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A Comprehensive Survey of Deep Learning Models for Legal Document Summarization 15

A Comprehensive Survey of Deep Learning Models for Legal Doc...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Reddy, Kancharla Bharath Jayabharathy, J. Puducherry Technological University Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798331300579

Legal document Summarization poses a difficult challenge because of the complexity and diversity of legal language and *** recent years, deep learning models have shown promising results in summarizing legal *** article examines the challenges of legal document summarization, reviews the state-of-the-art deep learning models, and identifies research gaps in this *** future research directions and applications of legal document summarization using deep learning models are also *** paper is a valuable resource for researchers and practitioners interested in legal document summarization using deep learning models. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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Depression Detection Using Multimodal Analysis with Chatbot Support 2

Depression Detection Using Multimodal Analysis with Chatbot ...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sharma, Archana Saxena, Anuradha Kumar, Ashok Singh, Divyansh Affiliated to Aktu Lucknow Department of Computer Science and Engineering UP India

ISBN: (纸本)9798350371055

Depression, a widespread psychiatric disorder affecting people globally, spans all age groups, predominantly impacting adults. This bipolar disorder characterized by symptoms including pessimism, hopelessness, anhedonia, and sadness, significantly influences lives, contributing to depression. Our paper proposes a multi-model approach for depression detection, utilizing facial expression analysis, audio evaluation, and user text input through deep learning algorithms, alongside an intelligent chatbot for personalized support. This hybrid model integrates facial expressions, audio features, and textual input for a comprehensive approach to depression detection. The methodology includes four key objectives: a CNN model for real-time or pre-recorded video facial expression analysis, audio evaluation using an NLP algorithm to transcribe users' voices, text-based analysis uncovering linguistic patterns and emotional context, and Multimodal Fusion integrating outputs for a unified multimodal approach. The intelligent chatbot encourages users to share emotions openly, enhancing the system's accuracy in identifying individuals at risk of depression. Results demonstrate the fusion's contribution to early depression detection, enabling timely interventions and improving accuracy, efficiency, and overall performance. © 2024 IEEE.

关键词： anhedonia Depression influences Linguistic patterns Multimodal Analysis Pessimism Predominantly

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Transformer and Deep CNN-based Product Recommendation System 14

Transformer and Deep CNN-based Product Recommendation System

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Potti, Mahitha Chalumuri, Avinash Golagani, Vani Harini, D.N.D. Department of Computer Science and Engineering Visakhpatnam India

ISBN: (纸本)9798350335095

A product recommendation system is one of the crucial components of any E-commerce platform. Content-based filtering techniques effectively provide relevant product recommendations based on user actions on the E-commerce platform. However, with the rapid growth of products and product data, content-based filtering faces several challenges, particularly in feature extraction. Conventional feature extraction methods cannot capture the wide range of product characteristics that influence consumer preferences. In addition, as the number of features extracted increases, the computation time and memory requirements also increase, making it difficult to scale the system. In this paper, we propose the use of various deep learning techniques like conventional Natural Language Processing(NLP) methods, transformer-based methods, Convolutional Neural Networks (CNNs), and Autoencoders to develop 12 models for a recommendation system. Our work aims to identify products with similar textual descriptions and visual appearances to generate accurate recommendations. The 12 models are divided into three categories based on the type of feature extraction techniques used. The proposed approach uses self-attention to extract text features and powerful image feature extraction techniques to effectively capture the product characteristics and address the limitations of traditional methods. After extensive experimentation and evaluation through A/B testing, we observed that the best-performing model is based on a transformer, CNN, and autoencoder. The model generated accurate recommendations and achieved significantly higher user ratings, with an average of 9.9 out of 10 points. Also, the latency of the model is very low. © 2023 IEEE.

关键词： Feature extraction

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LW-ALDDNet: A Novel Lightweight CNN Model for Automated Detection of Apple Leaf Diseases 15

LW-ALDDNet: A Novel Lightweight CNN Model for Automated Dete...

