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A Comparative Exploration of Machine Learning and Graph Deep Learning Models for Discriminatory Speech Detection 2

A Comparative Exploration of Machine Learning and Graph Deep...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kavya, J. Pragathy Reddy, K.N. Maddipatla, Phanindra Kesanapalli, Anjali Arya, Arti PES University Dept. of Computer Science Engineering Karnataka Bengaluru560100 India

ISBN: (纸本)9798350371314

In an era marked by digital expansion, discriminatory speech remains a widespread concern, exerting significant societal implications. This study presents an overview of discriminatory speech detection techniques and explores the complexities involved in identifying discriminatory speech by utilizing a variety of datasets from various fields. The importance of preprocessing methods and feature engineering is next examined, opening the door for more in-dept. study. Furthermore, the paper delves into traditional Machine Learning (ML) algorithms and state-of-the-art Deep Learning (DL) models along with a crucial component of using graph-based structures with Natural Language Processing (NLP) techniques and the use of graphs to extract contextual information. Using both graphs and NLP algorithms enhances the identification process by uncovering contextual connections within textual data. © 2024 IEEE.

关键词： Speech recognition

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An Intelligent Approach to Grape Leaf Disease Diagnosis Through Machine Learning 2

An Intelligent Approach to Grape Leaf Disease Diagnosis Thro...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Uniyal, Sneha Dhoundiyal, Parveen Sharma, Vikrant Vats, Satvik Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

This study underscores the pressing necessity for early and accurate diagnosis of grape leaf diseases, advocating for an automated solution that harnesses the synergistic power of image processing and machine learning. Employing methodologies such as Naïve Bayes, Gradient Boosting, and Random Forest, the primary objective is to proficiently classify grape leaves as either healthy or afflicted by diseases. The model casts its focus on prevalent grape leaf maladies, including Grapes Healthy Leaf, Grape Black Rot,Grape Black Measles, Grape Isariopsis leaf. The process commences with the meticulous preparation of grape leaf images, followed by the extraction of crucial features, which serve as the foundation for the application of machine learning techniques. The viability of the grape crop sector hinges on the efficacy of this innovative approach, which has the potential to significantly curtail the spread of diseases in grape fields while enhancing the precision of disease identification. This study serves as a compelling demonstration of the formidable effectiveness of machine learning algorithms in the detection of grape leaf diseases, thus ushering in the prospects of automation and improved decision-making within the realm of agriculture. © 2024 IEEE.

关键词： Disease Identification Gradient Boosting Grape leaves Machine Learning Naïve Bayes Random Forest

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Enhanced Deep Learning-Based Malware Classification Using MobileNetV2 4

Enhanced Deep Learning-Based Malware Classification Using Mo...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Manasa, V.Gnana Sri Gopinath, M. Ram, M.Aditya Khan, Md.Sajid Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Kanuru India

ISBN: (纸本)9798331529635

In the rapidly evolving landscape of cybersecurity, the classification of malware has become increasingly critical due to the exponential growth in the volume of malware, which poses significant security threats to financial institutions, businesses, and individuals. Traditional signature-based detection methods have struggled to keep up with the growing variety and complexity of new threats, making it necessary to use more advanced techniques. Among these, deep learning has emerged as a promising approach for malware detection, particularly through the classification of malware images by transforming binary files into grayscale representations. While previous research has predominantly utilized deep learning architectures for this task. However, in this research work, we developed a model with Mobilenet to enhance classification accuracy. The results of this research demonstrate the revolutionary potential of deep learning-based methods for malware family classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Big data Application in Cancer Classification by Analysis of RNA-seq Gene Expression 3

Big Data Application in Cancer Classification by Analysis of...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Amruth, A. Ramanan, R. Paul, Rhea Vishal, S. Saravanan, S. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338607

computer science domain has got massive developments in the past decade, especially in the field of Big data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and services. Healthcare is one such industry which comes across voluminous data. Big data platform is used to process the data and subsequently AI models can be used for prediction or classification. This paper focuses on proposing a Spark based pipeline to reduce the time required to process the voluminous data. With the aid of this new pipeline, we classify the type of cancer through analysis of RNA-seq gene expression levels. The Spark-based pipeline is run on default standalone mode and on a cluster. The study recorded that the Spark-based pipeline run on cluster mode improved the performance approximately by 11 times (regarding processing costs) when compared to the performance of Python-based pipeline. © 2023 IEEE.

