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

作者： Deb, Priom Bhuiyan, Asibur Rahman Mahrin, Habiba Momenine, Marzanul Evan, Md. Saharan Sohan, Md. Sajid Ullah Khan, Md. Tazfiq Brac University Dept. of Computer Science And Engineering Dhaka Bangladesh

ISBN: (纸本)9798350394474

Online sexism refers to gender-based discrimination and harassment that occurs in online spaces, such as social media platforms, online communities, and forums. Machine learning models can recognize and lessen online sexism by automatically detecting sexist content in social media posts. In this experimental analysis, we have evaluated the performance of five different machine learning models, including Logistic Regression, Gaussian Naive Bayes, Decision Tree Classifier, Support Vector Machine (SVM), and KNeighbors Classifier. Our objective was to detect online sexism using the Explainable Detection of Online Sexism (EDOS) data set. We preprocess the data set by cleansing the text data with a regular expression, removing null values, removing redundant columns, and vectorizing it with TfidfVectorizer. The results of our study indicate that the Logistic Regression, Gaussian Naive Bayes, Decision Tree Classifier, and Support Vector Machine (SVM) models are efficacious in detecting occurrences of online sexism. Nonetheless, the KNeighbors Classifier algorithm shows comparatively lower accuracy in this aspect. The present analysis highlights the capacity of machine learning models to identify instances of online sexism. The highest accuracy score we obtained for the Support Vector Machine (SVM) model is 94.64%. © 2024 IEEE.

关键词： Logistic regression

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Single Object Tracking in Satellite Videos Through Deep Learning and Motion Detection Techniques 6

Single Object Tracking in Satellite Videos Through Deep Lear...

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

作者： Hayder, Israa Alizadeh, Fattah Dept. of Computer Science and Engineering University of Kurdistan Hawlere Erbil Iraq Dept. of computer science Toronto Metropolitan university Toronto Canada

ISBN: (纸本)9798350394634

Single object tracking in satellite videos has recently gained a lot of attention in the field of computer vision. Although the satellite images are very informative, the small size of the objects and limited spatial and temporal resolution might make object tracking difficult and inaccurate. In this paper, we propose a method for accurately tracking small objects in satellite videos. In the initial phase, we applied the Fast Super-Resolution Convolutional Neural Network (FSRCNN), a deep learning-based super-resolution technique, to enhance the resolution of the frames. This procedure serves to sharpen the details and features of the objects, resulting in a clearer representation. The main contribution, however, lies in our incorporation of a robust motion detection technique into our framework, marking a significant step forward in this study. The motion detection approach combines various methods and operations with the primary goal of making moving objects distinctly visible and easily distinguishable from the complex image background. This enhancement significantly contributes to the precision and accuracy of the tracking system. We combined both approaches with Fully Convolutional Siamese Network (SiamFC) due to its robustness in tracking processes across frames in videos, capacity to handle changes in target appearance, and ability to deal with small datasets. The Satellite Video Single Object Tracking (SatSOT) and SV248A10-SOT (SV248S), two satellite video single object tracking datasets, were used to evaluate the proposed approach. When the techniques were applied, the SatSOT dataset showed an 11% precision score increase and a 10% success rate improvement. The SV248S dataset exhibited even more significant improvements, with precision scores increasing by 18% and success scores rising by 9%. © 2024 IEEE.

关键词： Satellites

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A Multistage Cascaded Neural Network Model to Identify the Severity of Epileptic Attack

A Multistage Cascaded Neural Network Model to Identify the S...

