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Machine Learning Approach towards Stratification of Opioid Patients Based on Data Analysis - A Study 15

Machine Learning Approach towards Stratification of Opioid P...

引用

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

作者： Praneeth, A. Siva Ramakrishna Reddy, Ch. Bayapa Reddy, Ch. Vijaya Durga Sai, G. Revanth Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

This work investigates machine learning and deep learning methods for drug categorization in order to address the urgent problem of opioid abuse. Because of the intricacy of the issue, many approaches to anticipate opioid prescribing have not produced adequate results in terms of accuracy. The goal of this study is to use structured and unstructured data from the MIMIC-III database to categorize patients who are opioiddependent. To distinguish between purposeful and inadvertent opioid users, 41 pertinent variables were included in both classic machine learning and deep learning models. The findings show that deliberate users from structured data and inadvertent users from unstructured data may be predicted with greater accuracy. Furthermore, encouraging outcomes were obtained from the integration of these models via knowledge distillation. Additional clarification on the categorization of opioid patients was provided by ablation analysis. Utilizing cutting-edge methods such as knowledge distillation and attention processes, this study improves the accuracy of drug categorization and provides insightful information for managing the opioid problem. © 2024 IEEE.

关键词： Ada Boost convolutional neural network Lstm Opioid Random forest XG Boost

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Detecting DDoS Attacks in Software-Defined Networking through the Utilization of Supervised and Unsupervised Learning Techniques 15

Detecting DDoS Attacks in Software-Defined Networking throug...

引用

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

作者： Sathwik, T. Reddy, Tadi Sai Vishruth Gangavarapu, Venkat Rohith Bhaskaran, Sreebha Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

Given the dynamic nature of SDN environments, it is necessary to quickly recognize Distributed Denial of Service (DDoS) attacks to maintain our network’s integrity and ensure uninterrupted service delivery. However, the centralized control plane of SDN makes it particularly vulnerable, as a compromise here can allow attackers to manipulate network traffic and configurations extensively. The traditional detection methods often have trouble keeping up with these attacks’ dynamic nature, resulting in high false positive rates and poor response. This paper proposes an approach that uses supervised and unsupervised learning techniques to enhance DDoS attack detection capabilities in SDN infrastructures. Using Mininet and Ryu, our method involves collecting network traffic data, extracting relevant features, and constructing classification models based on labelled datasets that can identify typical attack patterns. Additionally, these initiatives are supported by unsupervised learning approaches like clustering or anomaly detection, which help reveal new types of attack signatures not seen before and abnormal behaviours within networks caused by them. As a result, what we have done here enables real-time detection against DDoS through seamless integration into the Ryu controller, thus strengthening the security posture against emerging threats in cyberspace targeting SDNs. ©2024 IEEE.

关键词： Self-supervised learning

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Development of a Comprehensive Learning Management System using Full Stack Technology: A Modern Approach to Educational Platforms 5

Development of a Comprehensive Learning Management System us...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Supriya, M. Rathinavel, Keshav Radhakrishnan, Divya Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

Using the advantages of modern web technologies, this project utilizes powerful data modelling characteristics of Prisma and dynamic front-end capacities of *** to design a Learning Management System (LMS). The aim of this project is to create a comprehensive platform that would effectively facilitate the organization of educational resources and assignments, as well as student cooperation, ensuring scalability and performance at the same time. Many software engineering practices are utilized during this project for code maintainability, security, and performance. The full cycle of software development consists of testing and iterative adaptation of the developed systems based on the opinions of end-users. Ultimately, the LMS is improved through thorough testing and feedback from the users in an iterative and progressive process. The findings of this research are pertinent to the future developments of educational technology that will seek to implement modern, full-featured, and user-friendly LMS. © 2024 IEEE.

