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An Exploratory Analysis of Deep Learning Models for Detection of Lung Cancer in Medical Images 8th

An Exploratory Analysis of Deep Learning Models for Detectio...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Giraddi, Shantala Kuraning, Vidya Baligar, Vishwanth P. School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India

ISBN: (纸本)9789819766802

Lung cancer remains a significant global health concern, and advancements in early detection methodologies are required. This paper conducts a detailed investigation and comparative analysis of four models in deep learning (Extreme Learning Machine (ELM), VGG16, ResNet50, and VGG19) applied to the important task of detecting lung cancer from medical images. The goal of the project is to gain nuanced insights into the trade-offs between model complexity, computing resources, and performance of these models in detection of lung cancer. These findings are intended to guide clinicians and medical researchers in choosing the top deep learning model for a specific clinical application. Our study focuses on key performance metrics such as training and inference time, number of parameters, and model accuracy and uses a diverse dataset of medical images, including lung scans. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lung cancer

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Optimized BERT Model for Question Answering System on Mobile Platform 2nd

Optimized BERT Model for Question Answering System on Mobile...

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

作者： Patil, Priyadarshini Rao, Chandan Meena, S.M. School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India

ISBN: (纸本)9783031584947

In Natural Language Processing (NLP), question-answering systems are a classic problem, yet they pose several open challenges. After the pandemic situation of COVID-19, online learning has become crucial, in which question-answering systems are also beneficial to students in searching for answers to subject-related questions. The paper aims to address the challenge of providing a lightweight and efficient question-answering system, especially for students with limited resources, particularly those using low-cost mobile devices. The strategy involves post-training quantization to reduce the size of the BERT model while maintaining high accuracy. Quantization is a technique that reduces the memory footprint of deep learning models, making them more suitable for deployment on mobile devices and low-cost hardware. The reduction in the BERT model size from 438 MB to 181 MB with negligible accuracy degradation is a significant achievement. The resulting mobile application works offline, making educational content accessible in areas with limited internet connectivity. The system achieved an F1 score of 0.87 with negligible degradation compared to the accuracy of the BERT model F1 score: 0.90. Key considerations include user experience, maintenance and memory optimization. This paper offers a practical and cost-effective solution to enhance educational access on mobiles while addressing resource constraints. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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Horizontal Pod Autoscaling in Kubernetes Cluster Using Long Short-Term Memory 4th

Horizontal Pod Autoscaling in Kubernetes Cluster Using Long ...

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4th International Conference on Data, engineering, and Applications, IDEA 2022

作者： Patil, Somshekar Narayan, D.G. Bhat, Ajeya Hungund, Aishwarya Patil, Disha M. School of Computer Science and Engineering KLE Technological University Karnataka Hubballi580031 India

ISBN: (纸本)9789819724505

Containerization is a major trend in software development as an alternative to virtualization. It involves encapsulation of software code, libraries, packages, and dependencies, so that it can run uniformly and consistently on any infrastructure. On a single server or virtual machine, by using the same operating system, we can deploy multiple application with the help of containerization. One such open-source container orchestration technology that supports declarative setup and automation for managing containerized workloads and services is Kubernetes. It comprises a vast environment that is expanding and evolving quickly. Tools, assistance, and services for Kubernetes are readily accessible. Horizontal pod autoscaling plays a pivotal role in the modern-day world due to diversified user requests and resources. This paper proposes a custom autoscaler to perform horizontal pod autoscaling. The effectiveness of this lstm-based autoscaler is compared with the default autoscaler based on their response times and SMAPE (Symmetric mean absolute percentage error) error value is considered as an evaluation parameter. From the results, it is revealed that the LSTM model performs better in predicting the scaling of pods compared to the ARIMA model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software design

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An Efficient Multilingual Text Classification using IndicCorp dataset 5

An Efficient Multilingual Text Classification using IndicCor...

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

作者： Madanbhavi, Lalitha Desai, Padmashree Sirur, Neha Dhirendra Deshpande, Ananya Hiremath, Risheek V. Patil, Chetan M. Kle Technological University School of Computer Science and Engineering Hubballi India Rv College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Language detection is a crucial preprocessing step in natural language processing (NLP) tasks, especially in a multilingual environment. This paper presents a language detection system utilizing the Naive Bayes classifier, applied to the IndicCorp dataset, which includes a diverse range of languages prevalent in the Indian subcontinent. The IndicCorp dataset provides a rich source of textual data across multiple Indian languages, enabling the development of robust and accurate language models. Our system leverages the simplicity and effectiveness of the Naive Bayes algorithm, known for its high efficiency in text classification tasks. The proposed approach involves pre processing the dataset, extracting features using the CountVectorizer, and training the Naive Bayes classifier to identify the language of a given text. Experimental results demonstrate the system's capability to achieve an accuracy of 73.37% in language detection across various Indian languages within the IndicCorp dataset. This performance underscores the system's effectiveness in classifying languages like Hindi, Bengali, Tamil, and others, highlighting its potential for broader NLP applications and multilingual content processing. The necessity for multilingual classification arises from the need to support diverse linguistic communities and facilitate seamless communication. Accurate language detection enables various downstream applications such as machine translation, sentiment analysis, and information retrieval, enhancing user experience and accessibility. This study emphasizes the practical application of Naive Bayes for language detection and underscores the value of the IndicCorp dataset in developing language processing tools tailored to India's linguistic landscape. The achieved accuracy demonstrates our approach's robustness in handling diverse linguistic characteristics and showcases its potential impact on communication and information retrieval in multilingual contexts. © 2024 IEEE.

