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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Shashidhar, S. Sunil, C.K. Vineetha, B. Govt. First Grade College Department of Computer Science Karnataka Manvi India Indian Institute of Information Technology Department of Computer Science and Engineering Karnataka Dharwad India Presidency University Presidency School of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798331505776

Digital twins (DTs) have developed as a transformative technology in smart farming, facilitating real-time simulation, monitoring, and optimization of agricultural processes. This survey explores DTs' definition and their various applications in agriculture, underscoring their ability to enhance decision-making and upsurge farm productivity. It analyses the existing surveys and explains different use cases, highlighting the projects that exemplify the DT's practical integration in smart farming systems. The role of the Internet of Things (IoT) and machine learning (ML) in DT is also discussed for creating accurate and efficient DTs for precision agriculture. To identify gaps and areas for further research, a comparative analysis of these technologies is presented. This comprehensive survey intends to deliver understandings into the present state of DTs in smart farming, addressing challenges and providing directions for future development to improve agricultural practices. © 2024 IEEE.

关键词： Smart agriculture

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IoT-Based Smart Mask Protection against the Waves of COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11153-11164页

作者： Goar, Vishal Sharma, Aditi Yadav, Nagendra Singh Chowdhury, Subrata Hu, Yu-Chen Govt. Engineering College Bikaner Rajasthan Bikaner India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India Department of Computer Science & Applications SVCET Engineering College Andra Pradesh Chittoor India Department of Computer Science and Information Management Providence University 200 Sec. 7 Boulevard Shalu Dist Taichung City43301 Taiwan

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytic

关键词： COVID-19

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Machine learning-based data processing for predictive modeling in mechanical systems

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Life Cycle Reliability and Safety engineering 2025年 1-10页

作者： Mustafa, Jiyaul Ahmad, Shahnawaz Hussain, Shahadat Department of Mechanical Engineering SEAS Bennett University (The Times Group) Greater Noida 201310 India Department of Computer Science Engineering and Technology Bennett University (The Times Group) Greater Noida 201310 India Science Technology and Technical Education Department Govt. of Bihar Patna India

Machine learning (ML) has become the prevalent practice in the field of predictive modeling in mechanical systems, which allows the identification of performance patterns and detection of early signs of a malfunction. The use of MS together with conventional mechanical approaches like finite element evaluation has improved structural analysis and failure assessment. Furthermore, the ML has revealed fresh sets of material properties enabling the creation of new and unique materials since ML scans a vast database to uncover new potentialities. When we have appropriate specifications for mechanical material and apply ML algorithms to the design process, the correct combinations of elements and configurations are revealed. The paper also highlights the difficulties encountered when dealing with big and tremendously large data, the significance of preprocessing in training and how the various categories of training Learning;Supervised, Unsupervised and Reinforcement learning apply to material design. This work offers a great overview of the current use of ML in analyzing mechanical systems and material science and presents a possible trajectory for further development of the predictive modeling and material design. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.

关键词： Data preprocessing Feature extraction Machine learning Material design Predictive modeling

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Anemia Identification from Blood Smear Images Using Deep Learning: An XAI Approach 1

Anemia Identification from Blood Smear Images Using Deep Lea...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Sajith, Sneha Pooja, A. Ramesh, Theertha Rajpal, Parvathi Roshna, A.R. Ahammad, Javed Asjad Nabeel, P. Govt. College of Engineering Department of Computer Science Kerala Kannur India IQRAA International Hospital & Research Centre Kerala Kozhikode India

ISBN: (纸本)9798350306637

Anemia is a prevalent hematologic disorder characterized by a reduction in the number of red blood cells or a decrease in their oxygen-carrying capacity. Accurate and timely diagnosis of anemia is crucial for effective patient management and improved health outcomes. In this proposed work, we developed a CNN-based model to classify individuals as having symptoms of Iron-deficiency Anemia (IDA) or not, using blood smear images. The CNN model will be trained and fine-tuned using this dataset to maximize the classification accuracy. This helps healthcare professionals to understand the system's decisions and assist in the diagnosis and management of anemia. Despite a relatively limited number of data samples, our proposed work showed promising results by leveraging augmentation techniques. We achieved an accuracy of 0.95, with precision and recall values of 0.9 and 0.89, respectively. The area under the Receiver Operating Characteristic (ROC) curve is reported as 0.98. © 2023 IEEE.

