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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Wasankar, S.W. Jawandhiya, P.M. Sipna Coet Department of Computer Science & Engineering Amravati India Plit Department of Computer Science & Engineering Buldhana India

ISBN: (纸本)9798350391770

This study seems to propose an innovative approach to addressing the complexities of agricultural sustainability and productivity. By integrating graph-based Q-learning into two crucial aspects of agriculture - nutrient management and crop quality prediction - it offers a promising solution to optimize resource usage and enhance crop yield and quality. The utilization of Q-learning, a reinforcement learning technique, in constructing Nutrient Action and Quality-Action Graphs is particularly intriguing. These graphs seem to serve as dynamic frameworks for decision-making, allowing the algorithm to adapt and learn optimal strategies based on environmental conditions and crop requirements. The emphasis on tailoring nutrient management strategies to special crop and soil textures while taking environmental conditional factors like temperature and humidity underscores the importance of precision agriculture. This approach has the potential to revolutionize traditional fertilization and plant management practices by offering more tailored and adaptive solutions. Overall, combining of these models into agro decision-making processes holds promise for improving both productivity and sustainability in the face of fluctuating environmental conditions and increasing food demand. © 2024 IEEE.

关键词： Agricultural Productivity NAF Plant Quality Improvement Q-Learning QAF Reinforcement Learning

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Knowledge Navigator - Guiding You Through Your Learning Journey Using AI

Knowledge Navigator - Guiding You Through Your Learning Jour...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Yadav, Anup Lal Talwandi, Navjot Singh Kuhar, Sunny Kumar, Munish Chandigarh University Department Of Computer Science & Engineering Mohali India Piet Department Of Computer Science & Engineering Haryana India

ISBN: (纸本)9798350306446

It might be intimidating to navigate the wide array of learning resources in this era of information overload. This article presents Knowledge Navigator, an artificial intelligence (AI) system that adapts the learning process to the needs, preferences, and learning styles of each individual. Knowledge Navigator evaluates past knowledge, determines learning preferences, and comprehends user intent by combining sophisticated Natural Language Processing (NLP) and machine learning algorithms. Users are guaranteed to receive the most interesting and pertinent learning materials, according to their tastes and goals, thanks to this customized approach. The potential advantages of Knowledge Navigator, such as higher learning efficiency, better information retention, and increased student motivation, are discussed in the paper's conclusion. It also looks at potential research avenues in the future, such as gamification and integration with practical uses. © 2024 IEEE.

关键词： Learning algorithms

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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization 31

ROUGE-SciQFS: A ROUGE-based Method to Automatically Create D...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ramirez-Orta, Juan Milios, Evangelos Maguitman, Ana Soto, Axel J. Faculty of Computer Science Dalhousie University Canada Institute for Computer Science and Engineering UNS - CONICET Department of Computer Science and Engineering Universidad Nacional del Sur Argentina

ISBN: (纸本)9798891761964

So far, the task of Scientific Query-Focused Summarization (Sci-QFS) has lagged in development when compared to other areas of Scientific Natural Language Processing because of the lack of data. In this work, we propose a methodology to take advantage of existing collections of academic papers to obtain large-scale datasets for this task automatically. After applying it to the papers from our reading group, we introduce a novel dataset for Sci-QFS composed of 8,695 examples, each one with a query, the sentences of the full text from a paper and the relevance labels for each. After testing several classical and state-of-the-art embedding models on this data, we found that the task of Sci-QFS is far from being solved, although it is relatively straightforward for humans. Surprisingly, we found that classical methods outperformed modern pre-trained Deep Language Models (sometimes by a large margin), showing the need for large datasets to better fine-tune the latter. We share our experiments, data and models at https://***/jarobyte91/rouge_sciqfs. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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Morlet wavelet transformation based deep similarity structured neural learning for image quality assessment

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Multimedia Tools and Applications 2025年第16期84卷 16789-16807页

作者： Balakrishnan, N. Robinson, Y. Harold Department of MCA Sona College of Technology Salem India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India

Quality assessment is a key problem to be resolved in image processing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the process of image quality assessment was not sufficient. A novel method Morlet Wavelet Transformation Based Deep Similarity Structured Neural Learning (MWT-DSSNL) is proposed to enhance the performance of image quality assessment with minimal peak signal-to-noise ratio. The MWT-DSSNL Method is based on artificial neural networks with representation learning. The MWT-DSSNL Method initially gets the number of images from a given dataset as input. The MWT-DSSNL Method uses multiple layers to extract higher-level features from the input images. The MWT-DSSNL Method is a Feed-Forward network where each layer employs the output from the previous layer as input. The MWT-DSSNL Method is designed based on a biological neural network of the human brain. Contrary to the existing method, the MWT-DSSNL Method uses multiple hidden layers and Morlet wavelet transformation to deeply analyze input images and thereby extract features such as luminance, contrast, and structure with a minimal amount of time consumption. By considering the discovered features, finally MWT-DSSNL Method determines structural similarity and thereby exactly identifies the quality of input images with a lower time. From that, the MWT-DSSNL Method achieves enhanced image quality assessment performance when compared to existing works. The simulation of the MWT-DSSNL Method is conducted on factors such as PSNR, average processing time, quality detection accuracy, and false positive rate with different numbers of input images. The simulation result depicts that the MWT-DSSNL method increases the accuracy and also minimizes the time of image quality assessment when compared to conventional works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024

关键词： Network layers

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Home Automation-Based Health Assessment Along Gesture Recognition via Inertial Sensors

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computers, Materials & Continua 2023年第4期75卷 2331-2346页

作者： Hammad Rustam Muhammad Muneeb Suliman A.Alsuhibany Yazeed Yasin Ghadi Tamara Al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports *** have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to *** have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.

