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Sarcasm and Humor Detection in Code-Mixed Hindi Data: A Survey

Sarcasm and Humor Detection in Code-Mixed Hindi Data: A Su...

引用

International Conference on Computing and Machine Learning, CML 2024

作者： Ganganwar, Vaishali Manvainder Singh, Mohit Patil, Priyank Joshi, Saurabh Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (纸本)9789819765874

Sarcasm and humor play vital roles in natural language, facilitating human expression and social interaction. In code-mixed languages like Hindi, where multiple languages intertwine, detecting humor and sarcasm poses unique challenges for researchers and developers. This paper provides a comprehensive survey of sarcasm and humor detection, focusing on code-mixed Hindi and English data. It explores linguistic complexities, emphasizing diverse features contributing to humor and sarcasm. Machine learning and deep learning techniques are examined, highlighting their relevance in code-mixed data contexts. The development of a code-mixed Hindi humor and sarcasm dataset is discussed for training and evaluating detection models. The paper critically analyzes existing research, experimental results, and practical implications, addressing challenges and future directions. It aims to bridge the gap between evolving code-mixed languages and the need for accurate humor and sarcasm detection. Further exploration and advancement in this domain are encouraged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Using Website Content for Detecting Phishing URLs: A Novel Approach 1

引用

2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Ganganwar, Vaishali Hussain, Syed Iarshed Powar, Ankur Gaur, Shushant Kumar, Amit Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

Phishing attacks have become a rampant cybercrime, where hackers use deceptive methods to trick individuals into sharing their personal information. However, with the advent of AI models like ChatGPT, detecting phishing URLs accurately has become increasingly challenging for traditional machine learning techniques due to the creation of fake content by hackers. To address this issue, we propose a new feature called "human-content" that helps in differentiating legitimate and phishing websites based on human-generated content on a website. To classify phishing URLs, we employed various features, such as domain-based, JavaScript-based, and lexical-based features, along with the novel "human-content" feature. To perform the classification, we used five machine learning classifiers, including Gradient Boosting Classifier, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression. Our experimental results on a dataset consisting of both legitimate URLs obtained from Alexa and phishing URLs from PhishTank demonstrate that the Gradient Boosting Classifier has the best performance, achieving an accuracy of 95.8%. Our proposed approach provides promising results in detecting phishing URLs and is more robust to AI-generated fake content. As phishing attacks continue to become more sophisticated, our method can help enhance online user security by effectively detecting phishing URLs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Websites

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Noval Approach using Image Detection to Identify Diseases in Fruit Plants (Apple) 9

Noval Approach using Image Detection to Identify Diseases in...

引用

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

作者： Kadam, Anup Dwivedi, Anand Prakash Shekhar, Amatya Sree, Dasari Hema Prajapat, Avnish Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

In the face of the critical imperative to feed a projected global population of 9 billion people by 2050, India, harboring one of the world's largest populations, grapples with formidable challenges in food production, exacerbated by substantial crop losses. An alarming statistic reveals that approximately 30 percent of India's agricultural yield succumbs to various factors, including pests, diseases, and post-harvest inefficiencies. This staggering loss not only poses a severe threat to India's food security but also contributes significantly to the broader global food crisis. Traditional solutions, often reliant on chemical pesticides, not only fall short in effectively addressing these challenges but also incur adverse environmental impacts, further exacerbating the sustainability dilemma. In response to this pressing issue, a groundbreaking research project emerges, advocating for innovative approaches that seamlessly integrate advanced technology, data-driven insights, and inclusivity to forge a resilient and sustainable agricultural ecosystem. This transformative initiative specifically targets the identification of plant diseases in apple trees, recognizing the potential ripple effects on broader agricultural practices. By leveraging cutting-edge technology like CNN and CAE the research introduces a comprehensive methodology that not only efficiently identifies and manages plant diseases but also holds the promise of sustainable crop management and economic stability. © 2024 IEEE.

