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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Srivastava, Neeraj Kumar Pandey, Prafull Mishra, Manoj Kumar Mishra, Vikas Department of Computer Science and Engineering United Institute of Technology Prayagraj Naini India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj Naini India Department of Computer Applications United Institute of Management Prayagraj Naini India

ISBN: (纸本)9789819930098

The use and importance of educational data mining (EDM) are growing very fast as the entire world is going digital. The predictions of EDM [1, 2] are playing very important role not only in improving the academic performance of individual students but also are very useful for institutions, policy makers, administrators to plan and design new policies that will certainly help them to achieve high academic performance of their students. On this basis, this paper approaches students’ performance in secondary school of two Portuguese schools. The informational elements comprise demographic, societal, and educational [3] features. Although there are many ML [4, 5] algorithms, yet in this paper, five ML classifiers such as random forest, Naïve Bayes, logistic regression, decision tree, and support vector machine have been used to predict the students’ performance that on the basis of given features, whether a student can be passed in secondary education or not. On the dataset, the Naïve Bayes classifier gives the best performance than other classifiers, when the dataset is partitioned into 80–20 ratio as training and test datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Soil Analysis Using Deep Learning for Precision Agriculture 1

Soil Analysis Using Deep Learning for Precision Agriculture

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Fotabong, Thierry Forsack Muhammad, Abdullahi Keisham, Murphy Mehrotra, Tushar Singh, Rajneesh Kumar Singh, S. Pratap Agrawal, Arun Prakash Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India Symbiosis Institute of Technology Nagpur Campus Deemed University Pune India

ISBN: (纸本)9798350365016

This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study addresses the limitations of traditional soil classification methods, which are often labor-intensive and reliant on expert judgment. The proposed approach involves collecting soil image data, preprocessing it using techniques such as Gabor filtering, and classifying the images with a CNN model. The methodology includes data acquisition, feature extraction, and model training, aiming to accurately predict soil parameters using diverse input data sources like sensor readings and satellite imagery. The potential benefits of this approach include real-time monitoring, optimal fertilizer application, and enhanced crop management, which can contribute to sustainable agricultural practices and food security. © 2024 IEEE.

关键词： Convolutional neural networks

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Granular Network Traffic Classification on Mobile Application Service for Better Parental Control 2

Granular Network Traffic Classification on Mobile Applicatio...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dhivvya, J.P. Aswini Dath, Guru Chinta, Mahesh Kumar Karki, Ankit Amrita Vishwa Vidyapeetham Amrita School of Computing Center for Wireless Networks and Applications Department of Computer Science and Engineering Kollam Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Kollam Amritapuri India

ISBN: (纸本)9798350360523

The widespread use of mobile applications among children had its limitations in excessive screen usage in entertainment. Traditional parental control techniques mostly depend on blocking applications based on their name or categories and are ineffective at identifying potentially new dangerous applications. This study addresses this issue by introducing a paradigm shift for protecting kids' digital experiences through granular network traffic classifications. This system classifies mobile app network traffic into various groups by implementing machine learning or deep learning models. Machine learning models like linear regression, Adaboost Classifier, Support Vector Classification (SVC), Ridge Classifier and deep learning models like Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short Term Memory (LSTM) were used to identify and classify mobile applications. Initially, the deep learning RNN model results had 80% accuracy in the classification of mobile apps, and the machine learning logistic SVC and Adaboost Classifier had 72% accuracy. After adding new applications to the existing dataset, the accuracy of the RNN model has remained the same 80% accuracy. Meanwhile, the machine learning model accuracy reduced to 70%, proving the flexibility of the system. This work can assist parents in monitoring their kids to use mobile applications more for education. This work has a huge scope for information technology management of social change using hybrid learning models in software-defined networking in local networks for the administrators to empower their users to wisely use the internet. © 2024 IEEE.

关键词： Learning systems

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Telecom Churn Classification using Scatter Search and Random Forest Classifier

Telecom Churn Classification using Scatter Search and Random...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Varun, A.S. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte Institute of Professional Education Nitte University Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

The Telecom Churn Dataset is a valuable resource that contains customer-related information such as demographics, usage patterns, billing data, and customer service interactions. The proposed model is used to explore various factors that might impact churn behavior and apply machine learning techniques to create predictive models for proactive retention efforts. This study have employed different approaches and methods, including random forest, logistic regression, and decision trees, as well as feature selection techniques like Correlation-based Feature Selection (CFS), scatter search etc. The proposed methodology involves preprocessing the data using techniques like SMOTE to handle class imbalance, selecting relevant features through Scatter search, and applying classification algorithms like the Random Forest (RF) classifier. The proposed model using Scatter search and Random Forest classifier has best formed with an accuracy of 91.9%, precision and recall of 0.919. Thus the model is well suited for telephone churn classification with the optimized feature set selection and classification. © 2023 IEEE.

关键词： Classification feature reduction random forest SMOTE Telecom churn

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AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD and BRP 3rd

AMBTC-Based High Capacity Data Hiding Scheme Exploiting PVD ...

