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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chandankhede, Arpit Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Meghe Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Supply Chain Management (SCM) worldwide makes major advancements through the Internet of Things (IoT) by enabling real-time monitoring and prediction analytics and automatic decision capabilities in worldwide deliver chains. Through IoT technology integration with Artificial Intelligence (AI) and blockchain and cloud computing the supply chain solves major operational problems including monitoring difficulties and waste reduction and regulatory compliance requirements and operation leadership deficits. The implementation of IoT for supply chain management encounters major adoption barriers from high installation costs combined with security risks and operability complications as well as limited scalability capacity that affects especially small to medium enterprises (SMEs) and developing markets. The evaluation paper examines the shifting impact of IoT in supply chain management which enhances performance metrics and brings greater clarity and strengthens supply chain resilience. A scientist studied existing papers and case studies to reveal several essential advantages which include better inventory management, predictive analysis systems and more effective supply-chain collaborations. The analysis emphasizes effective solutions for security privacy and moral and environmental issues to guarantee sustainable expanded IoT adoption. This paper delivers practical knowledge alongside best practices to help organizations adopt IoT systems which create adaptable efficient deliver chains with sustainability at their core thus achieving long-term market benefits in today's complex global marketplace. © 2025 IEEE.

关键词： Inventory control

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB · Hz6/7

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Institute of Integrated Microelectronic Systems College of Information Science and Electronic Engineering Zhejiang University Faculty of Electrical Engineering and Computer Science Ningbo University

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic （MWP）systems. Conventional linearization schemes usually involve the fine tuning of radio-frequency （RF） power distribution, which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics chip. In this paper, we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is eliminated. The device consists of two carrier-depletion-based Mach–Zehnder modulators （MZMs）, which are connected in series by a 1×2 thermal optical switch （OS）.The OS is used to adjust the ratio between the modulation depths of the two sub-MZMs. Under a proper ratio, the complementary third-order intermodulation distortion （IMD3） of the two sub-MZMs can effectively cancel each other out. The measured spurious-free dynamic ranges for IMD3 are 131, 127, 118, 110,and 109 d B·Hz6∕7at frequencies of 1, 10, 20, 30, and 40 GHz, respectively, which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： Effective refractive index Integrated photonics Lithium niobate Modulation transfer function Optical signals Silicon modulators

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Assessment of ingestion dose from 226Ra, 228Ra, and 40K in vegetables consumed in Koya district, Iraq

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Environmental science and Pollution Research 2025年第19期32卷 11972-11980页

作者： Saleh, Dedawan Salam Salh, Hemn Smail, Jahfer Majeed Ahmad, Saddon Taha Department of Physics Faculty of Science and Health Koya University Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Faculty of Medicine Koya University KoyaKOY45 Iraq

The present work comprises the measurement of activity concentrations of three radionuclides, 22⁶Ra, 228Ra, and 4⁰K in 33 species of vegetables commonly used in Koya district, the Iraqi Kurdistan region. The analysis of the samples was performed with high purity germanium (HPGe) gamma spectrometry. Among the analyzed samples, mushrooms presented the highest activity concentrations of 22⁶Ra (3.34 ± 1.18 Bq·kg⁻1), 228Ra (1.24 ± 0.5 Bq·kg⁻1), and 4⁰K (1481.93 ± 14.46 Bq·kg⁻1) while legumes had the lowest levels of these radionuclides. The estimated annual effective dose from the consumption of these vegetables was computed to be 97.216 µSv·y⁻1 and this is much lower than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) global reference level of 290 µSv·y⁻1. Therefore, it can be concluded that the radiological risk from the consumption of vegetables in this area is very low thus giving a reassurance to the people on the safety implication on the radiological health hazards. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Radiation effects

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Implementation of Secure and Verifiable Access Control Procedures using the NTRU Cryptosystem to Store Big Data in the Cloud Environment

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Recent Patents on engineering 2025年第2期19卷 57-70页

作者： Babu B, Ravindra Saxena, Swati Widjaja, Gunawan Vinodha D, Vedha Omarov, Batyrkhan Ramaiah, Gurumurthy B. Vats, Prashant Adama Ethiopia Department of Computer Science KCG College of Technology Chennai India Universitas Krisnadwipayana Jawa Barat 13077 Indonesia Department of Electronics and Communication Engineering JCT College Of Engineering and Technology Pichanur Coimbatore India International University of Tourism and Hospitality Turkistan Kazakhstan Technical and Vocational Training Institute Addis Ababa Ethiopia Department of Computer Science and Engineering Faculty of Engineering and Technology SGT University Haryana Gurugram India

Background: Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores. Methods: However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility. Results: In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour. Conclusion: It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users. © 2025 Bentham science Publishers.

