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Comparative Analysis of Extractive Summarization Using NLP

Comparative Analysis of Extractive Summarization Using NLP

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Ray, Ashutosh Mishra, Anjana Sahoo, Bisal Tripathy, Hrudaya Kumar C.V. Raman Global University Dept. of Computer Science and Engineering Odisha Bhubaneswar India Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798331508432

The step-by-step method of constructing a short, easy-to-read, understandable, and contextual summary of an extensive text document is called text summarization. The principal role is to acquire the correct amount of information within a period. Text reports are finished by humans, which requires experience in space and is also terribly tedious and time-consuming. Along with the advancement of technology, the amount of text data is difficult to understand and is not available in a structured manner due to the larger number of characters. Hence, this is a necessary tool for today. It has been divided into two subparts: text summarization with an abstractive approach, otherwise known as Abstractive Text Summarization (ATS), and the second, text summarization with an extractive approach, otherwise known as Extractive Text Summarization (ETS). In comparison to ATS, the second technique is significantly more manageable and efficient. ETS primarily operates by extracting essential tokens, and in some cases, entire sentences, from the input text document. Consequently, this approach autonomously generates a summary. This work has not only identified novel findings but also suggested cutting-edge techniques and approaches for future researchers in the field, offering valuable insights and practical support. © 2024 IEEE.

关键词： Data assimilation

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A Satisfibility based Watermarking Approach for Hardware Security in High Level Synthesis 20

A Satisfibility based Watermarking Approach for Hardware Sec...

引用

20th IEEE India Council International Conference, INDICON 2023

作者： Cs, Ashwin Poddar, Vipul Roy, Suchismita National Institute of Technology Dept. of Computer Science & Engineering Durgapur India National Institute of Technology Dept. of Electrical Engineering Durgapur India

ISBN: (纸本)9798350305593

In today's world Time-To-Market (TTM) plays a very important role in the success of any given chip. To reduce the TTM, many manufacturers rely on reusable IP cores. These reusable Intellectual Property(IP) cores may come with a lot of unforeseen security issues related to reliability and verifiable point of origin. This paper presents a way of watermarking to reduce these security threats by embedding the designer's watermark into an IP design at High Level Synthesis (HLS) stage. A boolean Satisfibility (SAT) based approach is proposed which has no additional area and performance overhead. © 2023 IEEE.

关键词： High Level Synthesis Satisfibility Watermarking

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using Data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

引用

2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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IoT Attack Detection Method based on Synthetic Minority Over-Sampling with Random Forest Technique 10

IoT Attack Detection Method based on Synthetic Minority Over...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Tripathi, Neha Mishra, Amit Kumar Bagla, Piyush Pandey, Neeraj Kumar Mittal, Saksham Dept. of Computer Science and Engineering Dehradun India National Institute of Technology Dept. of Computer Science and Engineering Jalandhar India School of Computing Dit University Dehradun India

ISBN: (纸本)9789380544472

The primary objective of this study is to create a self-protection system for the Internet of Things (IoT) that relies on machine learning. One such resampling approach is the proposed IoT Attack Detection Method based on Synthetic Minority Over-sampling with Random Forest (IADMSMO-RF). To identify risks in the IoT ecosystem, we employ IADMSMO-RF, which improves network search computation. Assessment is performed on the Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) dataset to see how effective the trial is in recognizing attacks on the IoT. For the success of the IoT, it is crucial to have a reliable system for detecting and protecting against cyberattacks. Compared to Synthetic Minority Oversampling Technique - Synthetic Minority Oversampling Technique (PPDT -SMOTE), the suggested technique outperforms it in attack detection and categorization. The proposed system has an accuracy of 97.46%, a false alarm rate of 0.2%, and a curve area of 98.76%. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection 37

Augmented Memory Replay-based Continual Learning Approaches ...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Amalapuram, Suresh Kumar Channappayya, Sumohana S. Tamma, Bheemarjuna Reddy Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad India Dept. of Electrical Engineering Indian Institute of Technology Hyderabad India

ISBN: (纸本)9781713899921

Intrusion detection is a form of anomalous activity detection in communication network traffic. Continual learning (CL) approaches to the intrusion detection task accumulate old knowledge while adapting to the latest threat knowledge. Previous works have shown the effectiveness of memory replay-based CL approaches for this task. In this work, we present two novel contributions to improve the performance of CL-based network intrusion detection in the context of class imbalance and scalability. First, we extend class balancing reservoir sampling (CBRS), a memory-based CL method, to address the problems of severe class imbalance for large datasets. Second, we propose a novel approach titled perturbation assistance for parameter approximation (PAPA) based on the Gaussian mixture model to reduce the number of virtual stochastic gradient descent (SGD) parameter computations needed to discover maximally interfering samples for CL. We demonstrate that the proposed approaches perform remarkably better than the baselines on standard intrusion detection benchmarks created over shorter periods (KDDCUP'99, NSL-KDD, CICIDS-2017/2018, UNSW-NB15, and CTU-13) and a longer period with distribution shift (AnoShift). We also validated proposed approaches on standard continual learning benchmarks (SVHN, CIFAR-10/100, and CLEAR-10/100) and anomaly detection benchmarks (SMAP, SMD, and MSL). Further, the proposed PAPA approach significantly lowers the number of virtual SGD update operations, thus resulting in training time savings in the range of 12 to 40% compared to the maximally interfered samples retrieval algorithm. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Intrusion detection

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Fine Tuning GANs for Image Colorization: Effects of Training Data and Epochs 7

Fine Tuning GANs for Image Colorization: Effects of Training...

