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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Creation and Classification of Kannada Meme dataset: Exploring Domain and Troll Categories 2nd

Creation and Classification of Kannada Meme Dataset: Explo...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Kundargi, Sahana Y. N, Lohith M, Anand Kumar Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9783031584947

In this pioneering research, the first-ever Kannada memes dataset is established, marking a groundbreaking contribution. This dataset encompasses 2002 memes, spanning various categories such as movies, politics, sports, trolls, and non-troll memes. The classification models have been meticulously fine-tuned for memes, incorporating image-based models using DenseNet169 and text-based models with BERT for text encoding. An innovative multimodal approach combines insights from images and text, acknowledging the comprehensive nature of meme content. Throughout the study, model strengths and weaknesses are assessed, emphasizing their reliance on cutting-edge technologies like Deep Learning and Natural Language Processing. Valuable improvements are recommended, such as the implementation of oversampling techniques and regular dataset updates to enhance relevance and accuracy. This work extends beyond immediate research, contributing to the development of adaptive meme classification systems, particularly for Kannada-speaking audiences within the evolving meme culture landscape. Notably, the results indicate that multimodal models achieved the best scores for domain classification, while image-based models excelled in troll meme classification, further highlighting the significance of this approach within the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification (of information)

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Challenges Associated with the Adoption of Artificial Intelligence in Medical Device Software 30th

Challenges Associated with the Adoption of Artificial Intell...

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30th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2022

作者： Cepeda Zapata, Karla Aniela Ward, Tomás Loughran, Róisín McCaffery, Fergal Dundalk Institute of Technology Dundalk Ireland Lero Limerick Ireland Insight SFI Research Centre for Data Analytics Dublin Ireland

ISBN: (纸本)9783031264375

The utilization of Artificial Intelligence (AI) has changed and enhanced several industries across the world, such as education, research, manufacturing and healthcare. The potential of AI to create new and enhanced applications that can benefit patients and physicians has created interest and enthusiasm, especially in a Medical Device Software (MDS) context. Although, the adoption of AI in MDS has also brought concerns for regulatory agencies and policymakers. The complexity of AI has challenged the standard requirements set by regulatory agencies, especially in the context of the differences between traditional MDS and AI. Additionally, the unique capacity of AI to continuous learning for optimal performance in real-world settings may also bring potential harm and risk to patients and physicians. The challenges discussed in this paper are in relation to: (1) Software Development Life Cycle (SDLC) frameworks;(2) learning processes and adaptability of AI algorithms;(3) explainability and traceability;and (4) conflictive terminology. At the end of this paper, conclusions and future work are presented to contribute to the safety and methodical implementation of AI in health care settings. © 2023, The Author(s).

关键词： Artificial intelligence

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Detecting Email Fraud Attacks Using Ensemble Machine Learning Method

Detecting Email Fraud Attacks Using Ensemble Machine Learnin...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Kalaiselvi, K. Mahalakshmi, S. Jayalakshmi, S. SIMATS Saveetha College of Liberal Arts and Sciences Department of Data Analytics India DRBCCC Hindu College Department of Computer Applications Chennai India MEASI Institute of Information Technology Royapettah Department of MCA Chennai India

ISBN: (纸本)9798350394412

An ensemble machine learning (ML) technique is used in this work to advance the precision of double-dealing email detection. Email fraud is still a main problem, often employing phishing and spoofing strategies that are very sophisticated and circumvent conventional security measures. This research intends to increase detection systems' presentation by applying the collective intelligence of numerous machine learning models inside an ensemble framework. These kinds of solutions are essential for protecting people and companies from monetary losses, data breaches, and harm to their reputations from unscrupulous email activity. This work developments the area of cybersecurity by providing a strong technique for detection and preventing email fraud attempts, strengthening overall digital resilience in today's linked world through severe testing and analysis. Email is a productive means of communication. It is one of the most widely utilized forms of communication and can be employed for both legal and illicit purposes. Numerous characteristics that may be useful in identifying email fraud attempts are currently being researched. MATLAB is used to assess the email fraud filtering performance, and ML classifiers are tested experimentally within the Ensemble classifier. To increase performance, this research suggests using an Ensemble machine learning (EML) classifier with feature extraction to improve the classification accuracy of fake emails. Though the scam has changed and will probably change again, its basic idea of promising something in exchange for nothing and preying on people's weaknesses has not changed. Fraud email detection's precision and adaptability were assessed utilizing characteristics, such as recently suggested features. where flowing large data can be considered. To automate the many processes of KD, we can explore implementing an automated fraud detection system in addition to including attachment functionalities in our future work. © 2024 IEEE.

关键词： Adversarial machine learning

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Distributed Bilevel Optimization with Communication Compression 41

Distributed Bilevel Optimization with Communication Compress...

