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31st International Conference on Computational Linguistics, COLING 2025

作者： He, Qiyuan Wang, Yizhong Yu, Jianfei Wang, Wenya College of Computing and Data Science Nanyang Technological University Singapore Paul G. Allen School of Computer Science & Engineering University of Washington United States School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761964

Advancements in language models (LMs) have sparked interest in exploring their potential as knowledge bases (KBs) due to their high capability for storing huge amounts of factual knowledge and semantic understanding. However, existing studies face challenges in quantifying the extent of large-scale knowledge packed into LMs and lack systematic studies on LMs' structured reasoning capabilities over the infused knowledge. Addressing these gaps, our research investigates whether LMs can effectively act as large-scale KBs after training over an expansive set of world knowledge triplets via addressing the following three crucial questions: (1) How do LMs of different sizes perform at storing world knowledge of different frequencies in a large-scale KB? (2) How flexible are these LMs in recalling the stored knowledge when prompted with natural language queries? (3) After training on the abundant world knowledge, can LMs additionally gain the ability to reason over such information to infer new facts? Our findings indicate that while medium-scaled LMs hold promise as world knowledge bases capable of storing and responding with flexibility, enhancements in their reasoning capabilities are necessary to fully realize their potential. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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An Efficient Classification of Cassava Leaf Disease using VGG 16

An Efficient Classification of Cassava Leaf Disease using VG...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Soujanya, A. Cherukuvada, Srikanth Murugan, V. Ramya, Singaraju Agalya, K. Gayathri, S. Ibrahimpatnam India School of Computing SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai India CARE College of Engineering Department of Artificial Intelligence and Data Science Thayanur Trichy India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Samayapuram Trichy India

ISBN: (纸本)9798350319200

Cassava is an essential food item in the tropics, it may origin enormous productivity decreases in cassava, leading to significant financial losses and a shortage of basic foods. Small-scale farmers grow the essential food and nutrition crop cassava because it can withstand harsh conditions. In many African nations, it provides a substantial source of carbohydrates. Leaf infections can occasionally harm cassava crops, limiting total productivity and farmers' income. In this study, Pre trained models are utilized for the determination of disease identification and classification on plant leaf issues. VGG 16 has been put into use, and its performance has been examined. An exploratory study is carried out using an online Cassava image collection, which contains pictures of Cassava leaves afflicted with five different disease categories. The vgg16 classifier that was suggested surpasses its competitors greatly and achieves an accuracy of 97.58%. The results of the test demonstrate that improving classification performance by using a database of images that is well balanced is possible. © 2023 IEEE.

关键词： Classification CNN Deep learning Transfer learning VGG16

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Detecting and Diagnosing Alzheimer's Disease: A Transformer-Based Survey (DAD-T) 4

Detecting and Diagnosing Alzheimer's Disease: A Transformer-...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Selvam, Prabu Gunasundari, C. Tamilselvi, M. Haneef, Pinjari Suresh, T. Jini, R. School of Computing Srm Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India Roever Engineering College Department of Computer Science & Engineering Tiruchirapalli India India K. Ramakrishnan College of Engineering Department of Aiml Tiruchirapalli India Sri Muthukumaran Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

Alzheimer's Disease (AD) represents a significant health challenge in today's world. Applying deep learning across various disciplines has demonstrated remarkable potential and has garnered substantial interest. The Transformer model has been shown to have exceptional performance across various computer vision tasks, significantly enhancing its application in medical imaging. Compared to conventional CNN, RNNs, and traditional deep learning algorithms, the Transformer network has demonstrated encouraging results in numerous studies and tasks. This paper reviews multiple works for Mild Cognitive Impairment, early diagnosis, AD and related conditions in the Transformer network over the last half-decade. This paper also presents a comprehensive overview of the data sources, pre-processing, feature extraction, training protocols, and evaluation methods to serve as a resource for future research on AD. Additionally, this survey offers a performance comparison of Transformer models across different datasets, a concise examination of these challenges and suggests potential directions for future research. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Zero Trust Networks: Evolution and Application from Concept to Practice

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Computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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Smart Plant Irrigation System with Arduino Uno and Soil Moisture Sensor 2

Smart Plant Irrigation System with Arduino Uno and Soil Mois...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Patni, Jagdish Chandra Surya Prakash, Vuppu Vardhan Reddy, Gajjala Sri Snehith Santhosh, Anjana Reddy, Yammanuru Chaitanya Bhaskarrao Bahadure, Nilesh Srivastava, Akhilesh Kumar Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India ABES Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

Automatic Smart Plant Watering System is a smarter solution that works on Arduino Uno, which can automatically operate for plant watering at optimal moisture conditions of soil. It checks the moisture with the help of a soil moisture sensor and, once the level of moisture goes down below a threshold, initiates a water pump. This user-friendly system is also energy efficient, making it suitable for busy or travel users, further contributing to sustainability plant care, thereby making it ideal for residential and nursery settings while emphasizing the promising use of smart agriculture and IoT technologies. This paper focuses on developing a smart water framework designed to efficiently manage water distribution based on real-time soil and environmental conditions. © 2025 IEEE.