关键词： Deep learning

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Deep Neural Network-Based Fingerprint Reformation for Minimizing Displacement 12

Deep Neural Network-Based Fingerprint Reformation for Minimi...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Kumar, Munish Kumar, Sandeep Gulhane, Monali Beniwal, Rajender Kumar Nitin Choudhary, Shilpha Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Department of Cse Pune India Electrical and Electronics Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar India Govt. Polytechnic Department of Computer Engg. Haryana Hisar India Hyderabad India

ISBN: (纸本)9798350369861

In the realm of biometric authentication and forensic analysis, the accurate matching of fingerprints is paramount. Fingerprint displacement, caused by factors such as skin elasticity and pressure during touch, has been a significant challenge in achieving precise fingerprint recognition both during the enrollment and the authentication process. This research presents a technique to address this issue by leveraging Support Vector Machines (SVM) for fingerprint transformation and displacement minimization. The proposed methodology involves the extraction of distinctive fingerprint features and the application of SVM-based algorithms to realign and correct fingerprint distortions resulting from displacement. We analyze the effectiveness of SVM in reducing displacement-induced errors and improving matching accuracy. Experimental results demonstrate the potential of SVM-based fingerprint transformation techniques to significantly enhance the robustness and reliability of fingerprint recognition systems. This research contributes to the ongoing efforts in biometrics and forensic science by providing a practical solution to the problem of fingerprint displacement, with potential applications in identity verification, criminal investigations, and security systems. The findings of this study offer a promising avenue for further research and development in the field of biometric authentication, ultimately leading to more accurate and secure identification methods. © 2023 IEEE.

关键词： Support vector machines

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Fingerprint Image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation 4

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Choudhary, Suresh School of Engineering & System Sciences Mds University Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Govt. Engineering College Department of Computer Applications Bikaner India Aryabhattt College of Engieering and research Centre India Mds University Ajmer India

ISBN: (纸本)9798331540364

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness. © 2024 IEEE.

关键词： Gabor filters

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Peak Pattern Classification in Mass Spectrometer Data using Deep Learning Techniques 1

Peak Pattern Classification in Mass Spectrometer Data using ...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Ushas.A.K Dhanya, M.B. Panachakel, Jerrin Thomas Deepak, S. Viji, H. Thampi, Smitha V. Satheesh Thampi, R. College of Engineering Trivandrum Department of CSE Thiruvananthapuram India ISRO Space Physics Laboratory VSSC Thiruvananthapuram India Govt. Engineering College Department of ECE Thrissur India Computer & IT Group VSSC ISRO Thiruvananthapuram India

ISBN: (纸本)9798350371802

Mass spectrometry is a powerful analytical tool that measures the mass-to-charge ratio (m/q) of ions. Its applications span diverse fields, from environmental analysis on Earth to deciphering planetary atmospheres in space missions. CHandra's Atmospheric Composition Explorer-2 (CHACE-2) is a neutral gas mass spectrometer deployed on the Chandrayaan-2 orbiter, identifies the lunar exosphere's composition by analyzing the m/q ratios of collected gas samples. However, manually interpreting these mass spectra to pinpoint specific elements is time-consuming. This study presents deep learning techniques, particularly ID Convolutional Neural Networks (ID CNNs) and a ID CNN-Long Short-Term Memory (LSTM) hybrid model, to automate peak pattern identification. CNNs demonstrate particular proficiency in extracting local features within the data, while LSTMs capture temporal dependencies. By leveraging deep learning, this approach offers significant potential for rapid and accurate identification of specific peak patterns in mass spectrometer data. © 2024 IEEE.