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2024 IEEE International Conference on Signal Processing, informatics, Communication and Energy Systems, SPICES 2024

作者： Simon, Martha Thampi, Lidiya Lilly Dept. of Computer Science College of Engineering Trivandrum India Indian Institute of Information Technology Kottayam Dept. of Computer Science Kottayam India

ISBN: (纸本)9798350376135

Epilepsy is a neurological disorder characterized by recurrent seizures, impacting the quality of life for millions of individuals worldwide. Timely and accurate detection of epileptic seizures is crucial for proper diagnosis and effective treatment. Electroencephalogram (EEG) signals provide vital information about brain activity and serve as a valuable tool for epilepsy detection. The methodology opted for this study was an enhanced multilayer feed forward neural network for automatic detection of epilepsy from EEG signals and hence predicting the severity. The authors build a multistage cascaded neural network model for achieving the same. Initially a Simple Artificial Neural Network (SimpleANN) is designed to classify EEG segments into seizure, healthy and interictal. Later a Multilayer Feed Forward Neural Network (MFFNN) is employed for detecting the severity of epileptic patients and hence can be classified into primary and secondary stages. The experimental results showed that the proposed SimpleANN classifier had provided noticeable results, and outperforms all tested classifiers. The study involves the collection of EEG data from a diverse group of patients, including those with epilepsy and healthy individuals. This automated epilepsy identifying system passes through different stages including decomposition, feature extraction for achieving a better performance model. The proposed system was tested and compared with SVM, KNN, MLP and Decision Tree under different measurement metrics. ANN has managed to give an accuracy of 80 - 90% which is significantly higher as compared to other machine learning models. The proposed work can automatically learn complex patterns from EEG data provides an advantage in identifying subtle seizure related features that may not be evident through manual analysis. Furthermore, the model offers real-time capabilities, making it suitable for continuous monitoring of patients at risk of seizures. © 2024 IEEE.

关键词： Electroencephalography

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Enhancing Domain-Specific ASR Performance Using Finetuning and Zero-Shot Prompting: A Study in the Medical Domain 6

Enhancing Domain-Specific ASR Performance Using Finetuning a...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Maulik, Utsav Bandyopadhyay Mitra, Pabitra Sarkar, Sudeshna Dept. of Computer Science and Engineering IIT Kharagpur West Bengal India

Domain Adaptation has emerged as an important development in Speech Recognition systems for improving the transcription accuracy of the input audio. This study explores the enhancement of Domain-specific Automatic Speech Recognition performance through finetuning and postprocessing using Large Language Models, focusing specifically on the medical domain. We investigate how domain-specific finetuning and advanced text postprocessing techniques can significantly improve transcription accuracy in medical contexts, reducing errors in specialized terminology, acronyms, and abbreviations. Our findings highlight the benefits of integrating Large Language Model based postprocessing with Automatic Speech Recognition systems to achieve better results in complex domains. © 2025 Copyright for this paper by its authors.

关键词： Speech recognition

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Plant Disease Detection for Guava and Mango using YOLO and Faster R-CNN 2

Plant Disease Detection for Guava and Mango using YOLO and F...

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

作者： Shetty, Kruthi U. Javed Kutty, Rida Donthi, Khushi Patil, Anuj Subramanyam, Natarajan Pes University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360523

Traditional methods of plant disease detection are cumbersome and prone to errors that cannot be avoided. Plant disease detection can help prevent crop losses and ensure food security. The system employs state-of-the-art deep learning techniques to automatically detect and classify plant disease in agricultural fields. The proposed system addresses the challenges associated with identifying plant diseases, which often have similar visual characteristics, by using a combination of deep learning and computer vision methods, specifically employing the YOLO (You Only Look Once) and Faster Region-Convolutional Neural Network with a ResNet-152 backbone. The system is trained on a custom dataset where the images were captured in an uncontrolled environment, specifically focusing on two plant leaves, namely Guava and Mango, to achieve high accuracy in identifying diseases in real-world field conditions. The proposed system has the potential to revolutionize plant disease detection and ensure food security. © 2024 IEEE.

