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Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： M. Ramla R. Anita Jasmine M. Ramesh Femilda Josephin Joseph Shobana Bai Department of Computer Applications SRM Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Computer Science SRM Institute of Science and Technology Chennai India Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%.

关键词： COVID-19 Image segmentation Technological innovation Accuracy Convolution Lungs Computed tomography Transfer learning computer architecture Predictive models

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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Performance Evaluation of Three Intelligent Optimization Algorithms for Obstacle Avoidance Path Planning 14th

Performance Evaluation of Three Intelligent Optimization Al...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Chen, Shumeng Wang, Chia-Hung Dong, Zhenyu Zhao, Qigen Yang, Qipeng Wei, Yi Huang, Guoshu College of Computer Science and Mathematics Fujian University of Technology Fuzhou City China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou City China

ISBN: (纸本)9789811684296

In this paper, we study an intelligent path planning problem on avoiding traffic obstacles in urban transportation networks. We apply three heuristic methods to solve the optimization model, including genetic algorithm, wolf pack algorithm, and a mixture method of genetic algorithm and wolf pack algorithm. In the numerical results, we compare the solutions and performance of these three intelligent algorithms. It shows that the optimal path lengths obtained by these three algorithms are very closed, whereas the convergence time of the genetic algorithm is the shortest among them. We also find that the mixture method is more efficient in updating the population, and its convergence speed is the fastest. Besides, it is found that the convergence time of the wolf pack algorithm is smaller than the others. By means of the analysis in our experiments, we evaluate the pros and cons of these algorithms when solving the studied optimization problem, which would be of great significance for the intelligent optimization of obstacle path planning in smart cities. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Efficient Scanning Activity Detection in IoT Networks Using Ensemble Learning 23

Efficient Scanning Activity Detection in IoT Networks Using ...

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2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023

作者： Igried, Bashar Alauthman, Mohammad Alkhaldy, Mohammad Alsarhan, Ayoub Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology Department of Computer Science and Applications The Hashemite University Zarqa Jordan Department of Information Security University of Petra Amman Jordan Department of Business Intelligence and Data Analytics University of Petra Amman Jordan

ISBN: (纸本)9798400708312

The rapidly expanding universe of Internet of Things (IoT) networks has led to greater security risks due to the proliferation of sophisticated threats such as scanning activities, where adversaries use scanning to identify and exploit vulnerabilities in target IoT devices, which often have limited computing power and storage, making them attractive targets;to address this escalating challenge, this research proposes a comprehensive ensemble learning methodology specifically tailored for robust and timely detection of scanning activities in IoT networks, integrating a diverse set of base classifiers including Support Vector Machine (SVM), Neural Network (NN), Decision Trees (DT), and Linear Regression (LR), with predictions from the base models combined using a Linear Regression meta-classifier into a stacked ensemble model;extensive comparative evaluation on an IoT network dataset demonstrates the proposed ensemble technique achieves significantly higher accuracy, detection rate, F-measure, precision, and Area Under ROC Curve (AUC) versus individual traditional machine learning models, and remarkably, exhibits consistent performance across different cross-validation folds, highlighting its inherent stability and validity, suggesting it generalizes well without overfitting;in summary, this research puts forward an efficient and highly effective ensemble learning solution for the timely and precise detection of scanning attacks against IoT networks, serving as a vital step towards strengthening security against escalating threats. © 2023 ACM.

关键词： Network security

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An anonymous authentication and key exchange protocol in smart grid

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 206-215页

作者： Luo, Yi Zheng, Wei-Min Chen, Yeh-Cheng Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350100 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science University of California DavisCA95616 United States

With the use of a large number of digital devices in smart grid, how to protect the security of communication in Advanced Metering Infrastructure (AMI) has become a difficult problem. AMI plays an important role in smart grid, which is used two-way communication between users and electricity companies. AMI includes smart meters(SM), data collectors, AMI Head-End(AHE), etc. In this paper, we present an authentication and key exchange protocol between the smart meter and the AMI Head-End in smart grid. The security of proposed scheme is proved by random oracle model and BAN logic. At the end of the paper, the performance analysis shows that the scheme is efficient and suitable for AMI. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Authentication

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Comparative Analysis of RAG, Fine-Tuning, and Prompt Engineering in Chatbot Development

Comparative Analysis of RAG, Fine-Tuning, and Prompt Enginee...