关键词： Long short-term memory

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Performance Evaluation of Bit-String Normalisation of Fingerprint Database

Performance Evaluation of Bit-String Normalisation of Finger...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Chauhan, Kapil Panda, Sankarsan School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (纸本)9798350374933

This study presents a comprehensive methodology for fingerprint analysis, employing a sequence of advanced image processing techniques including preprocessing, skeletonization, and minutiae extraction, leading to the generation of a digital bit-string representation. By enhancing image quality through histogram equalization and adaptive thresholding, and by refining fingerprint ridge patterns to single pixel widths via skeletonization, the proposed techniques facilitate precise minutiae extraction. These steps significantly improve the accuracy and reliability of biometric identification systems, as demonstrated by the dense and accurate distribution of minutiae points. The resultant digital encoding supports efficient data storage and rapid matching processes, essential for robust biometric verification. This research underscores the potential of these methodologies to enhance the performance of fingerprint-based security systems and lays the groundwork for future integrations with machine learning for automated and scalable biometric solutions. © 2024 IEEE.

关键词： Musculoskeletal system

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Sparse Vectors Based Load Flow Solution in Radial Power Distribution Systems

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Journal of engineering Science and Technology Review 2022年第6期15卷 201-206页

作者： Bhaskar, Pullagura Reddy, G. Nageswara Electrical and Electronics Engineering Section Govt. Polytechnic College for Woman Andhra Pradesh Kadapa India Department of Electrical and Electronics Engineering YSR Engineering College of Yogi Vemana University Andhra Pradesh Proddatur India

A load flow technique for the radial power distribution (RPD) system is proposed to find the receiving bus voltages using distribution system lines impedance and the sum of the load currents flowing throw the distribution line. The RPD system structure identifies by forming the three sparse matrices (vertical vectors without any zeros) to determine the buses connected to a particular distribution system line. The method is implemented on the 33-bus and 69-bus RPD systems with realistic loads and compared the efficacy of the proposed method with available existing methods © 2022 School of Science, IHU. All Rights Reserved.

关键词： Electric power distribution

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IoT-Enabled Secure Energy Management System in Smart Meters with CRFN: A Novel Approach to Billing and Theft Prevention

IoT-Enabled Secure Energy Management System in Smart Meters ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Punitha, S. Subalakshmi, N. Kavitha, K. Jeyakarthic, M. Raak College of Engineering and Technology Department of Computer Science and Engineering Puducherry India D. G. Govt. Arts College for Women Department of Computer Science Tamilnadu Mayiladuthurai India Annamalai University Department of Computer and Information Science Tamilnadu Chidambaram India Annamalai University Department of Electrical & Electronics Engineering Chidambaram India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

In response to the challenges posed by missed electricity bill payments and unauthorized power consumption, this study introduces enhancements to existing smart energy meter systems. The proposed improvements include the integration of advanced security features and automation capabilities, such as refined theft control mechanisms and optimized communication protocols between energy providers and consumers. Leveraging IoT technology, the system enables real-time energy monitoring, automatic billing, and timely payment reminders via SMS. Additionally, innovative theft prevention measures, including automated power disconnection for overdue accounts and theft detection systems, bolster security and reduce losses. The integration of the Convolutive Recurrent Fusion Network (CRFN) algorithm significantly enhances the system's theft detection and prevention capabilities. The CRFN detects anomalies in power consumption that may indicate theft, such as sudden spikes or drops in usage, through advanced pattern recognition. Upon detecting potential theft, the system can trigger automated responses like alerts or power disconnection, reducing the need for manual intervention. The incorporation of a magnetic reed switch further enhances efficiency by automatically controlling power access for consumers who have not paid their bills by the due date. By addressing these challenges and integrating advancements, the proposed framework aims to create a more efficient, secure, and sustainable energy management solution, ensuring accurate billing, preventing unauthorized usage, and promoting environmental responsibility. © 2024 IEEE.

关键词： Reed relays

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