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Electronic Systems and Intelligent computing (ICESIC), International Conference on

作者： P. Malathi S. Rukmani Devi K. Senbagam T. Thirumalaikumari H. Anwer Basha B. Thamizhkani Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Chennai Tamil Nadu India Department of Information Technology School of Computing Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of EEE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Plant diseases and unpredictable yields are two issues that jasmine flower production must deal with, necessitating cutting-edge solutions for effective crop management. It provides a hybrid approach for plant health monitoring and jasmine yield prediction based on machine learning. For yield prediction, a Random Forest algorithm is used, and for illness identification via picture analysis, a Convolutional Neural Network (CNN) is deployed. For realtime monitoring, leaf photos and data from IoT sensors such as temperature, humidity, and soil moisture are analyzed. The suggested model outperforms conventional models in terms of accuracy, with a Mean Absolute Error (MAE) of 7.3 and an accuracy of 89% for yield prediction. CNN's F1-score for identifying plant diseases was 90.5%. With the help of TensorFlow and MATLAB Simulink, the system is implemented and offers farmers useful information. This all-inclusive solution improves crop management efficiency in jasmine production by optimizing yield projections and early disease detection.

关键词： Temperature sensors Plant diseases Accuracy Production Predictive models Flowering plants Mathematical models Convolutional neural networks Monitoring Random forests

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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Machine-learning-assisted intelligent synthesis of UiO-66(Ce):Balancing the trade-off between structural defects and thermal stability for e hydrogenation of Dicyclopentadiene

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Materials Genome Engineering Advances 2024年第3期2卷 96-106页

作者： Jing Lin Tao Ban Tian Li Ye Sun Shenglan Zhou Rushuo Li Yanjing Su Jitti Kasemchainan Hongyi Gao Lei Shi Ge Wang Beijing Key Laboratory of Function Materials for Molecule&Structure Construction Beijing Advanced Innovation Center for Materials Genome EngineeringSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and EngineeringBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologySchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijingChina Department of Chemical Technology Chulalongkorn UniversityBangkokThailand

Metal-organic frameworks(MOFs),renowned for structural diversity and design flexibility,exhibit potential in ***,the pursuit of higher catalytic activity through defects often compromises stability,requiring a delicate *** trial-and-error method for optimizing synthesis parameters within the complex chemical space is ***,taking the typical MOF UiO-66(Ce)as an illustrative example,a closed loop workflow is built,which integrates ma-chine learning(ML)-assissted prediction,multi-objective optimization(MOO)and experimental preparation to synergistically optimize the defect content and thermal stability of UiO-66(Ce)for efficient hydrogenation of dicyclopentadiene(DCPD).An automatic data extraction program ensures data accuracy,establishing a high-quality *** is employed to explore the intricate synthesis-structure-property correlations,enabling precise delineation of pure-phase subspace and accurate predictions of *** two iterations,MOO model identifies optimal protocols for high defect content(>40%)and thermal stability(>300℃).The optimized UiO-66(Ce)exhibits superior catalytic performance in hydroge-nation of DCPD,validating the precision and reliability of our *** ML-assisted approach offers a valuable paradigm for solving the trade-off riddle in materials field.

关键词： defect content machine learning metal-organic frameworks multi-objective optimization thermal stability

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Integrating Machine Learning Models for Real-Time Pollution Monitoring in Smart Cities 8

Integrating Machine Learning Models for Real-Time Pollution ...

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8th IET Smart Cities Symposium, SCS 2024

作者： Ravi, R. Saravanan, Matheswaran Jayakumar, Veni Sivaraman, V. Amirthalingam, V. Sengodan, Prabaharan Dept of CSE J.J College of Engineering and Technology India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology India India Department of Management studies JJ college of engineering and technology India Department of Computer Science & Engineering – Cyber Security Madanapalle Institute of Technology & Science Andhra Pradesh India Vinayaka Mission's Kirupananda Variyar Engineering College Salem India School of Computing Galgotias University Noida India

