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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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Multispectral Satellite Data Classification Using Rank Correlation Similarity Index and Automatic Labeling Technique

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SN computer science 2024年第8期5卷 1-20页

作者： Ain, Arindom Gogoi, Minakshi Chutia, Dibyajyoti Dept. of Computer Science and Engineering GIMT Under Assam Science and Technology University Assam Guwahati India Dept. of Computer Science and Engineering Girijananda Chowdhury University Assam Guwahati India NESAC Umium Shillong India

Multispectral satellite image classification is crucial in remote sensing and image analysis, serving various applications like water area extraction, urban region classification, and vegetation monitoring. Model-based classification, particularly supervised learning approaches, heavily relies on manually annotated training samples, which are labor-intensive and subject to human bias. However, solely depending on features derived from individual pixels may fail to capture spatial contextual information effectively and manage mixed pixels and various types of noise, resulting in classification inaccuracies. To address these challenges, this research proposes a novel unsupervised learning approach and automatic labeling technique (ALT). This approach utilizes collective information from groups of pixels instead of relying solely on individual pixels for identification and extraction of meaningful unique patterns which serves as the most significant factor for satellite image classification. A new similarity index, the patch similarity index (PSI), is generated by incorporating the rank correlation similarity index (RCSI). The PSI is used to determine the similarity factor both between the patches and within the patch. Subsequently, a clustering algorithm is employed to group pixels into four distinct classes: vegetation, water, built-ups, and open land. Additionally, ALT streamlines the post-classification process by reducing the need for manual intervention in mapping clusters to the actual class label. The proposed method offers a more accurate and reliable unsupervised classification for multispectral satellite imagery, promising enhanced efficiency and reduced manual labor. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automatic labeling technique Frequent masking Multispectral Patch similarity index Rank Correlation

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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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Enhancing Movie Recommendation Efficiency Using Ensemble Learning Techniques

Enhancing Movie Recommendation Efficiency Using Ensemble Lea...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Nitasha Pandey, Aniket Kumar Yadav, Ankit Mishra, Subrat Medhanshi Chandigarh University Dept. of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350376470

This research paper explores the 'Movie Dataset' to create a better movie recommendation. The data includes many features such as genre, ratings, players, and user preferences. Our main goal is to find the best combination model combining SVD (Singular Value Decomposition), SVD++ (SVD with Implicit Feedback) and KNN Baseline (K Nearest Neighbors with Baseline) recommendation algorithms to improve prediction accuracy and recommendations. The rapid expansion of digital media platforms has created a lot of information in the entertainment industry, making movie recommendations very important for the development of consumers. Through our research, we focus on the development of personalized movie recommendations and user satisfaction in the field of digital entertainment. © 2024 IEEE.

关键词： Collaborative filtering

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Poster Abstract: SenseEMS - Towards A Hand Activity Recognition and Monitoring System for Emergency Medical Services 22

Poster Abstract: SenseEMS - Towards A Hand Activity Recognit...

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22nd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2023

作者： Rahman, M Arif Weerasinghe, Keshara Wijayasingha, Lahiru Alemzadeh, Homa Williams, Ronald D. Stankovic, John Dept. of Computer Science University of Virginia Charlottesville United States Dept. of Electrical and Computer Engineering University of Virginia Charlottesville United States

ISBN: (纸本)9798400701184

Emergency Medical Services (EMS) providers use their hands extensively for the rescue operation and providing care to the patients in an EMS scene. Using smartwatch based sensor data, i.e., accelerometer, gyroscope, and magnetometer, we are developing SenseEMS, a system for hand operated EMS intervention detection and real-time monitoring. SenseEMS will use a hybrid deep neural network with appropriate real-time algorithms on the sensor data to detect multiple hand operated activities, i.e. CPR compressions, attaching defibrillation pads and breathing bags, and to provide quality assessment on different metrics of the activity, i.e., the rate and dept. of CPR compressions. Our initial results for this ongoing research show promising accuracy. Preliminary survey with 31 anonymous EMS responders suggests that this automated system will be highly beneficial for real-scene application and EMS training. © 2023 Owner/Author.

