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Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study 2

Effectiveness and Influence of Parts of Speech like adjectiv...

引用

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

作者： Adarsh, M.J. Ravikumar, Pushpa Karnataka Chikkamagaluru India Dept. of Computer Science and Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (纸本)9798350383867

In contemporary text analytics, examining the feelings in customer evaluations has taken centre stage. It is difficult to identify emotions in sentences written with irony. Social media portals and e-commerce portals are offering users to come up with comments and reviews with realistic funny emotions. As a way to identify the feelings expressed in sentences written in a mocking tone purpose, we have attempted to highlight the Adjectives function in this study by taking into account positive and negative adjectives as well as an interjection, both good and bad words. We propose the Adjective-based Sarcasm Detection Algorithm (ABSA) and the Interjection-based Sarcasm Detection Algorithm (IBSA) based on lexicon approach as machine learning approaches won't yield better results in detection of irony and have compared it with the existing approach with more sarcastic sentences. Our F score results for ABSA and IBSA are 0.86 and 0.91, respectively. This paper sheds light on investigating humour or irony in context of parts of speech (POS) words. ©2024 IEEE.

关键词： Social networking (online)

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Utilizing SVM, CNN to predict Alzheimer's disease

Utilizing SVM, CNN to predict Alzheimer's disease

引用

2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Bharath, Peddi Jaswanth, Pillella Pranav, P.N.S.S. Voleti, Chandana Shamshita Anitha, V. B V Raju Institute of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350394399

This abstract outlines a bioinformatics research endeavor focused on unraveling the intricate relationship between genes and age-related diseases, with a specific emphasis on Alzheimer's disease. The primary methodology involves the development of a classification model aimed at categorizing each gene, a fundamental step in understanding the genetic basis of diseases. To achieve this, this paper employed two distinct machine learning architectures: Support Vector Machine (SVM) and Convolutional Neural Network (CNN), both renowned for their efficacy in classification tasks. Prior to inputting data into these models, Principal Component Analysis (PCA) is utilized for feature extraction, a technique aimed at simplifying the dataset while retaining crucial information. This research endeavors to contribute to our understanding of Alzheimer's disease by leveraging advanced machine learning techniques to decipher the intricate genetic signatures associated with this debilitating condition. © 2024 IEEE.

关键词： Support vector machines

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Topic Modeling Based Clustering of Disaster Tweets Using BERTopic

Topic Modeling Based Clustering of Disaster Tweets Using BER...

引用

2024 MIT Art, Design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Rachel J, Julanta Leela Bhuvaneswari, A. Kumudha, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India T.John Institute of Technology Dept. of Information Science and Engineering Bangalore India

ISBN: (纸本)9798350362879

The essence of catastrophe management is in the capacity to promptly and effectively addressing unforeseen circumstances, as well as to implement premeditated and flexible strategies in accordance with established protocols and norms. Twitter has become a prominent means of disaster communication, particularly in nations where it has a substantial presence. Twitter can provide valuable information regarding the effects of infrastructure on emergency management in the event of significant disasters. However, the process of sifting through numerous unrelated tweets can be laborious and time-consuming. Prior research has successfully recognized the various categories of communications present on social media platforms during times of disaster. However, there is a dearth of recommended methodologies aimed at effectively extracting valuable information from these messages. In this study, we propose a framework that may be efficiently utilized to promptly deliver disaster effect information derived from social media sources. The methodology involves the application of certain techniques to process the unprocessed Twitter data by employing keyword filtering, location-based filtering, and analysis of tweet properties. Subsequently, Bidirectional Encoder Representations from Transformers (BERT) technique is utilized to categorize the tweets originating from disaster-Affected regions into distinct subjects that are relevant and valuable for emergency management. BERT analysis yielded a total of 376 themes within the dataset. Among these topics, a significant proportion of 196 were found to be closely associated with the impacts of disasters. These impacts encompassed many aspects such as outages, closures, flooded roadways, and damaged infrastructure. © 2024 IEEE.

关键词： Disasters

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TrustCaller- Voice-based Fraud Prevention System 4

TrustCaller- Voice-based Fraud Prevention System

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Sonwane, Rushikesh Patil, Mansi Chauhan, Purva Jain, Anushka Nandwalkar, Bhushan Awate, Ashish Shahade, Makarand SVKM's Institute of Technology Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350349900

An innovative project called 'TrustCaller' aims to improve call security. With the goal of establishing caller verification and building trust in incoming calls, the project makes use of state-of-the-art technologies for voice feature extraction, processing, and pattern matching. Utilizing React Native only for the Android application, 'TrustCaller' assumes a key function in boosting the security of phone communications when combined with Machine Learning (ML) and Speech Synthesis. This program is a vital step in protecting people from possible dangers and promoting safer phone communication. Moreover, it provides scalable security settings to guarantee users' privacy and control over communication. © 2024 IEEE.