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3rd International Conference on Advanced Computing and applications, ICACA 2024

作者： Jana, Manasi Jana, Biswapati Joardar, Subhankar Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India

ISBN: (纸本)9789819747986

Secret data hiding in compression domain reduces the transmission cost and also ensures more security than spatial and transform domain. In this paper, we present a data concealing strategy on the compression domain that delivers high embedding capacity (EC) while also improving the quality of transformed images. To compress the original image, a modified AMBTC method is used for its low time complexity. Modified AMBTC is used to transform one bit plane into two bit plane, which enhances the quality of the stego image. Three levels of security are applied on secret data to make the scheme more secure. Data are embedded into two adjacent pixels of each 1×2 block using PVD method. The robustness of the scheme is analysed by applying unknown permutation, unknown shared key and unknown iteration steps. The experimental results reveal that the suggested method outperforms the previous schemes in terms of embedding capacity and imperceptibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ambtc BRP Data hiding PSNR PVD

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'Optimizing Algorithmic Efficiency: Exploring Advanced Data Structures for High-Performance Computing' 3

'Optimizing Algorithmic Efficiency: Exploring Advanced Data ...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Rao, Lade Gunakar Nitya, Erupaka Devi Kusumba, Jyoshna Rajchandar, K. Narasimha Murthy, S. SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Telangana Hyderabad India Government Degree College Computer Science And Applications Telangana Warangal India Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350361391

This work explores advanced data structures as a means of optimizing algorithmic efficiency in high-performance computing. The search for faster and more scalable algorithms becomes essential as computing demands rise further. This paper explores state-of-the-art data structures intended to improve overall performance, memory usage, and computational speed. We hope to offer insights into the applicability and impact of various advanced data structures on high-performance computing tasks through a thorough review and analysis of them, including but not limited to self-balancing trees, hash tables, and advanced graph structures. The results of this study not only advance our theoretical knowledge of these data structures but also provide developers and researchers with useful advice on how to utilize algorithmic efficiency in quickly fully. It is important to remember that algorithmic urban planning is an interdisciplinary field that encompasses data science, computer science, urban planning, and many other fields. The goal of integrating algorithms and smart technologies is to build more efficient, sustainable, and liveable urban environments.[8]. © 2024 IEEE.

关键词： Advanced Data Structures Algorithmic Efficiency Computing (keywords) High-Performance Computing Optimizing

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HybridNet: Integrating Multiple Approaches for Aerial Semantic Segmentation

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SN computer science 2024年第1期5卷 133页

作者： Chouhan, Avinash Sur, Arijit Chutia, Dibyajyoti Aggarwal, Shiv Prasad North Eastern Space Applications Centre Meghalaya Umiam 793103 India Department of Computer Science and Engineering Indian Institute of Technology Assam Guwahati 781039 India

In recent times, semantic segmentation for VHR aerial images has become an emerging research topic due to its widespread applications in disaster management, environmental monitoring, natural resource mapping, etc. The problem of semantic segmentation can be modeled as an image-to-image mapping problem where pixel-level classification is required. Pixel level classification is challenging for the high-resolution aerial image due to the presence of the tiny objects in low-frequency and more information details for such tiny objects required for dense semantic labeling. In general, encoder–decoder based architecture for semantic segmentation suffers from information loss due to the up and downsampling process. To handle this, we extend a high-resolution network with dense connection integration to preserve the original resolution and better parameter sharing. We also incorporate a lightweight self-attention module for positional attention, which results in better segmentation maps. Additionally, we use a generalized Hough transform based deep voting module for pixel dependencies extraction. Experimental results reveal that the proposed model achieves the best mean intersection over union and overall accuracy in local and benchmark evaluation on the Vaihingen and Potsdam datasets. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： High-resolution connection Hough voting Semantic segmentation

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Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT 9

Monitoring, Tracking and Fighting Pandemics using Drone-base...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Kumar, K. Praveen Sangeetha, S. Kumar, T Rajesh Bhuvaneswari, R. Rasi, D. Mahaveerakannan, R. Kakatiya Institute of Technology and Science Department of Information Technology Warangal India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Computer Science and Applications Vellore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

关键词： Coronavirus

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Automated semantic segmentation and Diagnosis of Coconut Tree Infection Using Drone Imagery and Machine Learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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

作者： Vasumathy, M. Devakanth, J. Jude Moses Anto Thirugnanam, Mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of economy to the national GDP. About 19,247 million coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at 10-15%. Major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity. © 2024 IEEE.

关键词： Drones

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BuddyBot: AI Powered Chatbot for Enhancing English Language Learning 2

BuddyBot: AI Powered Chatbot for Enhancing English Language ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Dhivvya, J.P. Karnati, Sai Bhargav Amrita Vishwa Vidyapeetham Amrita School of Computing Center for Wireless Networks and Applications Department of Computer Science and Engineering Kollam Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Kollam Amritapuri India

ISBN: (纸本)9798350360523

Today, many students are having a hard time learning to speak the English language and the problem is even worse for those in rural areas, where both teachers and resources are rare. We are trying to break this disparity and provide better resources for those in need utilizing Artificial Intelligence and Natural Language Processing (NLP) to contribute to the SDG4 Quality and Inclusive Education. In this paper, we present the idea of how we developed Chatbot named BuddyBot Utilizing OpenAI Generative Pre-Trained Transformer 2 (GPT-2) and Google Text-to-Text Transfer Transformer (FLAN-T5) models. This system incorporates diverse datasets for fine-tuning and specialized tasks in English language learning. BuddyBot follows a five-phase architecture - User interface, Pre-processing, Natural Language Processing (NLP) stack, language generation engine and adaptive learning personalization. The E-Learn web-based interface offers interactive courses with BuddyBot assistance, emphasizing adaptive learning personalization. Dataset preprocessing includes tokenization, sentiment analysis, and specialized formatting. The NLP stack integrates GPT-2 and FLAN-T5 models into the framework, maintaining dialogue structure and personalization. Validation tests reveal model effectiveness, with reduced training loss (0.778 to 0.414 by epoch 8) and increased accuracy (0.810 training, 0.792 validation). Overall, BuddyBot's robust methodology and validation results showcase its efficacy in providing personalized English language learning experiences. © 2024 IEEE.

关键词： Sentiment analysis

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