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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Wilfred Blessing, N.R. Rohith Bhat, C. Sudha, I. Michael, G. Manikandan, T. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Oman Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9781032669663

The COVID-19 outbreak has significantly disrupted the agricultural system in India. In spite of this, the most recent quarterly GDP forecasts post-COVID scenario demonstrate the tenacity and durability of the Indian agricultural sector, which is anticipated to be the only sector to record a positive growth rate of 4% for fiscal years (FY) 2020 and 2021. Similarly, growth for the most recent quarter is projected to have been 5.89%, representing a reduction of 2.4% point. In this context, our goal is to compile a summary of the early evidence on the impact of COVID-19 on the agricultural sector in India, including production, marketing, and consumption, and then to design a set of pragmatic strategies to recover and thrive after the pandemic. According to the survey’s findings, the pandemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity prices, altering consumption patterns. The pandemic caused great emotional, social, economic, and bodily harm to everyone involved in India’s agriculture industry. The state has responded to the crisis by seizing the opportunity it presents and launching a variety of programmes and necessary changes. We suggest a ten-point plan that includes social safety nets, family farming, the trading of buffer stock, staggered procurement, and secondary agriculture in order to revitalise and prosper after the epidemic. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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A Novel Approach to Identify Pituitary Tumor Using an Optimal CNN Model with Deep Learning

A Novel Approach to Identify Pituitary Tumor Using an Optima...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Ebin, P.M. Thomas, Roja Ismail, Safad Kavitha Nair, R. Alliance School of Advance Computing Alliance University Banglore India Mangalam College of Engineering Department of Computer Science and Engineering Ettumanoor India College of Engineering Chengannur Department of Computer Engineering Chengannur India Acharya Institute of Technology Department of Ai &ml Banglore India

ISBN: (纸本)9798350313796

The technical paper introduces a novel approach to identifying pituitary tumors using an Optimized Convolutional Neural Network (OCNN) model with deep learning. Pituitary tumors rank among the most prevalent variety of brain tumors, and accurate identification is critical for proper diagnosis and treatment. The suggested method makes use of a deep learning framework that combines an ideal feature extraction method with a pre-trained CNN model. The pulled out features are then used to train the CNN model to classify pituitary tumors accurately. The effectiveness of the suggested method is assessed using a publicly accessible dataset, revealing that it surpasses the performance of current leading techniques. The suggested method possesses the capacity to act as a valuable asset for making clinical decisions in the field of neuroimaging, particularly for the accurate identification of pituitary tumors. The results of the experiment indicate that our suggested model successfully detects and classifies tumors with high accuracy, sensitivity, and specificity. Our model specifically achieves a 99.55% overall accuracy, 99% sensitivity, 99% specificity and 99% F1 score. These outcomes show that our suggested CNN design for brain tumor identification and classification performs better than several state-of-the-art techniques. © 2023 IEEE.

关键词： Tumors

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Enhancing Customer Retention through Feature Selection and XGBoost Classification for Churn Prediction

Enhancing Customer Retention through Feature Selection and X...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Raye Haarika Ayain John Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

Customer churn remains a significant and pressing challenge for both large enterprises and businesses across various industries. Particularly within the banking sector, where revenue is directly influenced by customer retention, there is a concerted effort to enhance predictive methods for identifying potential churn. This paper explores different machine learning (ml) algorithms employed in constructing a churn model to assist banking operators in predicting customers at risk of churning using minimum attributes and aiming to achieve good accuracy. The research delves into experimental results to compare the efficacy of various techniques, identifying the model that works best for predicting customer attrition in the banking sector in the end. This study uses feature selection and the XGBoost algorithm to select attributes based on their ranks.