关键词： Genetic algorithm human locomotion activity recognition human–computer interaction human gestures recognition principal hand gestures recognition inertial sensors principal component analysis linear discriminant analysis stochastic neighbor embedding

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Optimised brain tumour detection with the Pteropus Unicinctus optimisation reliant deep model

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International Journal of Intelligent Information and Database Systems 2025年第2期17卷 186-216页

作者： Chhabra, Sumit Bansal, Khushboo Desh Bhagat University Punjab Mandi Gobindgarh India Department of Computer Science Khalsa College for Women Amritsar India Department of Computer Science and Engineering Desh Bhagat University Punjab Mandi Gobindgarh India

This research Pteropus Unicinctus optimisation (PUO) is proposed which is mathematically derived from the echolocation character of Pteropusand and the eye power searching ability of Unicinctus. The integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) in the PUO-reliant deep model presents an optimal solution to enhance the precision and dependability of brain tumour detection. This strategy leverages the unique capabilities of both architectures and fine-tunes their performance through hyperparameter optimisation, which leads to better diagnostic results and improved quality of patient care in brain tumour detection. The proposed approach attained the accuracy of 99.51%, 99.58%, and 99.50%, and considering sensitivity 99.48%, 99.60%, 99.37% attained, and the model attained 99.48%, 99.58%, and 99.26% of specificity for BraTS 2019 dataset, SimBRATS 2019 dataset, and brain tumour dataset respectively. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Detection of Quality Deep Fake Images and Videos Using Customised Convolutional Neural Networks 1

Detection of Quality Deep Fake Images and Videos Using Custo...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mahar, Alap Verma, Pushpneel Singh, Ajit Bhagwant University Department of Computer Science and Engineering Rajasthan Ajmer305004 India VMSB University Department of Computer Science and Engineering India

ISBN: (纸本)9798331518578

Due to advancements in artificial intelligence there are numerous deep fake images and video collections are available on the Internet and social media. The primary aim of the study is to analyse the deep fake images and videos since the quality is constantly improving a novel method was developed for accurate detection of quality deep fakes. The suggested approach uses a customised convolutional neural network (CNN) technique that uses facial landmark detection to extract structured data from photos and video frames before feeding it into the methods of CNN. The customised CNN model is augment-based CNN for generation of fake images and fake data. Involves about 260 films from the data set in which 202 images are made up while others were real images. About 300 videos are used in which 250 videos are fake and 50 were real the proposed model achieved the accuracy of 95.58% and 0.97 AUC score that outperforms the existing models like MLP-CNN and CNN. Additionally, the method succeeds with greater accuracy then the conventional models like DST -Net, VGG 16 Efficient Net. This research study's primary goal is to create a new CNN learning method for identifying high-quality deep fake photos and videos. © 2025 IEEE.

关键词： Video analysis

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On Neutrosophy logic for Multiple Correlation Analysis in Fuzzy Uncertain Database

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Neutrosophic Sets and Systems 2025年 76卷 1-17页

作者： Wazir, Samar Beg, M.M. Sufyan Ahmad, Tanvir Sohail, Shahab Saquib Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Computer Engineering Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India

The primary goal of this paper is to introduce a novel method for mining frequent and interesting items by incorporating correlation analysis between two items in an uncertain transactional database using the OWA operator. The work is further expanded by proposing two additional methods for mining frequent and interesting items, utilizing fuzzy means and the OWA operator along with multiple correlation analysis for more than two items in uncertain transactional databases. The effectiveness of the proposed methods is evaluated by running the algorithms on both standard and example datasets, with results compared to the traditional probabilistic approach for identifying frequent and interesting items through multiple correlation. While multiple correlation analysis highlights the relationships of interestingness and uninterestingness between items, the OWA operator enhances the results when combined with fuzzy means and probabilistic methods. Additionally, the paper suggests a future research direction using Neutrosophy logic, which is anticipated to open new avenues for further exploration in this field. © 2025, University of New Mexico. All rights reserved.

关键词： Database systems

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Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments

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China Communications 2024年第7期21卷 307-324页

作者： K Ramya Senthilselvi Ayothi Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiTamil Nadu 600089India

The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data *** utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization *** this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud *** capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource *** is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into *** addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS *** further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM *** results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for *** statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.

关键词： Beluga Whale Optimization Algorithm(BWOA) cloud computing Improved Hopcroft-Karp algorithm Infrastructure as a Service(IaaS) Prairie Dog Optimization Algorithm(PDOA) Virtual Machine(VM)

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Cyberbullying detection based on the fusion of DistilBERT and SIMHASH Technique 2

Cyberbullying detection based on the fusion of DistilBERT an...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Kumar, Chintoo Arun Kumar, K. Gupta, Suneet Sardar, Tanvir Habib GITAM Department of Computer Science and Engineering Bengaluru India ACED Alliance Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350349221

The advent of social network sites increases the bullying content in textual and visual formats. Bullying content disheartened a user or community to a great extent. Also detection of cyberbullying content is a challenging task, especially in online social networks where individuals can be harassed or harmed..Recently, using pre-trained language models has played a pivotal role in solving this problem reasonably. In this paper, we employed a fusion of a transformer-based model and the SIMHASH technique to solve this classification problem. We consider a readily available cyberbullying dataset. We conducted a comparative study among state-of-the-art models to check the proposed model's efficacy. The proposed model provides a promising result and outperforms. © 2024 IEEE.

关键词： Social networking (online)

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