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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Varpe, Kanchan Sakhare, Sachin Joshi, Rahul Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance. © 2025 IEEE.

关键词： Convolutional neural networks

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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YOLO-LFD: A Lightweight and Fast Model for Forest Fire Detection

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computers, Materials & Continua 2025年第2期82卷 3399-3417页

作者： Honglin Wang Yangyang Zhang Cheng Zhu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Electrical&Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA

Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction network. Secondly, we design and implement the Lightweight Faster Implementation of Cross Stage Partial (CSP) Bottleneck with 2 Convolutions (C2f-Light) and the CSP Structure with 3 Compact Inverted Blocks (C3CIB) modules to replace the traditional C3 modules. This optimization enhances deep feature extraction and semantic information processing, thereby significantly increasing inference speed. To enhance the detection capability for small fires, the model employs a Normalized Wasserstein Distance (NWD) loss function, which effectively reduces the missed detection rate and improves the accuracy of detecting small fire sources. Experimental results demonstrate that compared to the baseline YOLOv5s model, the YOLO-LFD model not only increases inference speed by 19.3% but also significantly improves the detection accuracy for small fire targets, with only a 1.6% reduction in overall mean average precision (mAP)@0.5. Through these innovative improvements to YOLOv5s, the YOLO-LFD model achieves a balance between speed and accuracy, making it particularly suitable for real-time detection tasks on mobile and embedded devices.

关键词： Forest fire detection YOLOv5 lightweight small object detection

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Fake News Detection on Social Media Using Ensemble Methods

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computers, Materials & Continua 2024年第12期81卷 4525-4549页

作者： Muhammad Ali Ilyas Abdul Rehman Assad Abbas Dongsun Kim Muhammad Tahir Naseem Nasro Min Allah Department of Computer Science COMSATS UniversityIslamabad45550Pakistan School of Computer Science and Engineering Kyungpook National UniversityDaegu41566Republic of Korea Department of Computer Science and Engineering Korea UniversitySeoul02841Republic of Korea Department of Electronic Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam34223Saudi Arabia

In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble

关键词： Fake news detection Machine Learning(ML) Deep Learning(DL) Chi-Square ensembling

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AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第8期18卷 67-86页

作者： Quttoum, Ahmad Nahar Alsarhan, Ayoub Moh’d, Abidalrahman Aljaidi, Mohammad Samara, Ghassan Alshammari, Muteb Department of Computer Engineering The Hashemite University Zarqa Jordan Department of Information Technology The Hashemite University Zarqa Jordan Department of Math and Computer Science Eastern Illinois University CharlestonIL United States Department of Computer Science Zarqa University Zarqa Jordan Department of Information Technology Northern Border University Arar Saudi Arabia

Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant;however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising;however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to t

关键词： Navigation systems

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A Study of Blockchain-Based Internet of Things

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2023年第1期4卷 15-23页

作者： Qamar, Roheen Zardari, Baqir Ali Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan Department of Information Technology Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan

Blockchain and the Internet of Things (IoT), two of the most emerging technologies, are already reconfiguring our digital future, as described by the drastic change in the current network architecture. The incorporation of IoT has brought the objects around us to life, making them "smart" and capable of communicating with one another, thereby amassing massive data by consistently acquiring the physical world for analysis and intelligent action. It has made our dream of seamless integration of the digital and physical worlds a reality, altering the essence of our perception of the physical world. However, the issue with current IoT solutions is the requirement for a centrally controlled party (similar to a cloud server). This research covers the most essential implications of IoT’s most challenging scenarios, as revealed by the current studies, when connecting and communicating via the Internet. The vast amounts of generated sensitive data pose a significant risk to privacy and security;interestingly, the original architecture design calls for a decentralized system, such as the distributed or peer-to-peer (P2P) system. This scenario highlights the relevance of the blockchain, as it can offer a secure and trustworthy method of sharing information via a distributed/P2P model for achieving transparency, security, privacy, auditability, resilience, access authentication, data immutability, and so on. In this study, we explain how blockchain and IoT technologies are combined to overcome their specific shortcomings and ultimately achieve their maximum benefits. This research also discusses the technical aspects of IoT and bitcoin. We present a comprehensive overview of both technologies, including the blockchain-based IoT (BIoT) architecture and operation, and a few examples of BIoT applications and their comparison. © 2023 Authors. All rights reserved.

