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Skillify: Enhanced Learning Management System Using Generative AI 7

Skillify: Enhanced Learning Management System Using Generati...

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

作者： Dhairya Hrishikesh, Jeeri Sonu, Gollapalli Persis Vaishnavi, Dandla Sanapala, Srinuvasarao Pallavi, L. B V Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India

ISBN: (纸本)9798350372816

Traditionally, students faced challenges in acquiring knowledge from textbooks due to difficulties in access and the high cost associated with them. Online learning aims to overcome these hurdles by providing a digital alternative that is accessible to everyone. Students can overcome geographical obstacles by using online platforms, gaining access to a wealth of instructional resources. This digital paradigm shift seeks to democratize education by making it more equitable and inclusive. However, the accessibility of online learning remains a significant challenge, especially for those who are visually impaired. Additionally, the vast number of courses offered on online platforms can make it challenging for learners to select the right course that can advance their careers. Learners often struggle to identify the best courses to improve their skills. To overcome accessibility challenges for the visually impaired, our platform incorporates a text-to-speech feature, which converts written text into spoken words. To address the problem of choosing the right course, we are implementing a course recommendation system to enable learners to acquire new skills that are beneficial to their advancement. Our platform encompasses three distinct modules: user, professor, and admin. Each module serves a specific role within our system. This project exemplifies the potential of artificial intelligence in revolutionizing education through enhanced learning experiences. Our proposed model aims to improve the efficiency of existing models by implementing text-to-speech and course recommendation features. © 2024 IEEE.

关键词： Contrastive Learning

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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...

引用

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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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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CNN-Based Detection of DDoS Attacks in Multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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

作者： Verma, Rashmi Jailia, Manisha Kumar, Munish Kaliraman, Bhawna Banasthali Vidyapith Department Of Computer Science And Engineering Rajasthan Tonk India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Vaddeswaram India Dpg Institute Of Technology And Management Department Of Computer Science And Engineering Gurugram India

ISBN: (纸本)9798350366846

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) to detect Distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural Networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection. © 2024 IEEE.

关键词： Denial-of-service attack

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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

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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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Multi-News Summarization Using Multi-Objective Differential Evolution Framework 15

Multi-News Summarization Using Multi-Objective Differential ...

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

作者： Mishra, Santosh Kumar Rajiv Gandhi Institute of Petroleum Technology Department of Computer Science and Engineering Amethi Jais India

ISBN: (纸本)9798350370249

Summarization is a critical challenge in natural language processing, gaining prominence as individuals increasingly seek concise and accessible information. In the context of news articles, generating summaries from various sources on a given topic is particularly valuable. To address this, we frame the multi-news summarization task as a clustering-based optimization problem. Our approach employs a differential evolution-based multi-objective framework to solve it. News sentences are clustered using a multi-objective clustering technique, optimizing two cluster validity indices - the XB index and the PBM index - that evaluate the separation and compactness of the sentence clusters. This optimization simultaneously leverages the multi-objective differential evolution algorithm to automatically determine the optimal number of clusters. We conduct experiments using the multi-news dataset and evaluate the performance using the ROUGE-1, ROUGE-2, and ROUGE-SU metrics. The findings indicate that our proposed method surpasses existing approaches in generating high-quality summaries. © 2024 IEEE.

关键词： Multiobjective optimization

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Advanced Deep Learning Driven Geospatial Analysis for GLOF Risk Reduction: A Case Study from Pakistan’s Northern Mountain Ranges

Advanced Deep Learning Driven Geospatial Analysis for GLOF R...