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Future Technologies for Smart Society (ICFTSS), International Conference on

作者： Harshit Kumar Chaubey Gaurav Tripathi Rajnish Ranjan Srinivasa k. Gopalaiyengar Computer Science and Engineering IIIT Naya Raipur Raipur India Electronics and Communication Engineering IIIT Naya Raipur Raipur India Data Science and Applications IIT Madras Chennai India Data Science and Artificial Intelligence IIIT Naya Raipur Raipur India

ISBN: (数字)9798350373844

ISBN: (纸本)9798350373851

This paper examines the integration and comparative effectiveness of Retriever-Augmented Generation (RAG), fine-tuning, and prompt engineering in the development of advanced chatbots. By employing domain-specific fine-tuning, the study addresses contextual misunderstandings and inaccuracies prevalent in base Large Language Models (LLMs). RAG enhances chatbot functionality by incorporating real-time data retrieval, ensuring relevance in dynamically changing environments. Prompt engineering is utilized to refine input prompts, thereby optimizing the accuracy of responses. Employing the “openassistant-guanaco” dataset from Hugging Face, this research assesses the performance improvements offered by these methodologies, both quantitatively and qualitatively. The fine-tuned model outperforms other methods with an accuracy of $87.8 \backslash \%$ and a BLEU score of 0.81, proving its effectiveness in generating the most relevant responses. In contrast, while the RAG with LLM approach shows promising results with a reasonable accuracy of $84.5 \backslash \%$, the Prompt Engineering method, though slightly less effective with an accuracy of $83.2 \backslash \%$, still maintains competitive performance. This study highlights the unique and combined strengths of these technologies, contributing valuable insights into their synergistic potential for enhancing chatbot interactions

关键词： Analytical models Accuracy Large language models Chatbots Real-time systems Prompt engineering Faces

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Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

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Exploring Design Optimisation Techniques of a Radio Telescope Using Fixed Costing Constraints 2

Exploring Design Optimisation Techniques of a Radio Telescop...

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2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Iyer, Aditya Arun Prabhu, Gautham Manuru Gupta, Tanay Deshmukh, Shrey Rivankar, Rushit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Cost optimization is a common problem encountered in the design of telescopes. This paper comprehensively discusses various radio telescope designs worldwide, focusing on their design and utilities. It contextualizes the Pulsar data challenge and subsequently discusses and develops a mathematical model for designing radio telescopes. The model assumes a fixed-cost budget. This paper expands current ideas of modeling the system using figure-of-merit equations and optimizing them based on a fixed budget to obtain optimal and affordable radio telescope designs. © Published under licence by IOP Publishing Ltd.

关键词： Radio telescopes

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Etiseg-Net: Edge-Aware Self Attention to Enhance Tissue Segmentation in Histopathological Images

SSRN

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SSRN 2023年

作者： Rashmi, R. Girisha, S. Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this *** study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Channel-wise attention based binarization for CNN Architectures 5

Channel-wise attention based binarization for CNN Architectu...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Paul, Ipsita Bandyopadhyay, Mainak Dash, Satya Ranjan Aurpa, Tanjim Taharat Kiit D.U. School of Computer Engineering Bhubaneswar India Kiit D.U. School of Computer Applications Bhubaneswar India Bsmr Digital University Department of Data Science Engineering Bangladesh

ISBN: (纸本)9798331522100

Attention mechanisms are known to effectively increase the performance of a model by focusing on specific sections of the input while executing a task. Squeeze and Excitation module (SE-Net) and Efficient channel attention module (ECA-Net) are well-known Channel Attention networks that only involve a few parameters to achieve a clear performance gain. In order to further improve the model's accuracy, Binarized Neural Networks (BNNs) can be introduced in the same. BNNs are deep learning models that use binarized values for activation functions and training weights, instead of fully precised values that leads to much faster computation, occupying less memory and power. Motivated by this, a method of binarization of the convolutional network is proposed in this paper that can enhance the overall model accuracy. In our work, we focused on evaluating the performance of our binarized attention module over the pre-existing ones. Thus, the objective of this work is to design, implement, and evaluate efficient neural network models that balance accuracy and computational efficiency for image classification tasks on datasets with differing complexity levels, such as CIFAR-10 and CIFAR-100. By exploring various backbone architectures (e.g., MobileNetV2 and ResNet50), attention mechanisms (e.g., ECANet and SENet), and binarization techniques, this study aims to understand the impact of these components on model performance and resource requirements. Ultimately, the goal is to develop optimized models that achieve high accuracy with minimal computational overhead, making them suitable for deployment in resource-constrained environments. © 2025 IEEE.

关键词： Convolutional neural networks

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