关键词： Internet of things

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NJmat 2.0:User Instructions of Data-Driven Machine Learning Interface for Materials science

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computers, Materials & Continua 2025年第4期83卷 1-11页

作者： Lei Zhang Hangyuan Deng Department of Internet Engineering School of Software EngineeringNanjing University of Information Science&TechnologyNanjing210044China Department of Materials Physics School of Chemistry and Materials ScienceNanjing University of Information Science&TechnologyNanjing210044China

NJmat is a user-friendly,data-driven machine learning interface designed for materials design and *** platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials *** platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular *** automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural ***,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding *** automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials *** latest version(available at https://***/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction *** utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials *** NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training *** providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.

关键词： Data-driven machine learning natural language processing machine learning potential large language model

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A privacy-preserving deep learning framework for highly authenticated blockchain secure storage system

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Multimedia Tools and Applications 2024年第36期83卷 84299-84329页

作者： Haque, Sheikh Moeen Ul Sofi, Shabir Ahamd Sholla, Sahil Department of Information Technology NIT Srinagar Jammu and Kashmir Hazratbal190006 India Department of Computer Science and Engineering Islamic University of Science and Technology Jammu and Kashmir Awantipora192122 India

To ensure the privacy, integrity, and security of the user data and to prevent the unauthorized access of data by illegal users in the blockchain storage system is significant. Blockchain networks are widely used for the authentication of data between the data user and the data owner. However, blockchain networks are vulnerable to potential privacy risks and security issues concerned with the data transfer and the logging of data transactions. To overcome these challenges and enhance the security associated with blockchain storage systems, this research develops a highly authenticated secure blockchain storage system utilizing a rider search optimized deep Convolution Neural Network(CNN) model. The architecture integrates the Ethereum blockchain, Interplanetary File System (IPFS), data users, and owners, in which the Smart contracts eliminate intermediaries, bolstering user-owner interactions. In tandem, blockchain ensures immutable transaction records, and merging IPFS with blockchain enables off-chain, distributed storage of data, with hash records on the blockchain. The research accomplishes privacy preservation through six-phase network development: system establishment, registration, encryption, token generation, testing, and decryption. Parameters for secure transactions are initialized, user registration provides genuine user transaction credentials, and encryption guarantees data security, employing optimized Elliptic Curve Cryptography (ECC). Further, the optimized ECC algorithm is developed utilizing a novel rider search optimization that utilizes search and rescue characteristics of human, and rider characteristics for determining the shorter key lengths. Token generation involves issuing digital tokens on a blockchain platform, followed by testing using a deep CNN classifier to detect anomalies and prevent unauthorized data access during the test phase. The decryption of data is conducted for registered users. The developed rider search optimized deep CN

关键词： Blockchain

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Transfer Learning Empowered Skin Diseases Detection in Children

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computer Modeling in engineering & sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

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Improved PBFT Consensus Algorithm Based on Threshold Signature and RabbitMQ

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1476-1490页

作者： Xu, Xiang Liu, Chun-Yuan School of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin Heilongjiang150022 China

Due to its decentralized and tamper-proof features, blockchain is frequently employed in the financial, traceability, and distributed storage industries. The agreement algorithm, which is a crucial component of the blockchain distributed storage system, is responsible for maintaining data integrity and ensuring system security. It also has a significant impact on the blockchain system’s transaction throughput and confirmation time. Compared with public chain, alliance chain has fewer nodes and access mechanism, so PBFT consensus algorithm based on voting is generally used. However, there are still many shortcomings in the practical application of this algorithm. To solve the problem, an improved Byzantine consensus algorithm (TRPBFT) based on threshold signature and RabbitMQ is proposed in this paper. The experimental findings demonstrate that the enhanced consensus algorithm significantly increases both consensus efficiency and data throughput. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Authentication Protocol

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