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41st International Conference on Machine Learning, ICML 2024

作者： He, Yutong Hu, Jie Huang, Xinmeng Lu, Songtao Wang, Bin Yuan, Kun Peking University China University of Pennsylvania United States IBM Research United States Zhejiang University China National Engineering Labratory for Big Data Analytics and Applications China AI for Science Institute Beijing China

Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead and thus hindering efficiency and scalability. To resolve this issue, we introduce the first family of distributed bilevel algorithms with communication compression. The primary challenge in algorithmic development is mitigating bias in hypergradient estimation caused by the nested structure. We first propose C-SOBA, a simple yet effective approach with unbiased compression and provable linear speedup convergence. However, it relies on strong assumptions on bounded gradients. To address this limitation, we explore the use of moving average, error feedback, and multi-step compression in bilevel optimization, resulting in a series of advanced algorithms with relaxed assumptions and improved convergence properties. Numerical experiments show that our compressed bilevel algorithms can achieve 10× reduction in communication overhead without severe performance degradation. Copyright 2024 by the author(s)

关键词： Stochastic systems

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QUALYST: data Quality Assessment System for Thailand Open Government data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on Computer Science and Software Engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Using Blue Whale technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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KaustubhSharedTask@LT-EDI 2023: Homophobia-Transphobia Detection in Social Media Comments with NLPAUG-driven data Augmentation 3

KaustubhSharedTask@LT-EDI 2023: Homophobia-Transphobia Detec...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Lande, Kaustubh Ponnusamy, Rahul Kumaresan, Prasanna Kumar Chakravarthi, Bharathi Raja Indian Institute of Technology Kharagpur India Insight SFI Research Centre for Data Analytics University of Galway Ireland School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Our research in Natural Language Processing (NLP) aims to detect hate speech comments specifically targeted at the LGBTQ+ community within the YouTube platform shared task conducted by the LT-EDI workshop1. The dataset provided by the organizers exhibited a high degree of class imbalance, and to mitigate this, we employed NLPAUG, a data augmentation library. We employed several classification methods and reported the results using recall, precision, and F1-score metrics. The classification models discussed in this paper include a Bidirectional Long Short-Term Memory (BiLSTM) model trained with Word2Vec embeddings, a BiLSTM model trained with Twitter GloVe embeddings, transformer models such as BERT, DistilBERT, RoBERTa, and XLM-RoBERTa, all of which were trained and fine-tuned. We achieved a weighted F1-score of 0.699 on the test data and secured fifth place in task B with 7 classes for the English language. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Embeddings

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Noise filtering approach to improve handwritten digit recognition using customized CNN for Cerebral Palsy individuals

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The European Physical Journal Special Topics 2025年 1-19页

作者： Muthureka, K. Srinivasulu Reddy, U. Janet, B. Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology-Tiruchirappalli Tiruchirappalli India Centre of Excellence (CoE) in Artificial Intelligence National Institute of Technology-Tiruchirappalli Tiruchirappalli India Information Processing Lab Department of Computer Applications Tiruchirappalli India

Automatic recognition of handwritten digits in people with Cerebral Palsy (CP) is a serious challenge that requires advancements in data preparation for improved predictive accuracy. The disorganized digit pixel patterns caused by CP people’ innate motor coordination problems result in the classification errors due to the noise and outlier. Despite the fact that many handwritten digit databases for normal people are available, none expressly cater to those with shaky hands/CP dataset. This highlights the importance of an efficient pre-processing method for mitigating variances in CP individuals’ digit pixel patterns. A total of 43,000 images from 43 CP people were collected and organized into 10 different folders for data labeling. The authors used the K-Means Based Noise Removal (KMNR) approach to remove noise and outliers from a CP and tested it on a customized CNN. The KMNR model achieved an accuracy of 98.86%, compared to 93.54% without it. The F1-score values for state-of-the-art models without KMNR ranged from 85.00% to 97.00%, but after integrating KMNR, they increased to 92.00% to 98.00%. The precision values ranged from 85.00% to 97.00% in the absence of KMNR, but increased to 91.00% to 98.00% in the presence of KMNR. The recall values ranged from 85.00% to 97.00% without KMNR, but increased to 87.00% to 98.00% with KMNR.

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Interdependencies and Cascading Effects of Disasters on Critical Infrastructures: An Analysis of Base Station Communication Networks 15

Interdependencies and Cascading Effects of Disasters on Crit...

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15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Lee, Eva K. Wang, William Zixing Center for Operations Research in Medicine and Healthcare The Data and Analytics Innovation Institute Atlanta United States Georgia Institute of Technology Atlanta United States Accuhealth Technologies Atlanta United States

ISBN: (纸本)9789897586712

There are sixteen critical infrastructure (CI) sectors whose assets, systems, and networks, whether physical or virtual, are considered so vital to the United States that their incapacitation or destruction would have a debilitating effect on military readiness, economic security, public health, or safety. The communications sector is unique as a critical infrastructure sector due to its central role in facilitating the flow of information, enabling communication, and supporting all other CIs as well as other components of the economy and society. Within the communications sector, the cellular base station (cell tower) network serves as its foundational backbone. During a crisis, if towers in the network stop functioning or are damaged, the service load of associated users/businesses will have to be transferred to other towers, potentially causing congestion and cascading effects of overload service outages and vulnerabilities. In this paper, we investigate cellular base station network vulnerability by uncovering the most critical nodes in the network whose collapse would trigger extreme cascading effects. We model the cellular base station network via a linear-threshold influence network, with the objective of maximizing the spread of influence. A two-stage approach is proposed to determine the set of critical nodes. The first stage clusters the nodes geographically to form a set of sub-networks. The second stage simulates congestion propagation by solving an influence maximization problem on each sub-network via a greedy Monte Carlo simulation and a heuristic Simpath algorithm. We also identify the cascading nodes that could run into failure if critical nodes fail. The results offer policymakers insight into allocating resources for maximum protection and resiliency against natural disasters or attacks by terrorists or foreign adversaries. We extend the model to the weighted LT influence network (WLT-IN) and prove that the associated influence function is monoton

关键词： COVID-19

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