关键词： Crops

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CamoVeil: Digital Camouflage Pattern Generation System 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Ganganwar, Vaishali Rajput, Parshant Bhavana, N. Sinha, Swapnil Vaid, Aashna Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Camouflage is the art of concealing. It is widely used to conceal your own self and your equipment from the enemy. It had been in nature since before humans, animals have used it to adapt to their surroundings. Camouflage is rooted in nature, and can be seen in animals to conceal them from predators. Its widespread application for military tactics only started with British zoologists. Camouflage now has come to play a crucial role in various domains with applications in military, hunting, and wildlife observations. The traditional methods of generating camouflage patterns that are generated manually are time-consuming and pose significant challenges in achieving optimal cover for different environments. In order to solve the issues and limitations, we in this paper have proposed an automated method to generate camouflage patterns tailored for specific terrains. We utilize image detection, and color extraction and produce pixelated patterns. The entire process involves segmentation, object detection, dominant color extraction, and pattern generation using K-means++. The CamoGen program is then employed to generate camouflage patterns by covering the canvas with polygons of respective colors and pixelating them into block-like patterns. The proposed method offers flexibility by allowing the generation of multiple patterns through parameter adjustments. Validation techniques are also integrated to ensure the desired areas are concealed. By digitizing and automating the camouflage pattern generation process, our model represents a significant advancement in the field and has potential applications in stealth monitoring and military exercises. Experimental results demonstrate the efficiency and effectiveness of our approach, particularly in natural environments such as forests, grasslands, and deserts. Further enhancements are required for designing camouflage patterns in artificial environments like urban settings. By digitizing and automating the camouflage pattern gen

关键词： Military applications

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AeroDetect: Real-Time Small Object Detection in Aerial Images 2nd

AeroDetect: Real-Time Small Object Detection in Aerial Imag...

引用

2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Singhal, Nikita Pandey, Anuranjan Kumar Kumar, Ankit Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (纸本)9789819995233

Real-time small object detection in aerial images is a challenging task in computer vision because of the complex environmental conditions and fewer pixels occupied by the objects. Along with this getting real-time inference makes this much more difficult. To tackle these challenges we propose an improvement in the classical YOLOv3 network which is capable of removing noise from the images and performing small object detection to while maintaining a high frame frequency. We are using a drone to capture real-time footage from the AIT campus, which is further used for classifying objects like vehicles, pedestrians, etc. Our proposed model "AeroDetect" was able to beat various state-of-the-art models like YOLOv3 and YOLOv5 in various parameters by having higher mAP, and a lesser number of layers and parameters while maintaining a high frame rate which is crucial for our use case. We achieved mAP of 0.402 on the Visdrone dataset as compared to 0.4 and 0.38 by YOLOv3 and YOLOv5, respectively. On the VEDAI dataset, we got mAP of 0.632, whereas YOLOv3 and YOLOv5 achieved 0.578 and 0.604, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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AI-Generated Video Forgery Detection and Authentication 9

AI-Generated Video Forgery Detection and Authentication

引用

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

作者： Tiwari, Ayush Kumar Sharma, Aman Rayakar, Poonam Bhavriya, Manish Kumar Nisha Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

Deep learning has a variety of uses and issues it can solve in the real world, but it also has some limitations. The growing use of AI-Morped Videos is one of the most recent and complicated issues. "AI Morphed Videos,"which are digitally manipulated still or moving visuals, are made using deep learning techniques. In an AI Morphed Video, the target's face is superimposed over the original image so that the altered digital data can be used for online frauds, extortion, pornography, etc. It is getting harder and harder to manually discern between true and false as deep learning develops. Therefore, research and development in the field of AI Morphed Video detection are crucial. An overview of the several AI Morphed Video detection strategies is completed in time for the classification of feature-based, temporal-based, and deep feature-based AI Morphed Video detection. The comparison research is based mostly on the key features used, including the face detection architecture, the deep learning architecture, whether it is video-based or image-based, the dataset used, the frames size, and the dataset size used. Along with the comparison, a semi-supervised GAN architecture is also proposed and built to recognize the AI Morphed Video. © 2024 IEEE.

关键词： Generative adversarial networks

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Real-Time Network Monitoring and Reporting Using Network Intrusion Detection System 9

Real-Time Network Monitoring and Reporting Using Network Int...

引用

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

作者： Lonare, M.B. Joshi, Bhuvan Chandra Tripathy, Satyam Kumar Kumar, Shivam Tiwari, Shivanshu Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394474

The increased participation in digital networks for communication, commerce, and critical infrastructure, calls for a robust Network Intrusion Detection System. This paper systematically examines the current landscape of NIDS, by analyzing the methodologies, algorithms, and technologies used in finding different forms of network threats. The paper begins by presenting the fundamental principle of the Intrusion Detection System and the evolving threat scenario and the benefits of using ML and Deep Learning when employed in this use case. We also dive into the categorization of NIDS into Signature-Based, anomaly-based, and Hybrid approaches. Evaluating each category's strengths and weaknesses. The paper aims to provide a comprehensive discussion and comparison of the various techniques proposed like MCF-MVO-ANN, an Intrusion detection system based on scalable K-means and Random Forest, etc. By synthesizing current knowledge this paper aims to serve as a valuable resource for researchers, practitioners, and decision-makers in the field of cybersecurity. © 2024 IEEE.

