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Feature Selection for Capacitated Vehicle Routing Problem Algorithm Selection 4

Feature Selection for Capacitated Vehicle Routing Problem Al...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sehta, Neha Thakar, Urjita Shri G.S. Institute of Technology and Science Computer Engineering Department Indore India

ISBN: (纸本)9798331518981

The practical application of feature selection is evident in the machine learning domain. The purpose of a learning activity is to identify a mapping of features to output that generalizes the data. Therefore, identifying significant features is an utmost requirement. Feature selection approaches provide a means to decrease computational time, enhance predictive accuracy, and facilitate a deeper comprehension of the underlying data. This work presents an overview of feature selection methodologies documented in the literature. We additionally apply feature selection techniques to a dataset containing capacitated vehicle routing problem instances features and algorithm performance, designed to facilitate algorithm selection. Random forest which is an embedded method of feature selection is used which explicitly provides importance of each attribute for the underlying classification task. © 2024 IEEE.

关键词： Adversarial machine learning

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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Fine-tuning Llama-2-7b Model for Conversation Summarization 15

Fine-tuning Llama-2-7b Model for Conversation Summarization

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nivethitha, V. Verma, Nirvik Bokadia, Ronit Jaju, Ronith Vellore Institute of Technology Chennai School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350370249

In this research paper, we explore the optimization for conversation summarization of the Llama 2.7 b model by quantization-aware fine-tuning, specifically exploiting QLORA quantization techniques. In natural language processing (NLP), large language models (LLMs) have become powerful tools for various text-processing tasks including dialogue summarization in a rapidly evolving landscape. Still, the computational needs of these model's present obstacles to their practical implementation in conversational interfaces and live applications. Quantization observance fine-tuning is an optimistic way of compressing model parameters while still enhancing performance. This analysis demonstrates through rigorous experimentation that the QLORA quantization is effective on fine tuning the Llama 2.7b model for conversation summarization. Fine-tuning the model on a well-rounded dataset with conversational data derived from different sources such as social media, chat logs, and customer interactions makes it more adaptable to any sort of dialog that takes place. The incorporation of QLORA quantization's also improves the attention mechanism of the model enabling it to obtain relevant information such as generating concise summaries. The latest version of Llama is 2.7. It is called Llama 2.7b and provides several practical advantages for summarizing dialogues such as improved computational efficiency, better user experience in conversational interfaces, and capturing key insights from long conversations. Its outputs are in line with the current research on LLM-based conversation summarization and exhibit the viability of optimization techniques like quantization-aware fine-tuning to maximize the performance of large language models for particular natural language processing tasks (NLP). © 2024 IEEE.

关键词： llama 2 LLM Natural Language Processing QLORA Quantization

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Comparative Study of Techniques for Congestion Avoidance & Control in Wireless Body Area Networks

Comparative Study of Techniques for Congestion Avoidance & C...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Jakhar, Isha Vijraniya, Meenu Singh, Hitesh Kumar, Vivek K R Mangalam University Computer Science Department Gurugram India Noida Institute Of Engineering & Technology Computer Science Department Noida India

ISBN: (纸本)9798350366846

In past few years, considerable amount of research has been done in wireless sensor networks which have helped to discover the potential of wireless sensor networks (WSN) in many areas such as military, environment, communication, healthcare etc. Major improvements have been noted in healthcare having prominent discoveries in wireless body area networks (WBAN). WBANs help in monitoring the health of the patients remotely and prove to be a very economical solution for healthcare providers as well as patients by gathering biological data and transmitting it to the base station or the healthcare providers. Along with benefits, come various limitations of WBANs like coverage area, data security, congestion, scalability etc. Congestion has become one of the major issues in healthcare and if not addressed in time, can lead to critical packets not being sent and delay in receiving critical information which can prove harmful for the patient. In this paper, an extensive study has been done to study about various protocols developed to detect, avoid and mitigate congestion in order to provide seamless and timely delivery of services. © 2024 IEEE.

关键词： Wireless sensor networks

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A Framework for Advanced technology in Energy-Efficient Dew Computing for Smart Healthcare

A Framework for Advanced Technology in Energy-Efficient Dew ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Singh, Kiran Deep Singh, Prabh Deep Maurya, Sudhanshu Verma, Rohan Joshi, Kapil Khilnani, Laxmi Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

By allowing for remote patient monitoring, real-time diagnostics, and efficient healthcare data administration, smart healthcare technologies are transforming the medical business. However, energy consumption and latency concerns have been raised due to the growth of resource-intensive healthcare applications in cloud-based infrastructures. This study introduces a novel method for overcoming these obstacles by developing a Dew Computing framework optimized for smart healthcare settings that is both energy-efficient and scalable. This system, influenced by Dew Computing, brings cloud functionality to the network's periphery, where data and medical equipment functions are created. The framework prioritizes hardware selection that is light on energy consumption, dynamic allocation of resources, efficient data management, and encrypted communication channels. Our extensive analysis demonstrates the suggested framework's usefulness and low energy use in practical healthcare settings. Through our experiments, we have found that considerable decreases in energy use are possible without compromising the safety or quality of healthcare services. This study provides an energy-efficient answer for the development of future smart healthcare systems, adding to the expanding body of knowledge on edge computing and its applications in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

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Social Distance and Face Mask Detection using vision-based sensors and IP cameras 3

Social Distance and Face Mask Detection using vision-based s...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Vineela, A. Devi, G. Lavanya Deekshit, G. Vinay GMR Institute of Technology Department of Computer Science and Engineering Rajam India Department of Computer Science and System Engineering Vishakhapatnam India

ISBN: (纸本)9781665494007

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask. © 2023 IEEE.

