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检索条件"机构=Computer Applications in Science and Engineering dept."

共 35033 条记录，以下是91-100 订阅

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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Non-Intrusive Alert For Drivers With Fatigue 4

Non-Intrusive Alert For Drivers With Fatigue

引用

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

作者： Amrutha, K. Shreekumar, T. Resmi, K.R. Vineetha, K.R. Dept. of Computer Science Bangalore India Mite Dept of Computer Science and Engineering Mangalore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354218

Fatigue during driving significantly impairs a driver's reaction time, awareness, and decision-making abilities, leading to an increased risk of accidents. Recognising and mitigating driver fatigue is crucial for enhancing road safety and preventing potential collisions. This research introduces a non-intrusive method to reduce road accidents caused by driver fatigue. Recognising that accidents often occur due to lapses in driver alertness, the system utilises an affordable, high-quality webcam and dashcam. The approach involves extracting essential information through a multi-stage monitoring process to evaluate the driver's state thoroughly. The study includes a detailed analysis of webcam specifications, such as resolution, frame rate, and additional features. It also compares the traditional cascading technique with the advanced MediaPipe method, incorporating facial recognition, eye movement tracking, and head position analysis to detect driver fatigue accurately. © 2024 IEEE.

关键词： Eye movements

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An Effective Method to Detect DHCP Starvation Attack using Port Scanning

An Effective Method to Detect DHCP Starvation Attack using P...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Jony, Asaduzzaman Miah, Abu Saleh Musa Islam, Muhammad Nazrul Dept. of Computer Science and Engineering Dhaka Bangladesh The University of Aizu Dept. of Computer Science and Engineering Aizuwakamatsu Japan Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Nowadays, the DHCP starvation attack is a crucial attack that denied the services for the legitimate network user where the attacker mainly occupies all the IP addresses in the DHCP server pool. Most of the existing studies proposed ICMP (Internet Control Message Protocol) and the ARP (Address Resolution Protocol) based techniques to detect the DHCP starvation attack. However, the ICMP-based detection is ineffective if the client has an active host-based firewall that blocks the ICMP ECHO REQUEST, while the ARP-based detection does not work for DHCP networks separated by a DHCP relay agent and works only within the same or local network. Therefore, the objective of this research is to propose an effective technique to detect DHCP starvation attack detection through port scanning to address the limitations of ICMP and ARP-based detection methods. To attain this objective, this study first highlights the limitations of existing detection methods demonstrating insightful facts. Secondly, the study proposed a port scan-based DHCP starvation attack detection method for local and remote (relay) DHCP networks. Finally, the effectiveness and validation of the proposed techniques were demonstrated using the Multivendor Network Emulation Software (EVE-NG). The study showed that the proposed port scanning-based technique could accurately detect the DHCP starvation attack while overcoming the limitations of the ICMP and ARP techniques. © 2023 IEEE.

关键词： Network security

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Chatbot Based on Emotions Using Deep Learning 1

Chatbot Based on Emotions Using Deep Learning

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Nirupa, V. Renuka, G.B. Naga Lakshmi, B. Sumanth Kumar, C.P. Rudra Sekhar Reddy, A. Paramesh, J. Dept. of Computer Science & Engineering Madanapalle India Dept. of Computer Science & Engineering Anantapur India

ISBN: (纸本)9798350328042

Human emotions are unpredictable as they change very frequently. Human emotions areinvoluntary mental states of feelings and are accompanied by physiological changes. Emotions which can be detected through the facial expressions play a major role and are very much important for non-verbal communication as they reflect on their faces. In the process of developing computer modeling of human's emotions, a lot researches have been made. Therefore, a chatbot detecting the emotions can be a better approach for detecting emotions. In this study, it demonstrate that Convolutional Neural Networks (CNNs) offer a superior method for detecting human emotions and develop a deep learning model that can accurately classify the facial expressions. The FER 2013 dataset was used in this study's training. The evaluation of the suggested approach produces pretty accurate findings, which could inspire academics to develop a Chabot model based on emotions in the future. © 2023 IEEE.

