshvanand Institute of Technology Management & Gramothan Computer Science & Engineering Jaipur India Sardar Vallabhbhai National Institute of Technology Computer Science & Engineering Surat India Jaipur India

ISBN: (纸本)9798350379525

Lung cancer is most common in men in India. Smoking is the main cause of lung cancer, apart from this there are many other reasons also. There are many tests to detect lung cancer such as chest X-ray and Computed tomography (CT). In this paper we are using two datasets to validate our work. First dataset-I is numeric data which is collectively based on surveys and second dataset-II contains CT images of lungs. In numeric data we have 15 features and have applied a neural network and ensemble classifier to classify the data into normal and cancerous classes. After that Pearson correlation method is used to select the most prominent features. The highest performance is obtained when the XGBoost ensemble classifier is used and the obtained values are Precision (95%), Recall (98%) and Fl score (96%). In the second dataset-II we have applied CNN to classify the lung images into multiple classes (normal, benign and malignant). But the accuracy is not prominent. Then we are using feature selection methods on flatten images and applying ensemble classifiers (Random forest, Gradient Boosting and Support Vector Classifier) to classify the images multiple classes. Prediction after feature selection is given a more prominent result and these are precision(1.00), recall(1.00) and F1 score(1.00). © 2024 IEEE.

关键词： Lung cancer

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Smart Voting System Using Deep Learning 5th

Smart Voting System Using Deep Learning

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Prasanthi, P. Aneesha, K. Sujith, P. Deepika, S. Sai Vamsi, S. Jayanthi Rao, M. Department of Information Technology Aditya Institute of Technology and Management Tekkali532201 India Department of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali532201 India

ISBN: (纸本)9789819767311

The world's largest democratic nation is India, where we live. Therefore, it is imperative to guarantee that a fair election is used to elect the governing body. India exclusively employs an offline voting system, which is characterized by inefficiency and substandard performance due to the substantial manpower requirements and the extended duration for processing and disseminating the results. Therefore, a change that addresses these drawbacks is necessary for the system to become effective. The new procedure makes voting easier by not requiring a voter's physical appearance. This study focuses on a two-step authentication system that uses face recognition and an OTP mechanism to enable voters to cast their ballots remotely from any location using a computer or mobile device. If the user finds it more comfortable, this product also lets them vote offline. Prior to the election, voters’ faces are recorded using a face scanning technique that is helpful while voting. RFID tags, not voter ID the offline voting method. In order to use this method, voters must snap a photo of themselves prior to the election and compare it to the original before casting their vote. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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AI-Based Security Protocols for IoT Applications: A Critical Review

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Recent Advances in computer science and Communications 2023年第5期16卷 2-18页

作者： Chadha, Harshita Gupta, Shruti Khanna, Ashish Kumar, Neeraj Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Punjab Chandigarh India

The Internet Of Things (IoT) is a network of heterogeneous nodes that exchange data and critical information amongst themselves with minimum human intervention. The utility of this technology is large, thus it is used today in most modern applications. With billions of nodes connected across multiple applications, the area for potential security attacks is ever increasing. In addition to this, the nature of the data being transmitted also becomes more sensitive to the latest applications and this calls for effective security provisions. Due to its unique nature, traditional security provisions are not as successfully applicable in IoT networks. This leaves these networks vulnerable to malicious intruders. In such a scenario, Artificial Intelligence (AI) comes out as a powerful solution. This article serves to provide an overview of previously proposed AI-based solutions that can be applied to IoT networks to secure them. An industry-wide overview of security provisions is provided by categorizing IoT applications into three broad sectors, namely, healthcare, smart grid, and smart city. The survey strives to give a clear industry-oriented vision of the available AI solutions and address the requirement of an application-ready security survey in the field. © 2023 Bentham science Publishers.

关键词： Internet of things

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and Information technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of Electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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Open-source solutions for real-time data retrieval in industrial automation and IoT environments

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International Journal of Internet Manufacturing and Services 2025年第1期11卷 73-92页

作者： Hegde, Seema B. Reddy, Tadigotla Narendra Prasannan, Prasad Manjunath, K.V. Herbert, Mervin A. Rao, Shrikantha S. Department of Computer Science and Engineering Global Academy of Technology Bengaluru560098 India Smart Manufacturing Precision Machine Tools Central Manufacturing Technology Institute Bengaluru22 India Department of Industrial Engineering and Management Siddaganga Institute of Technology Tumkur572103 India Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India

Digitalisation of the manufacturing industries due to the implementation of the ‘industrial internet of things (IIOT)’ is a key enabler for improved productivity and reliability at a reduced labour cost. The industrial IOT connects all the industrial machines such as PLCs, CNCs, and robots through a robust network. The generated data by these end devices plays a vital role in industrial automation, however acquiring the data from machines specifically legacy machines using various communication protocols is the biggest challenge and costly process, especially for MSMEs. Hence this paper discusses the usage of the open-source framework for real-time data acquisition from industrial machines and its implication in Industry 4.0. The paper implements and validates the possibility of the usage of an open-source framework for data acquisition instead of vendor-specific licensed software using several test cases. The paper also validates and proves ‘Wireshark’ can be a universal open-source solution for data acquisition using any standard communication protocols from various vendor-specific machines. Hence this work provides a novel solution for the digitalisation of the MSME manufacturing industries efficiently at the reduced maintenance cost and improve their productivity. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cost reduction

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A literature study and performance gaps on Centralized server-based load balancing and routing strategies under cloud-IoT-WSN

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Australian Journal of Electrical and Electronics engineering 2024年第2期21卷 138-160页

作者： GSR, Yogaraja M N, Thippeswamy K, Venkatesh Information Science and Engineering S J C Institute of Technology Chickaballapura Karnataka India Ramaiah Institute of Technology Karnataka Bangalore India Computer Science & Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore India

Wireless Sensor Network (WSN) infrastructure has to be balanced for maintaining the Quality of Service (QoS) in the network. However, load balancing technology is complex for meeting the requirements of high flexibility and adaptability due to the conventional WSN architecture. The major complication for developing distributed techniques for these models is load balancing that is the major aim of this study. These distributed methodologies use routing decisions that must be efficient without the need for more information about the system, where one of the efficient techniques is probabilistic routing. This paper plans to conduct a critical literature review on a diverse centralised server for controlling the load balancing in the cloud and routing strategies in WSN as well as linked with an IoT systems. This study reviews a set of contributions regarding load balancing in the cloud and routing strategies in WSN with clear algorithmic analysis. The simulation tools and recorded performance measures are also analysed and sorted out. Finally, the existing research gaps and the future directions are expedited that to be adaptable to attain better protocol performance with a centralised server in balancing node traffic, load balancing in the cloud and improving the throughput of the entire network. ©, Engineers Australia.

关键词： Internet of things

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