关键词： Software testing

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Swarm-Optimized Deep Convolutional Networks for Precise Detection and Grading of Diabetic Retinopathy 4

Swarm-Optimized Deep Convolutional Networks for Precise Dete...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sadhana, U. Singh, Tripty Beena, B.M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350364729

Diabetic Retinopathy (DR) is a very critical problem of diabetes that can lead to blindness if not detected and treated in the early stage. This research was conducted on this difficult disease based on image processing and Machine intelligence. In this research, our model detects and classifies the DR using the swarm-optimized deep convolutional networks. The proposed model integrated the convolutional neural networks (CNNs) along with Particle Swarm Optimization (PSO) to enrich the performance of the model execution and results with fine-tuned hyperparameters. This custom-optimized CNN model, designed for image classification, was evaluated on the Kaggle diabetic retinopathy dataset. The swarm-optimized deep CNN model presented balanced performance across all DR classes and achieved a precision of 0.93, recall of 0.97, and F1-score of 0.95 for all classes. The model's overall accuracy reached 0.84 on the selected dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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A Two-Stage Ensemble Approach for Analysis of Optimizing Customer Churn with Lime Interpretability 5

A Two-Stage Ensemble Approach for Analysis of Optimizing Cus...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Tiwari, Utkarsh Ashwani, Siddhant Tripathy, Aayushi Jeeban Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350383522

Businesses must outbid suppliers to win over new clients in a very competitive industry. Since client retention directly affects a company's revenue, it is a prominent topic for research. Businesses can prevent client attrition by proactively addressing it when it occurs. A two stage predictive ensemble method is proposed with Gaussian Naive Bayes, Perceptron, Gradient Boosting Classifier, Decision Tree, K-Nearest Neighbors, Logistic Regression, and XGBoost used in the first stage model classification, and then models like Adaboost, Bagging Classifier, Stacking, and Voting are used in the second stage to increase forecast accuracy for the top four models chosen in the first stage. The LIME Explainer algorithm also analyzes data instance adjustments and their effect on predictions, improving interpretability for 'black box' models. This two-step approach addresses issues in crucial decision-making, by optimizing the final model's predictive power and interpretability. © 2024 IEEE.

关键词： Classification Dimensionality Reduction Ensemble Techniques LIME Explainer Two-stage Model Training

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Fault Detection in Solar Panel Images using a novel feed-forward based CNN model 15

Fault Detection in Solar Panel Images using a novel feed-for...

引用

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

作者： Karuppasamy, ArunaDevi Venugopal, Dhanalakshmi Balaji, P.S. Selvakumar, Karthik Ramachandran, Brintha Eliyan, Greeshma Cherian, Anju Mathew Pillai, Indu Govinda Gulf College Dept. of Computing Sciences Muscat Oman Sultan Qaboos University Dept. of Electrical and Computer Engineering Muscat Oman National Institute of Technology Dept. of Mechanical Engineering Rourkela India Pondicherry University Dept of Green Energy Technology Pondicherry India Saradha Gangadharan College Dept of Computer Applications Pondicherry India

ISBN: (纸本)9798350370249

The increasing dependence on renewable energy highlights the critical need for efficient and reliable maintenance strategies for solar panel installations. A key aspect of maintenance is early detection of faults in individual solar panels, which results in reduced energy production and increased maintenance costs. In this paper, we proposed a novel approach for fault detection in solar panel images. The proposed novel Feed-forward based CNN architecture utilizes the ability to automatically extract features from images to accurately identify faults in solar panels. The proposed CNN model trained on a small, annotated dataset of solar panel images, which includes clean and faulty panels. The Experimental results show the superiority of Feed-forward based CNN model in fault-detection compared to traditional CNN methods, achieving higher precision and recall rate in a wide range of fault types. The proposed approach not only significantly reduces the time and resources required for manual inspection, but also enhances the effectiveness of identifying faults within the solar energy systems. In conclusion, this paper introduces a novel Feed-forward based CNN for fault detection in solar panel images, which provides a solution to enhance the efficiency of solar power installation. © 2024 IEEE.

关键词： Convolutional Neural Network Fault Detection Filter Learning Logistic Regression Solar-Panel Images

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Implementing Internet Key Exchange with Image-based Key Exchange through Socket Programming 9

Implementing Internet Key Exchange with Image-based Key Exch...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sreedeepika, M. Lekhya, N. Sri Likesh, K. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

The primary objective of our project is to execute the Internet Key Exchange (IKE) protocol, which is commonly utilized for secure key exchange in virtual private networks (VPNs) and other secure connections. Our project is divided into two parts: a server and a client, both of which are written in Python and use socket programming. In order to receive an encrypted symmetric key, the server first creates anRivest, Shamir, Adleman (RSA) key pair, then delivers the client its public key. The symmetric key is contained into an image file and encrypted with the server's public key. A symmetric key is created on the client side, encrypted using the public key of the server, and hidden inside an image. After then, the server receives this encrypted key image. After the server gets the picture, it uses the decrypted symmetric key to decode another picture, which concludes the key exchange procedure. This creative strategy not only makes use of IKE's advantages but also investigates a novel technique for key exchange using picture hiding. © 2024 IEEE.

