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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Gull, Seema C. NaveenKumar, C.G. Gull, Karuna C. Sanskruti, V.B. Laddi, Mahantesh Karnataka State Open University Department of Studies in Computer Science Mysuru India S.G.Balekundri Institute of Technology Department of Computer Science & Business Systems Belagavi India M.S. Sheshgiri College Engineering and Technology Department of Biomedical Engineering Belagavi India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350376685

This project seeks to employ more sophisticated approaches to machine learning to determine asthma occurrences after vaccine administration. As vaccine distribution becomes more widespread around the world, it is important to monitor and prevent issues such as asthma. The goals for future research include the creation of risk-adjusted models that may predict the risk of asthma following vaccination, depending on patients' demographic information and the characteristics of the vaccines. The research uses several sophisticated techniques such as Artificial-Neural-Networks (ANN), Random-Forests (RF), Naïve-Bayes (NB), and logistic regression (LR) to improve the ability to predict and identify prevention measures and healthcare management. Thus, a significant innovation of this work is the comprehensive assessment of these algorithms when exposed to noise, which is a common issue in many medical data sets. The study also showed that Random-Forest and ANN models were significantly more accurate and yielded larger ROC AUC scores than other models;moreover, they were much less sensitive to noise with noise values ranging from 0 to 0.5. This places RF and ANN on a better pedestal to offer a better prediction of post-vaccination asthma than the other models, especially in a scenario where there is the possibility of data contamination. The present findings can thus help create better and more resistant models of prediction that are useful in better health management and treatment of asthma over and after vaccination. © 2024 IEEE.

关键词： Logistic regression

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Exploring Research Opportunities to Apply Data Mining Techniques in Software Engineering Lifecycle 1

Exploring Research Opportunities to Apply Data Mining Techni...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Anitha Gracy, J. Parthasarathy, S. Bagavathilaksmi, R. Thiagarajar College of Engineering Applied Mathametics and Computational Science Madurai India VELS Institute of Science Technology and Advanced Studies Department of Computer Application/Information Technology Chennai India

ISBN: (纸本)9798350342796

This Software engineering (SE) is the core research area of the software industry, and numerous algorithms and frameworks are proposed every day. Software Engineering is the discipline that keeps up with recent developments in fields and techniques such as data mining, machine learning, natural language processing, and artificial intelligence. It is difficult to manage such a massive amount of data in a software engineering repository if data processing and big data analytics are not available. This paper provides an in-depth study of data mining techniques that to improve the efficiency of the software development lifecycle. The study emphasizes the practical applications and discoveries made by text mining, clustering, classification, and other methods. © 2023 IEEE.

关键词： Classification Clustering Data Mining SDLC Software Engineering Text Mining

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

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computer Modeling in Engineering & sciences 2023年第11期137卷 2025-2047页

作者： Huabin Wang Ruichao Mo Yuping Chen Weiwei Lin Minxian Xu Bo Liu School of Computer Science and Engineering Huizhou UniversityHuizhou516007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science and Technology South China Normal UniversityGuangzhou510631China Department of New Network Technologies Peng Cheng LaboratoryShenzhen518066China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile ***,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy *** addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge ***,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model *** results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher ***,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

关键词： Pedestrian and vehicle detection YOLOv4 channel pruning cloud-edge collaboration

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Conformity, Confabulation, and Impersonation: Persona Inconstancy in Multi-Agent LLM Collaboration 2

Conformity, Confabulation, and Impersonation: Persona Incons...

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2nd Workshop on Cross-Cultural Considerations in NLP, C3NLP 2024

作者： Baltaji, Razan Hemmatian, Babak Varshney, Lav R. Department of Electrical and Computer Engineering United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798891761469

This study explores the sources of instability in maintaining cultural personas and opinions within multi-agent LLM systems. Drawing on simulations of inter-cultural collaboration and debate, we analyze agents’ pre- and post-discussion private responses alongside chat transcripts to assess the stability of cultural personas and the impact of opinion diversity on group outcomes. Our findings suggest that multi-agent discussions can encourage collective decisions that reflect diverse perspectives, yet this benefit is tempered by the agents’ susceptibility to conformity due to perceived peer pressure and challenges in maintaining consistent personas and opinions. Counterintuitively, instructions that encourage debate in support of one’s opinions increase the rate of inconstancy. Without addressing the factors we identify, the full potential of multi-agent frameworks for producing more culturally diverse AI outputs will remain untapped. © 2024 Association for Computational Linguistics.

关键词： Chatbots

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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

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Intelligent Automation & Soft Computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others.