关键词： Convolutional neural networks

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Classification and Prediction of Alzheimer's Disease using Deep Learning 7

Classification and Prediction of Alzheimer's Disease using D...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Brindha, D. Ebenezer, V. Selvaraj, E. Jackson Bevin Geoff, D.M. Dileep, B. Bethina Nambi, M. Thirumalai Dept. of Computer Science and Engineering Coimbatore India Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

This study has proposed a novel technique for performing object detection and image classification with a Convolutional Neural Network (CNN) architecture. Nevertheless, employing a 2D CNN architecture to identify a lot of patients as AD or MCI based on 3D MR Images becomes challenging when dealing with hundreds of MRI images that are highly similar in nature. It provides a solution to this problem that streamlines the concept of classification of patients based on 3D MRI by exploiting 2D data produced by the CNN structure. In this method, 2D characteristics from MRI are extracted and then converted by using an appropriate Machine Learning (ML) algorithm for categorization. © 2023 IEEE.

关键词： Magnetic resonance imaging

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TCCL: Discovering Better Communication Paths for PCIe GPU Clusters 24

TCCL: Discovering Better Communication Paths for PCIe GPU Cl...

引用

29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2024

作者： Kim, Heehoon Ryu, Junyeol Lee, Jaejin Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of Dept. of Data Science Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798400703867

Exploiting parallelism to train deep learning models requires GPUs to cooperate through collective communication primitives. While systems like DGX, equipped with proprietary interconnects, have been extensively studied, the systems where GPUs mainly communicate through PCIe have received limited attention. This paper introduces TCCL, a collective communication library designed explicitly for such systems. TCCL has three components: a profiler for multitransfer performance measurement, a pathfinder to discover optimal communication paths, and a modified runtime of NCCL to utilize the identified paths. The focus is on ring-based collective communication algorithms that apply to popular communication operations in deep learning, such as AllReduce and AllGather. The evaluation results of TCCL on three different PCIe-dependent GPU clusters show that TCCL outperforms (up to ×2.07) the state-of-the-art communication libraries, NCCL and MSCCL. We also evaluate TCCL with DL training workloads with various combinations of parallelism types. © 2024 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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Exploring Advanced Approaches: A Comprehensive Analysis of Machine Learning and Deep Neural Networks in Spectrum Sensing applications 4

Exploring Advanced Approaches: A Comprehensive Analysis of M...

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

作者： Karthiga, G. Saravanan, K. Amc Engineering College Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

The increasing demand for wireless communication resources has intensified the need for effective spectrum sensing techniques to alleviate the scarcity of available spectral bands. This survey paper presents an indept. exploration and comparative analysis of various spectrum sensing methodologies, focusing on Machine Learning (ML) and Deep Neural Network (DNN) approaches. The study delineates the landscape of traditional spectrum sensing techniques, encompassing energy detection, matched filtering, cyclo-stationary feature detection, and cooperative spectrum sensing. This paper explains sensing using machine learning-based such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Hidden Markov Models (HMM) are systematically elucidated. Moreover, the paper meticulously scrutinizes the advancements in deep learning-based spectrum sensing, encapsulating the utilization of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL) methodologies. The discussion encompasses their applications, advantages, and limitations in the context of spectrum sensing. © 2024 IEEE.

关键词： Hidden Markov models

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Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

引用

2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

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Predictive Modeling and Dynamic Analysis of Price Trends in eCommerce using ML Technique 3

Predictive Modeling and Dynamic Analysis of Price Trends in ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Banerjee, Pallab Prasad, Vishal Thakur, Kanika Mitra, Dipra Gaurav, Kumar Kanrar, Soumen Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotias University Dept. of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350364569

In the ever-evolving landscape of eCommerce, staying ahead of pricing trends is paramount for both consumers and sellers. This project presents an advanced Ecommerce Product Price Tracker with Price Prediction capabilities, aimed at empowering users with foresight into future pricing dynamics. Supervised Machine Learning algorithms, encompassing regression and time series analysis, form the backbone of predictive modeling, enabling accurate forecasts of forthcoming price movements. The resultant tool not only provides real-time tracking of current product prices but also furnishes predictive analytics, empowering users with actionable insights. By bridging the gap between data-driven insights and eCommerce dynamics, this project contributes to fostering efficiency and efficacy in the vibrant eCommerce ecosystem. © 2024 IEEE.

关键词： Predictive analytics

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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