关键词： Cloud security

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Enhancing Customer Retention: A Federated Machine Learning Framework for Banking Churn Prediction 3

Enhancing Customer Retention: A Federated Machine Learning F...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Vanitha, M. Faritha Banu, J. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Bharathi Salai Ramapuram Tamil Nadu Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542375

Customer attrition has become the significant challenge for the bank, making large volume of customers to migrate to other banks, as the banks keeps providing multiple benefits to the incoming customers. The loss due to migration of the existing customer to the competitive bankers creates the banking churn, means of loss of customer relationship with the bank, and affects the development, business and the profitability of the corresponding bank. It is essential to predict the banking churn with a primary objective of retaining the customers, considered as a critical task. To achieve this objective, banking sectors employ the customer behavioral analysis to determine the rate of customer churn, resulting in incorrect diagnosis of the churn rate. To over this concern, this research manuscript proposes a novel federated Machine Learning (ML) framework for the prediction of the customer churn, directly contributes for the enhancement of the customer retention. This proposed novel framework offers significant advancements for the banking prediction on customer retention, thus provides an accurate tool for the analysis of customer relationships. The proposed prediction framework is analyzed in terms of accuracy, precision, recall, F1 score and the performance is compared with the state of the art prediction methodologies. © 2025 IEEE.

关键词： Federated learning

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PDNet: A Novel Approach to Plant Disease Detection using CNN and Keras 6

PDNet: A Novel Approach to Plant Disease Detection using CNN...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Jain, Garima Shukla, Amita Chaudhary, Anamika Sharma, Nidhi Bisht, Sovers Singh Jain, Ankush Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Delhi India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9798331522667

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing stencil printing in PCB production using deep learning based approach for residue classification and optimization

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jain, Swati Patel, Ruhi Desai, Manad Verma, Jai Prakash Yoon, Sang Won Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India Department of Systems Science and Industrial Engineering State University of New York at Binghamton Binghamton NY United States

Surface-mount technology (SMT) is the technology used in the production of printed circuit boards (PCB) plays a vital role in PCB manufacturing for applications ranging from communication devices to medical systems. A significant challenge in the stencil printing process, a critical step in PCB production, is solder paste buildup in stencil apertures, contributing to 57%–71% of soldering defects. To address this issue, this study presents a convolutional neural network (CNN)-based framework for categorizing solder paste residue levels into three classes: low, moderate, and high. The methodology involves cropping and pre-processing images of stencil apertures, which are then fed into the CNN model for classification. To enhance model robustness and prevent overfitting, data augmentation techniques, including aperture rotation, are applied, effectively increasing the dataset size and promoting generalization. The model achieves a classification accuracy of 87% during testing, with precision and recall values provided for each residue class. This approach aims to optimize the stencil printing process by enabling accurate residue categorization and facilitating targeted cleaning cycles, ultimately reducing soldering defects and improving PCB production efficiency. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： CNN Deep learning Printed sensing board (PCB) Residue build-up Stencil printing Surface mount technology (SMT) SVM VGG16

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Telugu Language Analysis with XLM-RoBERTa: Enhancing Parts of Speech Tagging for Effective Natural Language Processing

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SN computer science 2025年第2期6卷 1-13页

作者： Bharathi Mohan, G. Prasanna Kumar, R. Krishna Jayanth, K. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Gaborone Botswana

This research investigates the efficacy of XLM-RoBERTa, a potent deep learning architecture rooted in transformer networks, for Part-of-Speech (POS) tagging—a foundational task in Natural Language Processing (NLP). The study narrows its focus to address the formidable UCD Telugu dataset, renowned for its intricate morphological characteristics that pose challenges to accurate tagging. Through meticulous fine-tuning of the pre-trained XLM-RoBERTa model. This study adeptly discerns and exploit intricate syntactic and semantic patterns underpinning the interplay between lexical elements and their respective POS tags. The rigorous evaluation regimen, complemented by a comprehensive statistical comparison with the Multilingual BERT model, conclusively establishes XLM-RoBERTa’s superiority. This advanced model attains a remarkable accuracy score of 91%, decisively surpassing the Multilingual BERT model, which achieves an accuracy of 88%. These findings not only underscore XLM-RoBERTa’s prowess in precise POS tagging, especially in the context of morphologically rich languages like Telugu, but also carry significant implications for the broader NLP community. This research unlocks valuable insights and positions XLM-RoBERTa as an exemplar of reliability and robustness in POS tagging. Furthermore, it extends an invitation for further exploration and innovation in deep learning architectures within the NLP domain. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bert Multilingual Bert NLP Parts-of-speech tagging Telugu XLM-RoBERTa

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Design and Implementation of a Slant Polarized Wideband Vivaldi Antenna Array Feed for Monopulse Radar Reflectors

Design and Implementation of a Slant Polarized Wideband Viva...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Masoumi, Reza Kazemi, Robab Fathy, Aly E. University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Traditionally, conical ridge horn antennas are used for feeding large reflectors, but they can cause grating lobes in arrays. This paper introduces a compact Vivaldi antenna for monopulse radar, featuring a planar feed structure that offers a wider bandwidth and simplifies manufacturing. The innovative design of the Vivaldi antenna incorporates strategically placed grooves and a circular dielectric lens to enhance gain and reduce sidelobe levels. Operating from 6 GHz to 18 GHz with proper impedance matching, the antenna is 23 mm wide (0.9λ at 12 GHz), making it over 66% smaller than traditional designs. The antennas are angled in 2 × 2 array configuration to overlap, with a center-to-center spacing of 11.3 mm (0.68λ at 18 GHz), ensuring that no grating lobes are present across the entire bandwidth. This innovative antenna effectively feeds an elliptical reflector, providing high gain, broadband capabilities, and significant null depth, ensuring accurate and efficient performance in monopulse radar applications. © 2025 IEEE.

关键词： Monopulse radar

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