关键词： Data assimilation

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Modern Techniques for Ancient Art: Deep Learning in Rock Art Motif Classification

Modern Techniques for Ancient Art: Deep Learning in Rock Art...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Benni, Rashmi Jain, Shraddha D. Rao, Vaishnavi V. Yaligar, Nikhil A. Department of MCA KLE Technological University Karnataka Hubballi580031 India School of Computer Science & Engineering KLE Technological University Karnataka Hubballi580031 India

ISBN: (纸本)9798350374933

Rock art motifs are painted or carved on the stones. They represent a wide range of subjects,animals, humans,symbols, and abstract designs. They usually provide clarity into the beliefs and daily lives of the ancient human ancestors. These rock art motifs are present around the globe, representing their own unique culture. Traditional methods of analyzing rock art involves heavy field work, photographic documentation, manual classification by experts, and are also resource-intensive. This research utilizes deep learning techniques such as Convolutional neural network, Efficient-Net, and Res-Net for the automatic classification of rock art motifs. Deep learning is the subset of the ML that uses a neural network with many layers to automatically learn and extract complex patterns from large datasets. CNN,along with efficient-net and Res-Net(Residual net),have had a remarkable success in the image classification domain. CNN is the type of deep learning technique designed for analyzing image dataset. They have a number of Convolutional layers and pooling layers to help with data abstraction. Efficient-Net is a deep learning technique for image classification which uses the compound scaling techniques. Res-Net is also a deep learning technique used for image classification, which has residual blocks and identity mapping. Advancements in technology offer promising solutions to these challenges. This intends to enhance the efficiency and accuracy of classifying rock art images, providing archaeologists with a powerful tool to preserve and understand our culture. The result of this research extends to archaeology, art history and ancient culture which helps other researchers for future study. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Diabetic Retinopathy Diagnosis Using Intelligent Images Classification

Enhancing Diabetic Retinopathy Diagnosis Using Intelligent I...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Manavachari, Deepa Naragund, Jayalaxmi G. Baligar, Vishwanath P. Kle Technological University School of Computer Science Engineering Department Karnataka Hubballi580031 India

ISBN: (纸本)9798350388602

Diabetic is insulin resistance that causes abnormal blood sugar regulation in human beings and damages the kidney, heart, eye, and nerve systems. Retinopathy is the study of retina in organ eyes, which explains the damage that happened to the small blood vessels in the retina. This may cause impaired vision in human. Diabetic patients who are suffering from retinopathy may have blindness. Therefore, it is necessary to detect diabetic retinopathy (DR) stages at an appropriate time. Machine learning algorithms help to categorize diabetic retinopathy stages from retinal images. Thus, the research work on classification helps the doctors to detect diabetic retinopathy. The authors of this article present the novel algorithm Synergistic Image Compression and Feature Extraction (SI COMFEX) for classifying retinal images. Convolutional Neural Network (CNN) and Visual Geometry Group 16 (VGG16) machine learning algorithms or models are configured for multiclass classification. These models successfully classify retinal images into different classes such as mild, moderate, No DR, Proliferative Diabetic Retinopathy (PDR) and severe. The authors of this paper have also compared the designed SI COMFEX algorithm with CNN and VGG16. The performances of the models are verified using Precision, Recall and F1 Score parameters. The comparative study shows that SI COMFEX gives 65.31% testing accuracy, which is better than VGG16 and 10% lower than CNN model. © 2024 IEEE.

关键词： Ophthalmology

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WebGL vs. WebGPU: A Performance Analysis for Web 3.0 5

WebGL vs. WebGPU: A Performance Analysis for Web 3.0

引用

5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Chickerur, Satyadhyan Balannavar, Sankalp Hongekar, Pranali Prerna, Aditi Jituri, Soumya Center for Artificial Intelligence Research KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

This study investigates web 3.0 heterogeneous computing with webGL, webGPU, and IPFS. The primary focus is on the benefits of utilising these technologies to enhance the functionality and performance of web 3.0 applications. The study investigates web 3.0 as it currently exists and the constraints that developers face due to graphic, computational, and storage capabilities. According to the findings, incorporating webGL and webGPU can considerably increase user experience, speed, efficacy, and decentralization. Finally, this study summarizes the importance of continuing research in this subject, particularly with relation to platform interoperability and the future prospects of heterogeneous computing on web 3.0 via graphical APIs. © 2024 The Authors. Published by Elsevier B.V.