ISBN: (纸本)9781837242672

The integration of machine learning (ML) models into smart city infrastructures offers a robust solution for real-time pollution monitoring. This paper presents innovative approaches, including data collection, preprocessing, model deployment, and adaptive learning techniques. Through comprehensive experiments, our study demonstrates the efficacy of various ML models. The Gradient Boosting model achieved the highest accuracy with an R-squared value of 0.93, MAE of 2.9 μg/m³, and RMSE of 4.2 μg/m^3. The DBSCAN algorithm effectively identified pollution hotspots with a silhouette score of 0.71 and a Davies-Bouldin index of 0.35. The Deep Q-Network (DQN) optimized pollution control strategies, achieving a cumulative reward of 1400 and a convergence rate of 90%. An ensemble model combining Random Forest and Gradient Boosting achieved an R-squared value of 0.94. These results underscore the potential of ML models to enhance pollution monitoring and management in smart cities, improving urban air quality and public health. © The Institution of Engineering & Technology 2024.

关键词： DBSCAN Deep Q-Network Gradient Boosting Smart cities

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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A Unified Approach for Binary-Class and Multi-Class Data Augmented Generation

A Unified Approach for Binary-Class and Multi-Class Data Aug...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Frederic Rizk Rodrigue Rizk Dominick Rizk Patrick Rizk Chee-Hung Henry Chu The Center for Advanced Computer Studies University of Louisiana at Lafayette Lafayette LA U.S.A. Department of Computer Science University of South Dakota Vermillion SD U.S.A. Department of Electrical Engineering and Computer Science The Catholic University of America Washington DC U.S.A. Department of Mechanical Engineering University of Quebec at Trois-Rivières Drummondville QC Canada The Center for Advanced Computer Studies Informatics Research Institute University of Louisiana at Lafayette Lafayette LA U.S.A.

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Deep neural networks excel in a wide range of tasks but require diverse datasets to prevent overfitting. Overfitting occurs when a network fits training data too precisely, leading to poor generalization. Data Augmentation is often used to mitigate overfitting aiming at enlarging and improving the quality of training datasets, facilitating the construction of superior deep learning models. MAGAN algorithm emerges as an innovative approach that functions as a Meta-Analysis of Generative Adversarial Networks (GANs). MAGAN harnesses the latent space capabilities of GANs to confront the challenges presented by binary-class, multi-class, grayscale, and RGB images, effectively covering a wide spectrum of scenarios. In this paper, we propose the use of MAGAN algorithm for binary-class and multi-class data augmented generation. We also undertake an in-depth experimental analysis, evaluating the performance of the proposed MAGAN-based approach in comparison to two alternative baseline scenarios: one without any augmentation and another utilizing a conventional augmentation method. To gauge the effectiveness of the proposed technique, we employed diverse classification metrics, including accuracy, loss, precision, recall, F1-score, and the confusion matrix. Our results demonstrate that the proposed approach surpasses the other two scenarios achieving improvements in terms of accuracy by a factor of x1.15 and x1.03, respectively. This underscores the significant advantages of harnessing MAGAN, a meta-analysis of GANs, for data augmentation across a range of image types and classification tasks.

关键词： Deep learning Measurement Accuracy Training data Artificial neural networks Gray-scale Data augmentation

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Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA Model with Feature Analysis

Intelligent Malicious URL Detection Using Kernel PCA-SVM-GA ...

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Data science and Network Security (ICDSNS), International Conference on

作者： Sandy Ariawan Arvind Kumar Chinthala Kumara Swamy V. Divya V Manikandan S. Rukmani Devi IAKN Tarutung Indonesia SCA Manav Rachna International Institute of Research and Studies Faridabad India CSE-DS Mallareddy College of Engineering Hyderabad India Department of Information Technology Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Dangerous websites, or rogue URLs, pose a serious threat to cybersecurity. Malicious URLs cost businesses and consumers alike billions of dollars annually because the system lure unsuspecting users into scams that steal personal information, cause financial loss, or install malware. Immediate action is required to detect and eliminate these threats. This form of detection has traditionally relied on blacklists. A major drawback of blacklists is that the system are not comprehensive and do not detect newly established harmful URLs. The procedure consists of three stages, which are preprocessing, feature extraction, and training the model. Important parts of data preprocessing when dealing with URLs are standardization and parsing. When it comes to detecting dangerous URLs using machine learning, feature extraction is a major concern. The proposed approach used a Kernal PCA-SVM-GA to train the model. The proposed approach beats both SVM and GA, with an average accuracy of 93.52%.