关键词： Soil moisture

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Machine Learning Aided Signal Detection in Underwater Wireless Optical Communication for IoUT applications

Machine Learning Aided Signal Detection in Underwater Wirele...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer engineering, ICSSEEC 2024

作者： Kamatchi, Kavitha Angayarkanni, V. Paramanandham, Nirmala Yogarajan, G. Krishnan, Prabu Velammal College of Engineering and Technology Department of ECE Tamilnadu India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu India School of Electronics Engineering Vellore Institue of Technology Tamilnadu India Mepco Schlenk Engineering College Department of Information Technology Tamilnadu India National Institute of Technology Karnataka Department of ECE Surathkal India

ISBN: (纸本)9798350378177

This study explores the effectiveness of the Machine Learning (ML) algorithms in addressing channel impairments in underwater Wireless Optical Communication (UWOC) systems employing On-Off Keying (OOK) modulation. The simulation takes into account underwater challenges such as signal attenuation, scattering, and absorption by using the Gamma-Gamma distribution to model fading and scintillation effects. The ML algorithm's performance is assessed by comparing its bit error rate (BER) against the signal-to-noise ratio (SNR) in comparison to the ideal scenario with perfect channel state information (CSI). The simulation covers various underwater scenarios, including ocean water, harbor water, clear water, coastal water, and oligotrophic water, showcasing the algorithm's adaptability in diverse environmental conditions. The results indicate that the SVM algorithm closely approaches the BER performance achieved with CSI, demonstrating its potential to improve communication reliability in UWOC systems under realistic channel conditions. This study provides valuable insights into the application of machine learning for signal detection in UWOC, offering prospects for enhanced underwater communication performance. © 2024 IEEE.

关键词： Signal to noise ratio

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Multi-Keyword Graded Exploration in Encrypted Cloud Data for Industries Based on Rc4+ and Forest 1

Multi-Keyword Graded Exploration in Encrypted Cloud Data for...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Balamurugan, P. Suresh Thangakrishnan, M. Arulkumaran, G. Jayagopalan, Santhosh School of Computing Dept. of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Einstein College of Engineering CSE India School of Computing and Information Technology REVA University Bengaluru India British Applied College Department of Computing Umm Al Quwain United Arab Emirates

ISBN: (纸本)9798350345957

The vast amounts of storage space offered by the cloud encourage businesses to store their most sensitive files and data on remote servers accessible over the internet. When comparing a local server and personal computer to a cloud environment, it's clear that many more tasks can be accomplished with the former. Cost, speed, global scalability, productivity, and performance are just a few of the six reasons why cloud services have gained popularity among businesses and consumers alike in recent years. This paper presents the River cypher RC4+ (RCPF) scheme for fast, efficient, and secure multi-keyword search indexing in an encrypted cloud environment. In order to encrypt and decrypt data, the RC4+ algorithm uses a final symmetric secret key that is created by a pseudorandom byte generator. In order to generate an index list for each file in a shorter amount of time, a minimal spanning tree is formed. After that, the encrypted index list is searched by the cloud server, and relevant files are ranked according to the search query. The user's decryption of the encrypted data using the symmetric key behaves as a whole. © 2023 IEEE.

关键词： Cryptography

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AI-Driven Prediction and Data Analytics for Neurological Disorders—A Case Study of Multiple Sclerosis 4th

AI-Driven Prediction and Data Analytics for Neurological Dis...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Vergis, Natasha Shrivastava, Sanskriti Srinivas, L.N.B. Jayavel, Kayalvizhi Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur Tamilnadu Chennai603203 India

ISBN: (纸本)9789819900466

Multiple Sclerosis is a central nervous system chronic condition wherein your own immune system attacks the myelin protecting the nerve fibers. MS happens to be the second leading disability cause for the world's youth. Hence to prevent disability, it is very important to choose an effective treatment. However, how patients respond to these treatments can be very different. Due to this, a crucial necessity of an effective individualized treatment is the fast and accurate identification of the unique response to said treatment. This paper hopes to combat the need for a more accurate prediction model in the identification of MS with MRI scans. Magnetic resonance imaging (MRI) is widely used among doctors as one of the screening methods in the diagnosis of MS. Physicians may use MRI modalities to get basic information about brain function and structure, which is critical for an early identification of MS lesions. Using MRI to detect MS is laborious, takes a long time, and is susceptible to human error. We look to implement a Computer-Aided Diagnosis System (CADS) to diagnose MS using MRI in a more efficient manner using a 2D CNN model. Manipulation of preprocessing techniques like data augmentation, denoising, rotation, and the like will be done. A seven-layer Convolutional Neural Network model that includes Dropout and Batch normalization has been implemented. When compared to traditional machine learning, Deep Learning-based CADS performed better in identifying MS utilizing MRI scans. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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