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2024 International Conference on Frontiers of Information technology, FIT 2024

作者： Murad, Nauman Ali Mir, Abinta Mehmood Shahzadi, Nazia Faculty of Computer Science Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (纸本)9798331510503

Glacial Lake Outburst Floods (GLOFs) pose a severe risk to populations in high-altitude areas, particularly in Pakistan's northern regions, where glacier melt has created 3,044 glacial lakes in Gilgit-Baltistan and Khyber Pakhtunkhwa [1]. The Chitral region is especially vulnerable, highlighting the need for robust monitoring and mitigation strategies. This paper focuses on using Deep Learning (DL) models for detecting and segmenting glacial lakes to mitigate GLOF risks. Using the Glacial Lakes Detection Dataset from High-Mountain Asia, covering 2080.12 km2 and including around 30,121 glacial lakes, we selected 1,200 cloud-free true-color images with ground truth masks. Our study evaluates DL models like DeepLabV3+, UNet, and YOLOv8 for lake segmentation and classification. Experimental results demonstrated model proficiency, with DeepLabV3+ (ResNet50 backbone and Dice loss function) achieving the highest IoU score of 77.2%. These findings highlight the potential of DL-based systems for enhanced GLOF monitoring and early warning. © 2024 IEEE.

关键词： Semantic Segmentation

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Mango Leaf Disease Detection and Classification Using Spatial Attention Enabled Ensemble Classification

Mango Leaf Disease Detection and Classification Using Spatia...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Pandiyaraju, V. Venkatraman, Shravan Abeshek, A. Aravintakshan, S.A. Pavan Kumar, S. School Of Computer Science And Engineering Vellore Institute Of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9798350364828

Leaf diseases are growing to be a concerning threat which impacts both the biological conditions and economic conditions of human beings. The poor cultivation would result in world hunger as well as increase in cost of living since reduction in supply would lead to increase in demand especially with necessity commodities. Mango being a seasonal crop with high value would face the same issue as well. Therefore, this research work proposes an ensemble learning classifier which is based on spatial attention that incorporates two deep learning classifiers namely the VGG-16 model and EfficientNetV2-BO model for immediate identification as well as classification of various mango leaf diseases such as Cutting Weevil, Gall Midge, Die Back, Bacterial Canker, Powdery Mildew, Anthracnose and Sooty Mould. The proposed ensemble model classifies effectively resulting with high evaluation scores of 97.13%, 97.53%, 97.33% and 97.38% in terms of Accuracy, Precision, Recall and Fl-Score respectively. These results are better than ResN et50, TL-based diagnosis using AlexNet, ANN, AlexNet and VGG16 models. © 2024 IEEE.

关键词： Cultivation

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Enhancing Athletic Performance:2D Human Pose Estimation Using Deep Neural Networks for Movement Analysis 7

Enhancing Athletic Performance:2D Human Pose Estimation Usin...

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

作者： Devi, Pothreddypally Jhansi Sumani, Aleshwaram Chandra, Chenna Mukesh Thrisha, Battu Bhanu Vamshi, Arkatala Sai B V Raju Institute of Technology Dept. of Computer Science Engineering Telangana India

ISBN: (纸本)9798350372816

computer vision is a field that is used to build many neural network-based prototypes efficiently. It analyses images and videos effectively and processes them digitally. Human Pose estimation (HPE) is one of the computer vision applications which additionally uses neural networks for prediction. Human pose estimation is used widely in many fields which includes action recognition, fitness, gaming, and motion capture. HPE identifies and locates the key points on the body joints of a person's body. These body points are connected to form a Skeletal Structure. This skeletal representation helps to easily visualize the image in 2-D. Focusing on Sports domain, HPE plays major role in estimating athlete movements. It helps in analyzing the feedback on the posture which allows to make adjustments during training. It also helps viewers to analyse the insights of athlete performance. To achieve the model that contains all these features, requires involvement of few frameworks. OpenPose is utilised for fast processing of videos and can detect the multiple people in one frame. OpenPose has the capability of handling the occulsion. On the Other hand, the model also import the capabilities of HRNet, which is used for analyzing athlete movements and tracks the gestures. After detecting the keypoints on the body joints, these points must be integrated to form 2-D images. To achieve the spatial relationship between the bodypoints Joint Graph Convolutional Neural Networks (JGCN) framework is used. The proposed model is estimated to generate approximately (4- 5)percent more efficiency and accuracy in its output 2-D image compared with the previous models. © 2024 IEEE.

关键词： Convolutional neural networks

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