关键词： Uniform resource locators Training Support vector machines Accuracy Data preprocessing Standardization Machine learning Feature extraction Vectors Blocklists

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Product Life Cycle and Business Model Techniques to Promote Marketing in Healthcare Units 2

Product Life Cycle and Business Model Techniques to Promote ...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Mandala, Gangu Naidu Kasi Raman, R. Sharma, Monika Raj, M. Sundar Lavanyalakshmi, N. Mishra, Nilamadhab SVKM's Narsee Monjee Institute of Management Studies Department of Finance Bengaluru India P.S.R Engineering College Department of Management Studies Sivakasi India The Technological Institute of Textile & Sciences Department of Management Studies Haryana India Panimalar Engineering College Department of Mathematics Chennai India Jayaraj Annapackiam College for Women Department of Commerce with Computer Application Theni India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Attempting to execute a very intensive and costly eHealth program, judgment in the Indian health service has typically refused to look beyond information and communications technology. As a result, the properly functioning assimilation of scheduled communication and information technology alternatives with different business components of the health framework has largely been ignored, whereas the Indian healthcare program's established and outdated business strategy keeps going to be extremely limited but instead updated towards the vastly increased and widespread infiltration of communication and information technology into the healthcare setting. Such an editorial focus on the description and configuration of the conceptual model helps to identify the approaching consequences of industrialization on the health industry prototype, and eventually propose a method for transforming Slovenia's original healthcare market prototype using information communication technologies. The analysis is focusing on case research findings and structured questionnaires with eminent specialists from many parts of the medical system who are heavily involved with the business model idea. According to the findings, an effective transition of the healthcare market prototype should take into account a list of criteria from the health system, such as a reconceptualization of the operations and relationship issues of and among the business model's main actors, and the retooling of organizational, structural, and technical components in line with long-term company goals. © 2022 IEEE.

关键词： Commerce

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Extraction of Actionable Threat Intelligence from Dark Web Data

Extraction of Actionable Threat Intelligence from Dark Web D...

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International Conference on Control Communication & computing India (ICCC)

作者： Varsha Varghese Mahalakshmi S Senthilkumar Kb Cyber Forensics and Information Security Department of Computer Science & Engineering ER&DCI Institute of Technology Thiruvananthapuram Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram Scientict_E Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of Cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based Cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making Cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities.

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A Study of Cryptographic Primitives in the context of Blockchain's Data Integrity and Privacy 11

A Study of Cryptographic Primitives in the context of Blockc...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Ramveer Pooja Agarwal, Ambuj Kumar Naaz, Rohaila Kumar, Raj Vijay, Richa Greater Noida Institute of Professional Studies Greater Noida India School of Engineering and Technology Ct University Ludhiana India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India Teerthanker Mahaveer University Faculty of Engineering and Computing Sciences Moradabad India Department of Computer Applications Faridabad India

ISBN: (纸本)9781665487320

In Today's World, Blockchain is a promising Technology in all areas;things have also been drastically changed after COVID-19;challenges surfaced for implementing blockchain technology in the context of its computational complexity and security. After invention of it in 2008, cryptocurrency applications, i.e., Bitcoin it also getting introduced in Different applications. One of the Major Building blocks of Blockchain is Cryptography techniques. This paper discusses Cryptographic primitives used in different phases of Blockchain implementation and the challenges of implementing these cryptographic primitives. Bitcoin uses Cryptographic primitives to generate public-private key pairs and mining, while Ethereum uses Elliptic curve cryptography to create public-private keys and digital signatures. This paper divided uses of cryptographic primitives into three categories used in Blockchains, i.e., key generation Hashes, signature generation hashes, and proof hashes. © 2022 IEEE.

关键词： Blockchain

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