关键词： computer vision

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Dynamic Task Scheduling for Load Balancing in Cloud Environments to Enhance Resource Allocation and Performance Efficiency

Dynamic Task Scheduling for Load Balancing in Cloud Environm...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Khan, Mohammad Imran Sharma, Kapil Amity University Dept. of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350319019

Effective resource distribution and speed efficiency are key to giving people the best service in cloud computer settings. Dynamic task ordering is a key part of reaching these goals because it uses available resources to easily distribute jobs while balancing the load. Within this paper, we suggest a new way to handle dynamic task scheduling that will help make cloud settings better at allocating resources and optimizing speed. The suggested approach uses a dynamic scheduling program that considers things like available resources, job needs, and present system load to make smart schedule choices. With dynamic task assignment, the system can change to changing job conditions in real time, making better use of resources and reducing response times. The suggested method includes load balance, which spreads jobs fairly among available resources so that computer or server doesn't get too busy. A fair job division keeps the system running smoothly, even during times of high usage. To make the best use of resources and reduce energy use, the suggested method also includes techniques like virtual machine consolidation and transfer. There is better resource usage and energy efficiency in the system because tasks are centralized on fewer actual computers and resources are allocated based on job demand. Based on results from experiments, the suggested method is better at allocating resources and improving performance compared to standard scheduling methods. In terms of shorter reaction times, higher output, and better use of resources, the results show big gains. © 2024 IEEE.

关键词： Resource allocation

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Smart Plant Watering System for Advanced Irrigation 2

Smart Plant Watering System for Advanced Irrigation

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Pujitha, Pasupuleti Sai Bhoomika, H.R. Saideep, Konduri Rahul Reddy, T. Banshal, Sumit Kumar Karmakar, Mousumi Shekhar, R. Dept. of Computer Science & Engineering Alliance University Bangalore India

ISBN: (纸本)9798350383867

The Smart Plant Watering System is an Internet of Things (IoT) solution for automated plant irrigation. It combines an ESP8266 Wi-Fi module for real-time monitoring with soil moisture and DHT11 sensors for data collection. It makes use of tools such as Blynk and Thing Speak to facilitate data visualization and remote control. An online crop recommendation systems using ML approaches and written in Python makes recommendations for the best crops to plant depending on the analysis of sensor data. With its ability to maximize irrigation, reduce water use, and improve crop management, this system represents a major breakthrough in precision agriculture for increased productivity and environmental sustainability. © 2024 IEEE.

关键词： Soil moisture

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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A review on 3D image reconstruction on specific and generic objects

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Materials Today: Proceedings 2023年 80卷 2400-2405页

作者： Shalma, H. Selvaraj, P. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Information Technology SRM Institute of Science and Technology Kattankulathur Chennai India

With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, dept. Estimation methods, Multi View Representations and the Data Representation Techniques. © 2021

关键词： Object recognition

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A Framework to Find Single Language Version Using Pattern Analysis in Mixed Script Queries 2

A Framework to Find Single Language Version Using Pattern An...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chaudhary, Anu Shekhar, Shashi Gla University Dept. of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350371055

Pattern analysis for converting a complete mixed code into a single language after determining confidence levels is a systematic approach crucial for applications requiring language uniformity. The process begins by tokenizing the mixed code into individual words or segments and employing a language identification algorithm to assign potential languages with associated confidence scores to each segment. Establishment of a confidence threshold allows for the reliable selection of dominant languages within the mixed code. Segmentation based on confidence levels and the application of a majority language rule ensures the prioritization of the most reliable language segments. In cases where confidence falls below the threshold or ambiguity arises, a fallback mechanism can be implemented, offering options like defaulting to a predefined language or flagging for manual review. The conversion strategy is then formulated based on the identified dominant language, utilizing tools such as machine translation services or language-specific processing libraries. The final output is a unified, single-language version of the mixed code, providing consistency and reliability in diverse linguistic contexts. The bulk of the words are Hindi, it has been noted after handing to the model. This is the reason these mixed codes need to be translated into majority language script. Make a table of replacements to swap out the unclear acronym with the matching term. There are 79 words in all, and 1 of those words are erroneous;thus, the correctness of the work is (78 / 79) × 100=98.73 %, and the mistake rate is (1/79)× 100=1.27%. © 2024 IEEE.

关键词： Dominant language Pattern analysis Segmentation Single language version

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