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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66560 条记录，以下是271-280 订阅

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FingerPrintInsight:Unveiling Crime Pattern Through Deep Fingerprint Analysis 4

FingerPrintInsight:Unveiling Crime Pattern Through Deep Fing...

引用

4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Kavitha, T. Revappa Handignur, Sachin B Sachin, Y.M. Purandare, Sumit S Rathish, C.R. New Horizon College of Engineering Dept. of Computer Engineering Bangalore India

ISBN: (纸本)9798350393354

A fingerprint identification system employing deep machine learning and Convolutional Neural Networks could automate analysis. Images from crime scene investigation techniques are entered into a database. Partial latent prints are often difficult to classify. The system operates in three phases: preprocessing, feature extraction, and matching. Preprocessing enhances image quality before feature extraction identifies distinctive minutiae points. False minutiae removal further refines the data. The preprocessed fingerprints serve as input to train and test the model. As new prints are incorporated along with confirmed matches to improve accuracy, the system facilitates identification. The approach scales without proportionate increase in human effort. Accelerated evidence analysis could potentially solve more crimes by linking cases in a database. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting Stock Price by LSTM-CNN Hybrid Model and Compares Deep Learning Models 1

Forecasting Stock Price by LSTM-CNN Hybrid Model and Compare...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Arora, Aditi Upadhyay, Yashaswi Shukla, Satvik Saket Abes Engineering College Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350377002

The The accurate forecasting of stock prices holds significant importance in devising effective trading strategies and making informed decisions about stock transactions. The inherent volatility in stock prices, driven by factors like geopolitical tensions, corporate earnings, and commodity costs, underscores the need for precise predictions. Domestic factors such as central bank policies, governmental decisions, inflation, and global market uncertainties also trigger stock price fluctuations. Estimating this volatility is a complex challenge for traders. A novel approach is presented in this research, proposing a hybrid model that integrates two distinct algorithms - Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). This model harnesses diverse data representations from stock time series to enhance price prediction accuracy. Comparative analyses are performed with alternative models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks) and Naïve Bayes to ascertain the most effective prediction paradigm with minimal error. The study demonstrates that combining temporal and image features extracted from the same dataset diminishes prediction errors compared to using these features independently. The hybrid model outperforms singular prediction models, highlighting a high enhancement. Among the individual models, the LSTM model exhibits superior performance in terms of prediction accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Analyzing and Visualizing Causes of Poverty in India: A Data-Driven Approach Using HPCC systems 8

Analyzing and Visualizing Causes of Poverty in India: A Data...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Skanda, P.R. Shobha, G. Shetty, Jyoti Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Poverty in India is a complex issue with multiple causes. This paper mainly focuses on three of the main causes for poverty: Education, Unemployment and Consumer Price Index. This study makes use of data received from the National Informatics Center (NIC) of India to predict poverty across the various states of India. Through applying preprocessing techniques and employing the HPCC systems Visualization bundle, the research shows that Bihar is facing a higher level of poverty, whereas Mizoram exhibits converse trend. Subsequently, the HPCC ML-Core bundle is applied to conduct linear regression analysis for predicting poverty scores. This predictive model demonstrated an accuracy of 97%. Lastly, the difference between a single-node and 8-node cluster is presented for runtime performance which clearly shows that the 8-node cluster has an advantage for similar computational tasks and thus makes it suitable. © 2024 IEEE.

关键词： Visualization

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Big Mart Sales Analysis 6

Big Mart Sales Analysis

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

作者： Kunal Singh, Muskaan Sharma, Harshit Roy, Shweta Agarwal, Kadambri Dept. of computer science and engineering ABES engineering college Ghaziabad India

ISBN: (纸本)9798350374629

Machine learning is a powerful technology that enables computer systems to recognize patterns and make prediction and choices without performing explicit programming. The basic notion behind this technology is to create models and algorithms that can take in input information, predict an outcome using statistical computation, and adjust outputs in response to fresh information. In this research paper, we examine the Big Mart Shopping Centre scenarios in order to forecast sales of various product categories and comprehend the impact of various factors on sales of various product categories. This paper focuses on leveraging machine learning techniques to predict sales for Big Mart with the aim of optimizing inventory management and maximizing revenue. Using a variety of characteristics from a dataset that Big Mart gathered, along with the methodology used to construct a prediction model, very accurate results are produced and these findings can be utilized to make decisions aimed at increasing sales and increase operational efficiency. ©2024 IEEE.