关键词： Sensor networks

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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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Design and Development of Expense App

Design and Development of Expense App

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Girdhar, Gunit Kumar, Sachin Bhardwaj, Anuj Sharma, Megha Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350388800

With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add location, upload more data, choose the amount of expense, and more. Additionally, the relational database will save this data. Users have the option to see and sort spending weekly, monthly, or annually. We may track the spending and minimize the amount of human computations for their costs by employing this. This allows a user to enter their income to get an estimate of their daily costs, and the results are saved for each individual user. Individuals who frequently go on excursions or to the theatre with friends might use this tracker to keep tabs on their spending. They will find it simple to split the bill using this tracker. This will show graphs according to the chosen perspective. Additionally, a user may write in this TR his monthly income or monthly expenditure cap. An integrated feature set is offered by this tracker system to assist you in managing your spending and cash flow. © 2024 IEEE.

关键词： Cost benefit analysis

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RT-MDM: Real-Time Scheduling Framework for Multi-DNN on MCU Using External Memory 24

RT-MDM: Real-Time Scheduling Framework for Multi-DNN on MCU ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Kang, Sukmin Lee, Seongtae Koo, Hyunwoo Chwa, Hoon Sung Lee, Jinkyu Dept. of Semiconductor and Display Engineering Sungkyunkwan University Korea Republic of Samsung Electronics Co. Ltd. Korea Republic of Dept. of Computer Science and Engineering Sungkyunkwan University Korea Republic of School of Electronic and Electrical Engineering Sungkyunkwan University Korea Republic of Dept. of Electrical Engineering and Computer Science Dgist Korea Republic of

ISBN: (纸本)9798400706011

As the application scope of DNNs executed on microcontroller units (MCUs) extends to time-critical systems, it becomes important to ensure timing guarantees for increasing demand of DNN inferences. To this end, this paper proposes RT-MDM, the first RealTime scheduling framework for Multiple DNN tasks executed on an MCU using external memory. Identifying execution-order dependencies among segmented DNN models and memory requirements for parallel execution subject to the dependencies, we propose (i) a segment-group-based memory management policy that achieves isolated memory usage within a segment group and sharded memory usage across different segment groups, and (ii) an intra-task scheduler specialized for the proposed policy. Implementing RT-MDM on an actual system and optimizing its parameters for DNN segmentation and segment-group mapping, we demonstrate the effectiveness of RT-MDM in accommodating more DNN tasks while providing their timing guarantees. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory management

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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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Integrating Deep Learning Techniques for Enhanced Cyclone Prediction and its Impact on Air Quality Index Utilizing Satellite Imagery

Integrating Deep Learning Techniques for Enhanced Cyclone Pr...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Vijayakumar, Sushruth Jamadhiar, Sonakshi Raj, Srujan M Mr, Vishnudeep Saritha, K. PES University Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350374711

Tropical cyclones (TCs greatly impact ecosystems and weather patterns, often leading to catastrophic outcomes this research explores dynamic relationship between the Air Quality Index (AQI) and Tropical Cyclones (TCs), examining changes in air quality during different stages of TCs. Focusing on Cyclone Biparjoy's impact on India's coastline in June 2023, we uncover its influence on air quality. The study shows good predictive performance by utilizing meteorological data, satellite observations, and machine learning algorithms such as CNN and ConvLSTM. Results show promising outcomes, with CNN achieving an RMSE of about 12.03 and an MAE of approximately 8.523 for TC intensity prediction, and ConvLSTM producing an RMSE of approximately 0.22 and a loss of around 0.04 for TC track prediction. Additionally, regression techniques using meteorological data from Tuetempo for AQI prediction were employed. The ExtraTrees Regression model exhibited strong predictive performance with an RMSE of approximately 19.627, while the Xgboost model had a slightly higher RMSE of around 21.72 our dataset included meteorological data from Tuetempo website for AQI forecasts and datasets like Hursat and INSAT 3D satellite photos for TC-related data, this work offers important insights into the dynamics of air quality during TC events. © 2024 IEEE.

关键词： Tropical cyclone

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