关键词： computer vision

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Meta-heuristic Algorithms for Double Roman Domination Problem

Meta-heuristic Algorithms for Double Roman Domination Proble...

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作者： Aggarwal, Himanshu Reddy, P. Venkata Subba Department of Computer Science and Engineering National Institute of Technology Warangal Telangana Hanamkonda506004 India

A variety of domination concepts have been defined to provide better routing and defense strategies under different constraints. A double Roman dominating function (DROMDF) on a simple, undirected graph G is a function g:V→{0,1,2,3} such that every vertex x∈V with g(x)= .is adjacent to at least two vertices y1,y2 with g(y1)=g(y2)= .or a vertex z1 with g(z1)=3. Also, a vertex p with g(p)= .is adjacent to at least one vertex q1 with g(q1)≥2. γdR(G), the double Roman domination number of G, is the smallest possible weight of all possible DROMDFs of G. Determining double Roman domination number of a graph is known to be NP-hard. Hence in this paper, we propose a genetic algorithm based approach for solving double Roman domination problem in which three heuristic algorithms have been proposed and problem specific crossover operator and a feasibility function has been developed. Further, we propose an ant colony optimization algorithm to solve double Roman domination problem. This paper provides an in-depth illustration of two algorithms for solving double Roman domination problem. Effectiveness of the proposed meta-heuristic algorithms is tested on the random graphs generated using NetworkX Erdős-Rényi model, a popular model for graph generation and Harwell–Boeing dataset, a well-known dataset for graph related problems. Further, we compare the results of both the meta-heuristic algorithms and the experimental results show that the proposed meta-heuristic algorithms for solving double Roman domination problem give a near optimal solution in reasonable time. Experimental results also show that the proposed ant colony optimization algorithm for solving double Roman domination problem outperforms genetic algorithm based procedure. © 2024

关键词： Genetic algorithms

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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Event Management System Using Generative AI 7

Event Management System Using Generative AI

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thummala, Sruthi Thammishetti, Saketh Varkol, Sharanya Thirunahari, Amarthya Lakshmi Kanthey, V.V.S. B V Raju Institute of Technology Dept. Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

This project depicts how Artificial intelligence is used in developing a "User-Engaging Event Management System". The developed system will spontaneously record all the events and online contests registered by an individual. Motivates the individual to participate the particular event by providing sufficient resources. It implements a persistent alarm or remainder system that operates continuously. The implementation of the system starts with choosing appropriate technologies (Deep learning Libraries, Natural Language Processor, DBMS, Web Development Framework and so on) focusing on generative AI followed by data collection (from existing event data, social media, crowdsourcing and so on) and preprocessing the data to ensure the quality for AI model training which integrates with app architecture. This study evaluates the effectiveness of the generative AI model in Event Management App by employing key numerical metrices. The accuracy metric gauges the model's overall correctness in generating event-related content, while precision focuses on the accuracy of positive predictions. It incorporates a robust feedback mechanism ,allowing users to provide input and receive corresponding responses, enhancing the overall user experience. The system will provide security for user information, access controls, and authentication protocols © 2024 IEEE.

关键词： Access control

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Retrospective Analysis of an Interpretable Skin Cancer Classification using Deep Learning Models 1

Retrospective Analysis of an Interpretable Skin Cancer Class...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Abraham, Shaun Shaji Dinesh Peter, J. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350372748

Skin cancer, particularly malignant melanoma, presents major diagnostic challenges due to similarities with benign tumors. Automatic detection systems based on deep learning algorithms provide intriguing answers, but lack interpretability, limiting clinical application. This research presents an explainable artificial intelligence (XAI) approach to skin lesion classification, with the goal of improving diagnostic accuracy and clinician trust. The created algorithm accurately identifies types of skin lesions using the dataset. Using the local interpretable model-agnostic explanations (LIME) framework, the model creates visual explanations to assist dermatologists in rational diagnosis. Explainability is integrated into the model, increasing its usability in real-world clinical practice. Furthermore, the study emphasizes the importance of XAI in increasing the interpretability and trustworthiness of deep learning models for skin cancer diagnosis, focusing on crucial features such as asymmetry and pigment network classification. This study helps to address the critical need for transparent and accurate AI models in the medical realm, particularly in preventing the rising global prevalence of skin cancer. © 2024 IEEE.

关键词： Deep learning

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