关键词： Diseases

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An Integrated System for Potato Leaf Disease Recognition 2

An Integrated System for Potato Leaf Disease Recognition

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rathore, Robin Dhoundiyal, Praveen Sharma, Vikrant Vats, Satvik Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

Since potatoes are a staple crop in India, there has been a noticeable growth in their cultivation. Given the significant nutritional and economic value of potatoes, this study addresses the significant issue of leaf diseases. The study enhances farmer lives and supports agricultural success in India by using machine learning techniques to categorize and detect diseases in real time. The major areas of study are image processing, feature extraction, and machine learning models like Random Forest, Naive Bayes, and K-Nearest Neighbors (KNN). The paper proposes a systematic framework that comprises steps for data collecting, picture preprocessing, feature extraction, training, and classification in order to reliably diagnose diseases. It also demonstrates how these procedures can be combined. The datasets used include approximately 5,000 labeled pictures of potato leaves that show early blight, late blight, and healthy leaves. Among the preprocessing methods are data augmentation, contrast enhancement, and image normalization. When it came to classifying diseases, the trained models fared extremely well;Naive Bayes, Random Forest, and KNN were especially accurate. The study demonstrates how integrating image processing and machine learning could aid in addressing potato leaf disease issues in agriculture.. © 2024 IEEE.

关键词： feature extraction image normalization Image processing K-Nearest Neighbors Machine Learning Naive Bayes Random Forest

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Visual Analytics of Streaming data in Concept Drift Detection 11

Visual Analytics of Streaming Data in Concept Drift Detectio...

引用

11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Ilic, Aleksandra Stojnev Stojanovic, Dragan University of Nis Faculty of Electronic engineering Dept. Computer Science Nis Serbia

ISBN: (纸本)9798350386998

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs. © 2024 IEEE.

关键词： Visual analytics

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A Study on Predicting Ratings System through Sentiment Analysis and Clustering of Coupang Product Reviews 13

A Study on Predicting Ratings System through Sentiment Analy...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Sung, Si-Yoon Jung, Jin-Woo Dongguk Univ. Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350373332

With the expansion of e-commerce platforms, online reviews have become increasingly important. Users often decide on purchases based on reviews and ratings. This study notes that reviews with identical ratings can differ in emotional content and hypothesizes that overall satisfaction can be inferred from these emotions. The aim is to develop a system to quantify satisfaction based on review content, especially when ratings are absent. This approach addresses situations where reviews lack ratings or users struggle to rate products despite providing feedback. We crawled product reviews from Coupang and performed sentiment analysis using a BERT model tailored for Korean. The sentiment analysis results were clustered using six different algorithms, and the outcomes were evaluated. The study explored the best methodology among 30 combinations of clustering algorithms and hyper-parameters. For evaluation, 'accuracy' and 'mean absolute error' were used as external metrics to compare with actual ratings, while the 'silhouette coefficient' was used as an internal metric to assess cluster clarity. Agglomerative Clustering showed the highest performance in external metrics but lower internal metric performance. Spectral Clustering demonstrated well-balanced and excellent performance across all metrics. © 2024 IEEE.

关键词： Sentiment analysis

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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks 2

Design and Optimization of High-Performance Health Monitorin...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mehta, Heena Singla, Mukesh Baba Mastnath University Dept. of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798331519056

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution. © 2024 IEEE.

关键词： Electronic health record

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Using AI and Blockchain in Case of Algorithm Regulation 2

Using AI and Blockchain in Case of Algorithm Regulation

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Tripathi, Neha Mishra, Amit Kumar Dept. of Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350320923

The implication of AI is determined by some basic applications where the important analysis is properly modulated as much as possible. All of the basic applications are determined by the growth of AI technologies for finding a better perspective appropriately. Fundamentally, there are different aspects based on the advantages of AI developed by the basic application on the organized basic application of the various variable perspective. In addition, all of the importance regarding the basic development basis is properly incremented on the functional approach. Examination of some basic applications to determine all of the appropriate resolutions has to be evaluated in this research article. © 2023 IEEE.

关键词： Machine learning

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Esteso: Interactive AI Music Duet Based on Player-Idiosyncratic Extended Double Bass Techniques

Esteso: Interactive AI Music Duet Based on Player-Idiosyncra...

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Stefani, Domenico Tomasetti, Matteo Angeloni, Filippo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy Italy

Extended playing techniques are a crucial characteristic of contemporary double bass practice. Players find their voice by developing a personal vocabulary of techniques through practice and experimentation. These player-idiosyncratic techniques are used in composition, performance, and improvisation. Today’s AI methods offer the opportunity to recognize such techniques and repurpose them in real-time, leading to new forms of interactions between musicians and machines. This paper is the result of a collaboration between a composer/double-bass player and researchers, born from the musician’s desire for an interactive improvisational experience with AI centered around the practice of his extended techniques. With this aim, we developed Esteso: an interactive improvisational system based on extended technique recognition, live electronics, and a timbre-transfer double-bass model. We evaluated our system with the musician with three duet improvisational sessions, each using different mapping strategies between the techniques and the sound of the virtual double bass counterpart. We collected qualitative data from the musician to gather insights about the three configurations and the corresponding improvisational duets, as well as investigate the resulting interactions. We provide a discussion about the outcomes of our analysis and draw more general design considerations. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： computer music

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