引用

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

作者： Prito, Rizvee Hassan Hassan, Md. Bayzid Nuzhat, Zarin Tasnim Saha, Ratul Sadia, Umme Fatema Tuz Sounok, Sabbir Ahmed Fahim, Sajid Faysal Mim, Aisha Siddika Siam, Shams E Hussain, Syed Musayedul East West University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370249

Plant diseases cause substantial losses in crop production, which exerts a major impact on the economic development of agriculture. Early detection of these diseases is inevitable to prevent significant production losses. However, accurate identification is challenging due to similarities in diseases, characteristics. Therefore proper recognition of these diseases has become a challenging task for farmers. But with the help of artificial intelligence and deep learning models in IoT devices, anyone can now take advantage of correctly recognizing diseases of various crops. As the Apple is the most consumed fruit in the world, a novel lightweight CNN model is introduced in this study to accurately identify apple leaf disease from the images of apple tree leaves. Our proposed model is evaluated alongside various popular deep CNN models, including MobileNetV2, InceptionV3, ResNet50v2, AlexNet, SqueezeNet, etc. on two public datasets of five and nine categories of apple leaf diseases with healthy leaves. The evaluation results revealed that our proposed model achieved 91.65%, and 97.53% accuracies on datasets of nine and five classes, respectively, which exceeded the accuracies of six deep CNN models implied on those datasets. Moreover, being a lightweight CNN model, our designed model has the least number of parameters, proving that it can detect apple leaf diseases with better accuracy and with less storage, less computational resources, and shorter detection time requirements. © 2024 IEEE.

关键词： Fruits

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Understanding Omitted Facts in Transformer-based Abstractive Summarization 10

Understanding Omitted Facts in Transformer-based Abstractive...

引用

10th International Multidisciplinary engineering.Research Conference on Moratuwa engineering.Research, MERCon 2024

作者： Panawenna, Pasadie Hiranya Wickramanayake, Sandareka University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka

ISBN: (纸本)9798331529048

Text summarization is a natural language processing task that generates concise document summaries. It can be extractive or abstractive. The former extracts pieces of the document, while the latter generates new concise sentences after identifying the critical information from the input text. ive Summarization (AS) more closely represents how a human would summarize and is used in multiple mission-critical downstream tasks in domains such as law and finance. However, the existing state-of-the-art AS models are based on black-box deep learning models such as Transformers. Hence, the users of such systems cannot understand why some facts from the document have been included in the summary while some have been omitted. This paper proposes an algorithm to explain which facts have been omitted and why in Transformer-based AS. We leverage the Cross-Attention (CA) in transformers to identify words in the input passage with minimum influence in generating the summary. These identified words are then given to a Large Language Model along with the input passage and the generated summary to explain the omitted facts and the reasons for omissions. The experimental results using the state-of-the-art AS model show that CA can help provide valuable explanations for the model's fact selection process. © 2024 IEEE.

关键词： Deep learning

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Classification of Plant Disease using Machine Learning Algorithms 6

Classification of Plant Disease using Machine Learning Algor...

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Appalanaidu, Majji V. Kumaravelan, G. Department of Computer Science Engineering Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science Pondicherry University Karaikal Campus Karaikal India

ISBN: (纸本)9798350374629

The identification of plant diseases has become a key topic in smart agriculture research in recent years. The productivity of agricultural products will undoubtedly increase with early plant disease identification. For the farmers, it is difficult to determine the specific diseases affecting the plant leaves. Therefore, the development of an automated system becomes an essential practice for detecting diseases. So that the remedies can be suggested according to the disease's severity. In this situation, image processing methods combined with Machine Learning (ML) algorithms have lately offered an accurate and dependable way of identifying and categorizing many plant diseases. Hence, this work investigates the importance of the deployment of various ML algorithms such as Naïve Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) in automatic classification of plant diseases on the benchmark plant village dataset. ©2024 IEEE.