关键词： Network security

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A Framework for Fleet Management using IoT and Predictive Analytics 15

A Framework for Fleet Management using IoT and Predictive An...

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

作者： Shivkumar, S. Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

The integration of IoT (Internet of Things) technology in vehicles has become increasingly imperative in today's rapidly evolving automotive landscape. IoT empowers vehicles to become more intelligent, transforming them into interconnected nodes capable of real-time data exchange. This capability is essential for enhancing road safety, as IoT-enabled vehicles can communicate with one another. In an era of data-driven decision-making, IoT in vehicles is not just a convenience but a necessity for safer, more efficient, and environmentally responsible transportation. In cosmopolitan cities like Bengaluru, the city's roads, once renowned for their tree-lined beauty, now find themselves choked with a relentless surge of vehicles. There is heavy traffic throughout the city with the air quality deteriorating in the city. Despite efforts to expand infrastructure and promote public transportation, Bengaluru's traffic remains a formidable challenge. This work proposes a simple framework for an IoT enabled visualization and predictive analytics in the context of fleet management in Bengaluru's scenario. This architecture could be further enhanced using various other sensors and machine learning models. This framework was implemented, simulated and analyzed using MATLAB and ThingSpeak application. © 2024 IEEE.

关键词： Decision making

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Prediction of Bank Performance Using Machine Learning Classifiers Optimized by Genetic Algorithm

Prediction of Bank Performance Using Machine Learning Classi...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Ainan, Ummey Hany Nur-E-Arefin, Md. Dept. of Computer Science & Engineering Chittagong Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Bank performance is defined as the reflection of the way by which the assets of the bank are utilized in a form which enables it to accomplice its targets. Economic development highly depends on the functionalities of the banks. In past statistical approach is used to predict bank performance. Nowadays Machine Learning (ML) approaches are used in banking sector for better accuracy. In this work three famous Machine Learning classifiers named Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are used to find out the bank performance. The dataset used in this work are consist of 50 Turkish banks, 30 American banks and 20 European banks. The data have 24 performance indicators that measures performance from the year of 2010 to 2020. CAMEL technique is applied in this dataset in order to find ratings of the banks. In this study Genetic Algorithm (GA) plays a vital role. GA is used as optimizer and feature selector. At the end the models are evaluated with and without feature selection as well as with and without optimization. In this study SVM with optimization but without feature selection provides best accuracy among all the models which is 97.06% test accuracy. On the other hand, LR provides 80.21% test accuracy with feature selection but without optimization which is lowest in the whole study. © 2022 IEEE.

关键词： Feature Selection

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An Innovative Approach for Detection of Motorcyclists Helmet Non-Compliance using Edge Computing 5

An Innovative Approach for Detection of Motorcyclists Helmet...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Jithendra, Mulpuri Krishna, M. Madhava Charan Sai, M. Harsha, P. Sri Supriya, M. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350389609

Using innovative technologies and creative strategies can help reduce fatalities and lessen the financial impact of accidents. This suggests a significant improvement in global traffic law enforcement and road safety advocacy. The study proposed in this paper uses an innovative approach to address the challenge of motorcyclists neglecting traffic regulations through a deep learning-driven framework. Using advanced pre-trained object detection models, the system accurately identifies helmetless motorcyclists and number plates. In addition, the proposed model does the Optical Character Recognition (OCR) to extract the license plate information from the identified motorcyclists who are not wearing helmets. The architecture was tested using an NVIDIA Jetson as the edge device, delivering quick performance, efficiently managing large data volumes, and optimizing resource use. Extensive testing and in-dept. analysis show that the proposed approach is robust and highly beneficial. The proposed system integrates OCR algorithms, edge computing capabilities, and advanced deep learning techniques to offer a scalable solution for assisting law enforcement entities and enhancing traffic safety initiatives. © 2024 IEEE.

关键词： Optical character recognition

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