引用

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

作者： Patil, Rahul Patil, Pramod Joshi, Rohit Joshi, Atharv Hastak, Ninad Joshi, Akshay Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Pune India Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Ever since the advent of Deep Learning, there has been a lot of experimentation and research being carried out in image processing. This gave rise to the conversion of black and white i.e. grayscale images into colorized images. Image colorization is a popular image-to-image translation problem. It finds its application in various domains like image restoration, movie colorization, colorization of old aerial surveillance shots, medical image colorization, etc. Earlier this was done manually by artists and photographers but recent advancements in deep learning have enabled automatic image colorization using techniques like Convolutional Neural Networks and Generative Adversarial Networks. CNN is a neural network that has layers that are essential for extracting features from images and can learn the complex mapping between grayscale images and their corresponding ground truth images. On the other hand, GANs is a relatively new technique that consists of two components: generator and discriminator. This research paper aims to present the results obtained by using both the above-mentioned methodologies for image colorization. Many examples of generated images and their respective accuracies and structure according to the technique used are discussed in the later part. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Generative Adversarial Network Grayscale Image Colorization

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Deep Reinforcement Learning Based Grid-Forming Inverter

Deep Reinforcement Learning Based Grid-Forming Inverter

引用

2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Balouji, Ebrahim Backstrom, Karl McKelvey, Tomas Chalmers University of Technology Dept. of Electrical Engineering Gothenburg Sweden Chalmers University of Technology Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798350320169

This research work introduces Deep Deterministic Policy Gradient (DDPG), a type of Reinforcement Learning (RL), for grid modeling, estimating voltage and phase angle, and control method for grid-forming inverters. The aim is to develop a grid-forming inverter that sets the voltage level and frequency of the grid and mitigates voltage dips originating from faults and frequency deviation fluctuations. Unlike conventional methods for estimating setpoints for the controller loops, we do not need several chains of estimation tools such as Fast Fourier Transform (FFT), Synchronous Reference Frame (SRF), or lowpass filters. With the DDPG, we also optimize the phase lock-loop (PLL) and accurately deliver the angle for the actuation part of the inverter to generate the given reference signal. The developed method does not need exhaustive tuning of parameters such as coefficients of PID controllers and lowpass filters. We observe that the proposed method has a faster response time than the PID-based control unit (15ms compared to 50ms) for the grid-forming inverter in the case of compensating voltage dips. We also observed that the DDPG-based grid-forming inverter is more efficient in compensating continuous voltage variations and frequency deviations than a trivial PID-based version. © 2023 IEEE.

关键词： Reinforcement learning

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Analysis of optimization Techniques for Dynamic Neural Networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

引用

3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 IEEE.

关键词： Neural network models

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Malware Detection across Clusters

Malware Detection across Clusters

引用

2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Manjushree, C.V. Nandakumar, A.N. Vemana Institute of Technology Dept. of Information Science & Engineering India City Engineering College Dept. of Computer Science & Engineering India

ISBN: (纸本)9781665462631

Due to diverse software vulnerabilities and hardware attacks, user credentials are vulnerable or could land in demilitarized zones. An attempt is made to explore and finally proposed a trust based malware detection based on optimal data classification. An improved glowworm swarm optimization (IGWSO) algorithm is proposed for clustering in the input dataset into different groups. After clustering, recurrent deep neural network based computation is used for deriving different trust levels for cloud information that classify the suspected intrusion. The proposed TMD system implemented in java with cloudsim tool evidently discovers the effectiveness of the algorithm over existing state of art systems on the parameters of high detection, precision, recall and F-measures. © 2023 IEEE.

关键词： Deep neural networks

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Hybrid Nature-Inspired Based Oversampling and Feature Selection Approach for Imbalance Data Streams Classification

Hybrid Nature-Inspired Based Oversampling and Feature Select...

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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Arya, Monika Dewangan, Bhupesh Kumar Verma, Monika Rohini, M. Motwani, Anand Sar, Sumit Kumar Bhilai Institute of Technology Dept. of Computer Science & Engineering CG Durg India Op Jindal University Dept. of Computer Science Engineering Raigarh India Sri Krishna College of Engineering and Technology Dept. of Computer Science & Engineering Coimbatore India Vit Bhopal University School of Computing Science & Engineering MP Sehore India

ISBN: (纸本)9781665492942

A big data stream is described using 5 mathrm{V}primes (Volume, Variety, Velocity, Variability, and Veracity). These characteristics impose various challenges. In many cases, data streams are unbalanced, making traditional data mining approaches impossible to employ. The standard data mining approaches are not suitable for imbalanced data streams for achieving analytical efficiency because they require periodic analyses, but big data requires real-time analytics. Additionally, the induction model must be re-run and rebuilt each time to add up the most recent data. Mining these unique streams, on the other hand, is one of the most intriguing research areas. Deep learning (DL) algorithms were developed to increase classification performance for issues requiring large data sets with varying types and characteristics. Feature selection (F.S.) is a critical stage in any classification application. F.S. entails eliminating superfluous and redundant characteristics, resulting in a prediction model that is more efficient, interpretable, and fast. While complete solutions are available for F.S., managing massive data streams that need instantaneous processing is challenging by its own nature. This work provides a hybrid metaheuristic strategy with FFSMOTE for oversampling imbalanced data stream and Honey Bee algorithm for feature selection. Hybridization aims to improve feature selection processes by combing the advantage of both the algorithms. Furthermore, the Ensemble Deep classifiers are used for data stream classification. © 2023 IEEE.

关键词： Classification (of information)

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