关键词： Secure communication

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Converging Diagnostic Paths: A Unified Approach to Acid-Base Imbalances in COPD Patients 2

Converging Diagnostic Paths: A Unified Approach to Acid-Base...

引用

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

作者： Kinikar, Atharva Shah, Manan Pune Institute of Computer Technology Dept. Information Technology Pune India Dwarkadas J Sanghvi Coe Dept. Computer Engineering Mumbai India

ISBN: (纸本)9798350360523

Chronic Obstructive Pulmonary Disease (COPD) significantly impacts patient quality of life and is a leading cause of morbidity and mortality. This paper introduces an innovative algorithm for diagnosing acid-base disorders in COPD patients, incorporating traditional and advanced methodologies like the Henderson-Hasselbalch equation and Bedside Stewart Method. The algorithm enhances the accuracy and speed of Arterial Blood Gas (ABG) analysis, crucial for COPD management. We present a comprehensive approach that includes a Gamblegram for visualizing acid-base balance and a sophisticated system architecture employing Optical Character Recognition (OCR) for efficient ABG report processing. Our findings demonstrate the algorithm's efficacy in providing rapid, detailed analysis, markedly reducing treatment initiation time. This advancement holds significant potential in revolutionizing COPD patient care by improving diagnostic accuracy and treatment strategies for acid-base disorders. © 2024 IEEE.

关键词： Gases

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Simplifying Digital Streaming: An Innovative Cross-Platform Streaming Solution 15

Simplifying Digital Streaming: An Innovative Cross-Platform ...

引用

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

作者： Muduli, Debendra Sahoo, Saroj Kumar Mishra, Jayanta Kumar Pradhan, Debasish Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Centurion University of Technology and Management Dept. of Computer Science Engineering India GITA Autonomous College Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Our research aims to investigate how reinforcement learning methods can be applied to develop a movie recommendation system. We frame the issue of interactive recommendation as a contextual multi-armed bandit, where the system learns user preferences by suggesting new movies and receiving feedback. Through reinforcement learning, we address challenges such as balancing exploitation and exploration and tackling the coldstart problem. Additionally, we incorporate the novelty factor of movies into our model. We evaluate both content-based and collaborative filtering approaches which achieved accuracy 72.33(%), both of which demonstrate effective recommendation outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Advancements in Animal Tracking: Assessing Deep Learning Algorithms 15

Advancements in Animal Tracking: Assessing Deep Learning Alg...

引用

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

作者： Akter, Tahmina Majumder, Avishek Mahmud, Tanjim Habib, Israt Binteh Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh International Islamic University Chittagong Dept. of Computer Science and Engineering Chittagong Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

In the rapidly advancing field of computer vision, object detection has become crucial for various applications, including animal tracking, face detection, and surveillance systems. This study investigates the efficacy of contemporary object detection methodologies by evaluating the performance of the You Only Look Once (YOLO) models and TensorFlow Model Zoo architectures for animal tracking. YOLO models, known for their ability to process entire images in real-time and predict bounding boxes and class probabilities simultaneously, offer significant advantages over traditional methods such as Convolutional Neural Networks (CNNs) and Fast R-CNNs. This paper compares the performance of YOLOv5 and YOLOv7, alongside TensorFlow-based models like Faster R-CNN ResNetv152 and SSD ResNet101, using a dataset of animal images. Our findings reveal that YOLOv5 outperforms other models with a mean average precision (mAP) of 9 7.5%, demonstrating superior accuracy and efficiency in object detection tasks. YOLOv7 also shows strong performance with an mAP of 96.7%, while TensorFlow Model Zoo's Faster R-CNN and SSD models lag behind with mAPs of 81.9% and 81.6%, respectively. The results highlight the significant advancements in deep learning and object detection algorithms, particularly the advantages of YOLO's architecture in handling complex detection tasks in real-world scenarios. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

引用

17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Graph Neural Network Guided Music Mashup Generation

Graph Neural Network Guided Music Mashup Generation

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model's performance. Generated mashups used for evaluation can be found in https://***/***/. © 2024 IEEE.

关键词： Graph neural networks

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms 15

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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

作者： Mahmud, Tanjim Akter, Tahmina Chakma, Rishita Barua, Tikle Hossain, Mohammad Shahadat Andersson, Karl Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati4500 Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå97187 Sweden

ISBN: (纸本)9798350370249

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling. ©2024 IEEE.

关键词： Adversarial machine learning

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