关键词： Rendering (computer graphics)

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Performance Evaluation of Ant Colony Optimization and Firefly Algorithm for the Traveling Salesman Problem: A Comprehensive Study

Performance Evaluation of Ant Colony Optimization and Firefl...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Benni, Rashmi Hiremath, Uma M KLE Technological University Department of MCA Karnataka Hubballi580031 India KLE Technological University School of Computer Science and Engineering Karnataka Hubballi580031 India

ISBN: (纸本)9798331539948

The Traveling Salesman Problem (TSP) is a classic NP-hard optimization challenge where a salesman must find the shortest route visiting a series of locations exactly once before returning to the starting point. Despite its straightforward definition, TSP's computational complexity escalates with the number of cities, posing significant challenges in terms of time and resources required for its resolution. This problem is crucial across various industries, influencing efficiency and cost-effectiveness. In logistics and transportation, solving TSP can lead to optimized delivery routes, reducing fuel consumption and travel time. Similarly, in manufacturing, it can streamline machine operations to cut down production time. Additionally, in fields like DNA sequencing, TSP algorithms enhance the arrangement of sequences, improving the accuracy and speed of analyses. Metaheuristic algorithms are often employed to tackle such complex problems. These high-level, problem-independent frameworks guide the development of heuristic optimization algorithms, offering flexible and robust solutions. Among these, Ant Colony Optimization (ACO) and the Firefly Algorithm (FA) are prominent. ACO mimics the foraging behavior of ants, utilizing heuristic data and pheromone trails to determine optimal routes. Conversely, FA simulates the firefly's attraction mechanism, where the search for optimal paths is driven by the quality of solutions, akin to light intensity. Both algorithms provide powerful tools for efficiently solving TSP and similar optimization challenges, offering unique approaches to enhance solution quality and computational efficiency. This comparative analysis explores the efficacy of ACO and FA in solving TSP, highlighting their strengths and potential applications in various optimization scenarios. © 2024 IEEE.

关键词： Ant colony optimization

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Accelerating fluid flow in Quantum Computing using GPU

Accelerating fluid flow in Quantum Computing using GPU

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Dandin, Joel Chickerur, Satyadhyan Kle Technological University School of Computer Science and Engineering Hubballi India Kle Technological University Centre for High Performance Computing School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350338003

In the last few years, quantum computing has evolved as the next big technological breakthrough. Many big companies are actively researching this area and have built open-source frameworks which can be used to develop solutions for problems that connect be solved by classical computers. One such problem is the solution of Navier-Stokes equations which takes exponential time for simulation but using quantum computers, they can be solved in polynomial time. But the current state of quantum computation does not enable everyone to test their implementation on a real quantum computer. In this situation, a simulator enables rapid prototype and testing techniques and algorithms for when the real quantum computer is ready. This paper focuses on simulating fluid flow algorithms using a quantum framework, such as Navier- Stokes equations, with the lattice Boltzmann method (LBM) used to numerically solve the relevant system of equations on both CPU and GPU to speed up the process. The quantum framework used in this paper is Qiskit, an open-source software development kit created by IBM that allows developers to create and test quantum algorithms. NVIDIA cuQuantum is an SDK of optimized libraries and tools for expediting quantum computing operations used in this paper to solve the fundamental problem of modeling large-size quantum circuits using the power of GPU © 2023 IEEE.

关键词： Navier Stokes equations

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A Two-level DDoS attack Detection using Entropy and Machine Learning in SDN 3

A Two-level DDoS attack Detection using Entropy and Machine ...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Puranik, Kishansmaran Patil, Kirankumar Ghaligi, Guruprasad Jannu, Rajath Patil, Sangamesh Narayan, D.G. Kachavimath, Amit KLE Technological University School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350338607

Software Defined Networking (SDN) separates the control plane from the data plane and has evolved into the next generation of internet architecture because of programmability and flexibility. This enables the centralized controller to completely control the entire network. Since the control plane is centralized, it is more prone to attacks. Distributed Denial of Service (DDoS) attack is one of the primary SDN attacks. In DDoS, a malicious attacker attempts to interrupt services by overwhelming the target network with malicious internet traffic. The resources of the impacted server, such as bandwidth and buffer size, are reduced that leads to the unavailability of resources to legitimate clients. In this work, we propose a new two-level approach for the detection of DDoS attacks using a faster entropy method on flow-based analysis and machine learning techniques for classification. Initially, entropy method is used to identify the malicious nodes. Later, the machine learning algorithms like Naïve Bayes, support vector machine, and random forest are used to detect attacks. We use Mininet emulator with POX controller to implement the proposed work. The results reveal that random forest has a better accuracy of 98.3% compared to the remaining algorithms. Furthermore, the proposed two-level approach reduces the false positive rate. © 2023 IEEE.

关键词： Network security

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