关键词： Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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Systematic Review of GAN for Enhancing Efficiency in AI in Gaming

Systematic Review of GAN for Enhancing Efficiency in AI in G...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Singh, Jasdeep Islam, Sardar M. N. Tewatia, Manav Garg, Deepanshu Fatima, Neda School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Victoria University Department of Computer Science and Engineering Melbourne Australia School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798350388800

A thorough analysis of Generative Adversarial Networks (GANs) and how they might be used to improve AI gaming efficiency is presented in this research. Game developers now have never-before-seen possibilities for innovation, immersion, and personalization thanks to GANs, which have become a potent tool for producing realistic data samples across a variety of fields. This review investigates the architecture, training methods, applications, difficulties, and potential future directions of GANs in AI gaming through a thorough analysis of the body of research and publications. The review demonstrates how GANs can transform the game industry by highlighting their many uses in procedural content generation, texture synthesis, character animation, environment design, and content customization. Nevertheless, there are certain difficulties in integrating GANs into AI games, such as computational resources, mode collapse, training instability, and assessment *** well as legal matters. To tackle these obstacles, multidisciplinary research endeavors and cooperation across academic institutions, corporate entities, and regulatory agencies are necessary. Going forward, new avenues for study and developments in GAN technology hold great potential for getting over these roadblocks and opening up fresh possibilities in AI gaming. We can create a future where AI powered gaming experiences change the parameters of interactive entertainment and provide players globally with more engaging, varied, and personalized gaming experiences by utilizing the possibilities of GANs and addressing the related obstacles. © 2024 IEEE.

关键词： Generative adversarial networks

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep Learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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Integrating LLM with CNN-RNN for Optimizing Crop Production

Integrating LLM with CNN-RNN for Optimizing Crop Production

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Gupta, Kartavya Rani, Khushi Ajmani, Prerna Sharma, Vandana Alkhayyat, Ahmed Kalinga Institute of Industrial Technology Computer Science and Engineering Bhubaneswar India Vivekananda Institute of Professional Studies-TC India CHRIST University Computer Science Department Bengaluru India The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350390049

This study establishes a hybrid methodology combining LLM, CNN, and RNN for predicting crop yield, and detailed coverage of each area of the hybrid is set as follows-. In general, traditional methods have great difficulties with analyzing agricultural data presented as complex non-linear data. Our suggested approach utilizes the extraction capabilities of CNN, the temporal sequence modeling of RNN, and the sophisticated data processing of LLM, and this hybrid approach produced high accuracy levels for tested crops including wheat, maize, millet, rice, and barley, as well as significantly better precision, recall and f-score cutoff levels than traditional methods. This research contributes to ongoing developments toward improved food security and agriculture output through the implementation of sophisticated machine learning models. This paper presents a novel hybrid model for predicting crops yields by combining Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) with Large Language Models (LLMs). Because agricultural data is challenging to model as traditional algorithms typically struggle with complex, inherently non-linear data, our modeling approach leverages the capabilities of sophisticated LLMs for processing data, temporal sequence modeling capabilities of RNNs, and data extraction capabilities of CNNs. Our hybrid model can produce high accuracy outcomes from the model experimentation using standard measures of output accuracy from a number of crop species, including barley, rice, millet, maize and wheat, and the model also produced significant and meaningful improvements over previous studies with traditional methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Promoting rigor in blockchain energy and environmental footprint research:A systematic literature review

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Blockchain(Research and Applications) 2024年第1期5卷 133-151页

作者： Ashish Rajendra Sai Harald Vranken Department of Computer Science Open UniversiteitHeerlen6419 ATthe Netherlands Department of Advanced Computing Sciences Maastricht UniversityMaastricht6211 LKthe Netherlands Institute for Computing&Information Sciences Radboud UniversityNijmegen6525 XZthe Netherlands

There is a growing interest in understanding the energy and environmental footprint of digital currencies,specifically in cryptocurrencies such as Bitcoin and *** cryptocurrencies are operated by a geographically distributed network of computing nodes,making it hard to estimate their energy consumption *** studies,both in academia and industry,attempt to model cryptocurrency energy consumption often based on a number of assumptions,for instance,about the hardware in use or the geographic distribution of the computing nodes.A number of these studies have already been widely criticized for their design choices and subsequent over-or under-estimation of energy *** this study,we evaluate the reliability of prior models and estimates by leveraging existing scientific literature from fields cognizant of blockchain,such as social energy sciences and information *** first design a quality assessment framework based on existing research,and we then conduct a systematic literature review examining scientific and non-academic literature demonstrating common issues and potential avenues of addressing these *** goal with this article is to advance the field by promoting scientific rigor in studies focusing on blockchain energy *** that end,we provide a novel set of codes of conduct for the five most widely used research methodologies:Quantitative energy modeling,literature reviews,data analysis and statistics,case studies,and *** envision that this code of conduct would assist in standardizing the design and assessment of studies focusing on blockchain-based systems’energy and environmental footprint.

关键词： Blockchain Energy consumption Environmental footprint Scientific rigor Reliability

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