关键词： Training Industries Accuracy Costs Computational modeling Machine learning Banking

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A Comparative Study of Machine Learning Models for Early Stage Identification of Powdery Mildew on Cherry Leaf

A Comparative Study of Machine Learning Models for Early Sta...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Supriya Shukla, Ashutosh Manchanda, Mahesh Coer University Department of Ai and Ml Roorkee India Upes University Electrical Cluster Dehradun India Graphic Era Hill University Department of Computer Science and Engg Dehradun India

ISBN: (纸本)9798350382723

Agriculture is the primary source of assistance for all the key sections of our country, INDIA. Population growth and Urbanization are the two major factors that create serious challenges for this field. According to the report of the Food and Agriculture Organization of the United Nations, by the next two decades, there will be a need for IT infrastructure to handle the rapidly expanding dietary needs of the two billion human population. Precision agriculture is the smart aid to resolving these issues. it allows for more efficient use of resources. It helps farmers to meet the growing demand for food sustainably by implementing smart IT techniques for several activities like Site-specific planting and fertilization, Crop monitoring, pest management, and variable rate irrigation. Autonomous vehicles, etc. keeping this in view a comparative study on advanced machine learning. Support vector machine, K-Nearest Neighbor, Artificial Neural Network, and Decision Tree algorithms have been implemented on MATLAB TOOL for early-stage identification of powdery mildew on cherry leaf. Hereafter considering various performance metrics like Confusion metrics parameters (accuracy, recall, precision), and ROC & AUC curve, it is found that ANN provides the best results of 91% on 1,200 images of the standard open source repository. © 2024 IEEE.

关键词： Decision trees

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SCORE REGULARIZED POLICY OPTIMIZATION THROUGH DIFFUSION BEHAVIOR 12

SCORE REGULARIZED POLICY OPTIMIZATION THROUGH DIFFUSION BEHA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Chen, Huayu Lu, Cheng Wang, Zhengyi Su, Hang Zhu, Jun Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University China Guangdong Guangzhou China

Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies. However, sampling from diffusion policies is considerably slow because it necessitates tens to hundreds of iterative inference steps for one action. To address this issue, we propose to extract an efficient deterministic inference policy from critic models and pretrained diffusion behavior models, leveraging the latter to directly regularize the policy gradient with the behavior distribution's score function during optimization. Our method enjoys powerful generative capabilities of diffusion modeling while completely circumventing the computationally intensive and time-consuming diffusion sampling scheme, both during training and evaluation. Extensive results on D4RL tasks show that our method boosts action sampling speed by more than 25 times compared with various leading diffusion-based methods in locomotion tasks, while still maintaining state-of-the-art performance. Code: https://***/thu-ml/SRPO. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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Classification of DDoS Attacks Using Ensemble Learning

Classification of DDoS Attacks Using Ensemble Learning

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Adepu Sai Aashrith Vishwaroju Sirichandana Barka Tarun Kumar M A Jabbar Y Sridevi Computer Science and Engineering (AI & ML) Vardhaman College of Engineering Department of English Vardhaman College of Engineering

In an era where the digital realm is indispensable to daily life, Constantly posing a threat, distributed denial of service (DDoS) attacks are capable of disrupting vital online services. Traditional methods of DDoS attack classification often fall short when facing the ever-evolving landscape of attack techniques. This paper unveils a journey through the complexities of enhancing DDoS attack classification with a focus on improving accuracy and adaptability. We embarked on this research by individually training a plethora of machine learning models. However, our preliminary findings highlighted the striking dis-parity in accuracy across different models, highlighting how complex and varied DDoS attacks are. Driven to overcome these constraints, we resorted to the sturdy framework of ensemble learning. Ensemble learning changed the game for accurate DDoS attack classification by effortlessly combining the best features of several base models. To be more precise, we blended two distinct baseline models: K-Nearest Neighbors (KNN) and Random Forest (RF). These models, each with distinct characteristics, combined to form a flexible and adaptive DDoS attack classification system. K-Nearest Neighbors (KNN) uses the proximity of data points to find trends and parallels. Resilience, high-dimensional data processing, and overfitting resistance are all favoured by the Random Forest (RF) ensemble of decision trees. Combining these several methods into a group greatly improved our categorization accuracy. Our research's ability to forecast accuracy using the ensembled model was one of its main results. This prediction accuracy indicates the flexibility and dependability of the classification model in addition to providing a crucial performance standard for assessing system effectiveness. Finally, our explo-ration of the DDoS assault classification demonstrated Ensemble Learning's superiority. The proposed strategy surpassed the existing methodologies, according to experimental