关键词： Network security

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Predictive Insights into Electric Vehicle Market Dynamics: An Exploration through Machine Learning 4

Predictive Insights into Electric Vehicle Market Dynamics: A...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Aaditya, S. B. Abrish Kurmi, Aditya Karandikar, P.B. Army Institute of Technology Department of Electronics and Telecommunication Engineering Pune India Army Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350354218

The burgeoning electric vehicle (EV) sector epitomizes a crucial transition towards more sustainable modes of transportation, underscored by the global imperative to mitigate carbon emissions. However, despite the technological strides made in this arena, the pace of EV adoption is tempered by a confluence of factors, including but not limited to the initial purchase cost, the driving range per charge, and prevailing consumer perceptions surrounding EV utility and feasibility. This investigation harnesses the analytical prowess of machine learning (ML) methodologies to delve into the nuanced dynamics of the EV market. By examining a comprehensive array of EV attributes, this study endeavors to distill the determinants that significantly sway consumer adoption patterns. Preliminary findings underscore the paramount influence of variables such as top speed, operational range, and rapid charging capability on the EV's market price, delineated by their pronounced positive correlations. These insights not only illuminate the attributes that consumers value most but also furnish automobile manufacturers with the empirical evidence needed to tailor their development and marketing strategies, thereby catalyzing the transition towards electric mobility. © 2024 IEEE.

关键词： Self-supervised learning

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Frame Acceleration and Super Resolution for video Enhancement Using GAN 8

Frame Acceleration and Super Resolution for video Enhancemen...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Lokhande, Sharayu Maity, Ankush Lenka, Sourabh Kumar Pious, Roshan Choudhary, Vishal Pawar, Shilpa Army Institute of Technology Dept. of Computer Engineering Pune India Army Institute of Technology Dept. of Electronics & Telecommunication Pune India

ISBN: (纸本)9798350391770

Low-resolution (LR) videos are common in various applications, including surveillance and video conferencing, primarily due to bandwidth constraints or older recording technologies. This research introduces a novel approach for enhancing the quality of LR videos by leveraging Generative Adversarial Networks (GANs) equipped with MeanShift loss, perceptual loss, Total Variation (TV) Loss, and a modified VGG19 architecture. By processing each frame of an LR video through our model, we achieve significant improvements in visual quality, detail sharpness, and temporal consistency, resulting in high-resolution (HR) videos that are both visually pleasing and realistic. The conventional approach to video enhancement, particularly when employing deep learning models like GANs, involves processing high volumes of data, which can be computationally intensive and time-consuming. This challenge is compounded in real-time applications or environments lacking powerful graphical processing units (GPUs). Our research proposes a novel solution by extracting frames from low-resolution (LR) videos, enhancing each frame individually, and then recombining them into a high-resolution (HR) video. This method offers a significant reduction in computational requirements, making high-quality video enhancement accessible on less capable hardware and suitable for real-time processing. © 2024 IEEE.

关键词： Generative adversarial networks

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Recommendation System for Research Grants 7

Recommendation System for Research Grants

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Lokhande, Sharayu Tholia, Rakesh Upadhyay, Mukul Choudhary, Sachin Rathore, Kuldeep Pawar, Shilpa Army Institute of Technology Pune Dept. of Computer Engineering Pune India Army Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

This research paper proposes the design and implementation of a recommendation system for researchers to simplify the process of finding research grants. Despite the positive impact that researchers and scientists have on the world through their discoveries and innovations, securing funding for their projects remains a challenging and time-consuming task. This paper objects to address the issue by introducing a recommendation system that leverages the architecture of the system, detailed design, project planning, and requirements to suggest grants that researchers may find interesting based on their platform activities. The proposed system will be beneficial for researchers, students, and scientists seeking grants, as it will provide an efficient and user- friendly way of finding grants that fit their interests and project requirements. © 2023 IEEE.

关键词： decision trees GBDT

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