关键词： Agricultural products

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Person Image Generation To Enhance The Input Training Image For Re-Identification 14

Person Image Generation To Enhance The Input Training Image ...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Anjali, P.S. Rahamathulla, K. Swaraj, K.P. Government Engineering College Department of Computer Science and Engineering Thrissur India Government Engineering College Sreekrishnapuram Department of Computer Science and Engineering Palakkad India

ISBN: (纸本)9798350335095

The process of recognizing a person from photos or videos taken with a different camera is called re-identification (reID). In today's world, the widespread use of extensive networks of CCTV and security cameras makes it difficult for a human operator to trace a person. This is where re-identification technology comes into play. However, person re-identification in real-world applications is very challenging due to the size of the existing dataset, the lack of human motions, and the inability to supply enough training samples. In this paper, proposed a method for person image generation using Generative Adversarial Networks (GANs). GANs allow us to build additional person images that look as realistic as possible. © 2023 IEEE.

关键词： Generative adversarial networks

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Enhancing Resilience of Deepfake Detection using Adversarial Frequency Masking 15

Enhancing Resilience of Deepfake Detection using Adversarial...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Seth, Nilesh Patel, Nitin Jindal, Rajni Department of Computer Science and Engineering Delhi Technological University Delhi India

ISBN: (纸本)9798331300579

This research presents an advanced approach to deepfake detection that combines frequency domain masking with adversarial training techniques to enhance the precision and resilience of the current models against adversarial attacks. The approach consists of advanced preprocessing of the images with Fourier and Wavelet transforms for feature extraction along with the creative adversarial training techniques. The approach's usefulness is demonstrated by experimental findings, especially in identifying difficult and realistic looking deepfakes. The study presents a comparative analysis between multiple models when trained using this technique and trained without using this technique. This research can be used to counteract deepfake threats and what future research should focus on in order to improve deepfake detection capabilities. © Grenze Scientific Society, 2024.

关键词： Frequency domain analysis

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A Review of Methods and Sensors for Detecting Forest Fire 2

A Review of Methods and Sensors for Detecting Forest Fire

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Gupta, Shilpy Mishra, Vikash Kumar Rai, Vipin Bishnoi, Aksh Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350368109

Forest fires are a real hazard to human lives, biotic components and abiotic components. Predictions says, by 2030 almost half of the world's jungle will be destroyed by forest fire. The early fire detection is only an optimized way to decrease damages caused by forest fires. That's why the forest fire prediction is gaining attention. Forecasting the origin and progression of forest fires, could yield substantial advantages for public health and safety, the financial sector, and the natural world. This might aid in identifying high-risk areas;for instance, with limited resources, professionals could opt to focus on studying certain places. A number of factors have led to the use of image processing in this study, including the quick advancement of digital camera technology, the camera's excellent ability to cover vast areas, the quicker reaction times of image processing techniques compared to current sensor systems, and the lower overall cost of image processing systems compared to sensor systems. Using satellite images, forest fires can be predicted as soon as possible, informing the fire department to operate rescue missions and stop fires. Old methodology uses Monitoring and Results Management (MRM), with University of California Irvine (UCI) machine learning repository, and Heat map to make more predictive power while recently used methodology, Random Forest regression uses Optimal Mass Transport (OMT) and NSD. © 2024 IEEE.

关键词： Fire hazards

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Quality-Driven Web Service Selection: Machine Learning Based Approach 11th

Quality-Driven Web Service Selection: Machine Learning Based...

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11th International Symposium on Applied Computing for Software and Smart Systems, ACSS 2024

作者： Kumar, Dewashish Sarkar, Anirban Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India

ISBN: (纸本)9789819797615

The task of choosing a suitable web service has become increasingly difficult in today’s landscape due to the growing number of services offering similar functionalities. This paper addresses the critical need for efficient web service selection based on Quality of Service parameters. A minimal set of discrete QoS characteristics were carefully chosen as inputs for several machine learning models targeted at finding and suggesting suitable web services. The performance of these models is systematically evaluated and compared. The machine learning techniques used for this work include a Support Vector Machine, Decision Tree, and Random Forest. Among these, the results show that the proposed Random Forest model consistently outperforms other techniques in terms of accuracy and effectiveness. This study contributes to the advancement of web service selection methodologies by providing insights into the comparative performance of various machine learning approaches, ultimately offering a valuable guide for decision-makers in navigating the intricate landscape of web service options. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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