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Clustering based Ensemble Machine Learning for Integrated Prediction and Classification of Ecological Zones

Clustering based Ensemble Machine Learning for Integrated Pr...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Alabdeli, Haider Reddy, Francis Vijaykumar Anna Chanti, Yerrolla Aruna, T.M. Das, Abhijeet The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Malla Reddy Engineering College for Women Dept of Computer Science and Engineering Secunderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai&ml Bengaluru India Department of Civil Engineering Bhubaneswar India

ISBN: (纸本)9798350360660

Accurate classification of ecological zones helps identifying the location of specific ecological features, and analyzing changing conditions. However, existing methods had limitations such as forming different image dimensions and analysing the areas within the images due to high noisy data. To overcome this, the proposed research integrated Density Based Spatial Clustering of Applications with Noise (DBSCAN) with Ensemble Machine Learning (Eml) algorithms using Multilayer Perceptron (mlP), Naïve Bayes (NB), Classification and Regression Tree (CART), and Logistic Regression (LR). To train the model, the Landsat series data which is obtained from Geographical Information System (GIS) is used and then pre-processed using Normalized Difference Vegetation Index (NDVI) to characterize a variety of vegetation properties. The DBSCAN is then used for noisy data to distinguish the densities of data points and to segment data in different fields. The integration of DBSCAN with ml classifiers improved the classification accuracy. The overall experimental results show that the proposed DBSCAN-Eml achieved 98.43% of accuracy and 97.89% of precision when compared with the existing methods Eml based spatial prediction method and Artificial Neural Network Cellular Automata (ANN-CA). © 2024 IEEE.

关键词： Adversarial machine learning

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MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer 31

MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs th...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hong, Seongtae Lee, Seungyoon Moon, Hyeonseok Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of Human-inspired AI Research Korea Republic of

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have rapidly advanced, with domain-specific expert models emerging to handle specialized tasks across various fields. However, the predominant focus on English-centric models demands extensive data, making it challenging to develop comparable models for middle and low-resource languages. To address this limitation, we introduce MIGRATE, a novel method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddings to target languages. MIGRATE enables effective cross-lingual adaptation without requiring large-scale domain-specific corpora in the target language, promoting the accessibility of expert LLMs to a diverse range of linguistic communities. Our experimental results demonstrate that MIGRATE significantly enhances model performance in target languages, outperforming baseline and existing cross-lingual transfer methods. This approach provides a practical and efficient solution for extending the capabilities of domain-specific expert models. © 2025 Association for Computational Linguistics.

关键词： Embeddings

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Augmented and End-to-End Models for Defect Classification of Structures 18

Augmented and End-to-End Models for Defect Classification of...

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18th ACM International Conference on Distributed and Event-Based Systems, DEBS 2024

作者： Kim, Gyu-Il Chung, Kyungyong Department of Computer Science Kyonggi University Suwon Korea Republic of Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9798400704437

This paper proposes an automated defect classification model for the safety diagnosis and maintenance of concrete structures. Traditional manual classification methods are time-consuming and rely on subjective judgment, whereas the proposed model utilizes time-series data obtained from sensors to enable faster and more accurate defect detection through data augmentation and Transformer-based classification. Data augmentation compensates for the imbalance in defect data distribution, and the Transformer effectively interprets the complex temporal patterns of the data, capturing long-range dependencies. This end-to-end model performs an integrated process from data processing to defect classification. Future research will explore the model's generalizability and the potential for integration into real-time monitoring systems. This paper presents a new approach to improving the economic and efficient evaluation of the safety of concrete structures. © 2024 Owner/Author.

关键词： Defects

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