关键词： Sales

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Design and Development of Cognitive Training systems Based on Extended Reality and BCI Technology 7

Design and Development of Cognitive Training Systems Based o...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Lin, Pei-Jung Ku, Hsiu-Chien Lin, Li-Lun Feng Chia University Dept. of Information Engineering and Computer Science Taichung Taiwan Hungkuang University Dept. of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350307207

Dementia is considered a Mild Cognitive Impairment (MCI), representing a blurred transition from normal cognitive function to dementia. MCI might be a precursor to dementia being caused by various neurodegenerative diseases, including Alzheimer's disease, stroke, metabolic disorders, and mental health conditions. According to the World Health Organization (WHO), over 55 million people worldwide are suffering from dementia, with nearly 10 million new cases each year. This number is expected to rise to 78 million by 2030. Numerous studies have proposed various training methods targeting specific cognitive domains in dementia. For instance, reminiscence therapy encourages individuals to recall buried memories, while reality orientation therapy focuses on strengthening orientation cues to help patients with dementia establish a stronger sense of control. With aging, changes occur in cognitive and physical functions. Research suggests that memory functions can be improved through training. Therefore, the primary goal of this study was to develop an Extended Reality (XR) cognitive system to assist MCI patients in enhancing logical thinking, increasing independence in daily life, and improving response accuracy. The application of extended reality technology in the medical field is expanding, covering areas such as surgical assistance, medical consultation, disease treatment, and medical training. We integrated mixed reality devices with brain-computer interface (BCI) equipment. By detecting brainwave signals in the frontal region of the brain through sensors, neural activity was quantified, and digital brainwave data was transmitted to the system. This data was then visualized on screen in real-time to analyze changes in brainwave data during training. Through multifaceted task training, the system enhanced cognitive response and sensory perception, stimulating brain activity with the goal of strengthening cognitive functions. © 2024 IEEE.

关键词： Mixed reality

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Enhanced Fetal Ultrasound Image Segmentation using Spatial Attention Mechanisms with UNet: SAUnet

Enhanced Fetal Ultrasound Image Segmentation using Spatial A...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Verma, Harshita Patro, Bdk Rajkiya Engineering College Dept. of Computer Science and Engineering Kannauj India

ISBN: (纸本)9798350376876

Fetal Ultrasound (US) imaging is necessary to prenatal treatment, as it provides information on the health and development of the fetus. Accurate segmenting is essential but poses significant challenges due to the dynamic fetal anatomy. This paper proposes Unet with Spatial Attention (SAUnet), a novel deep learning architecture that integrates attention mechanisms into the Unet framework. Unlike traditional models, SAUnet dynamically emphasizes key anatomical structures while suppressing irrelevant background information, enhancing the model's focus on informative regions within the US images. This targeted approach leads to improved segmentation accuracy and robustness. Our method was evaluated on the HC18 Challenge dataset, achieving a mean absolute difference(±std) of 2.23 ± 2.41 mm in head circumference measure and a mean Dice similarity coefficient of 97.63 ± 1.85%, demonstrating high segmentation accuracy. Additionally, mean difference and Hausdorff distance are 1.06 ± 3.11 mm and 1.42 ± 0.93 mm respectively suggesting robust performance. Incorporation of spatial attention in Unet architecture marks a significant advancement in fetal US image segmentation, offering a lightweight and efficient model suitable for real-time clinical deployment. Benchmark comparisons highlight SAUnet's competitive performance, reinforcing its potential as a state-of-the-art solution for prenatal imaging tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

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1st International Conference on Trends in engineering systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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Early Epilepsy Seizure Monitoring Through Integrated Eye Tracking, Heart Rate and EEG Sensors 21

Early Epilepsy Seizure Monitoring Through Integrated Eye Tra...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Barqan, Najlaa Almarri, Asma Alsheeb, Fatima Badawy, Salma Abualsaud, Khalid Yaacoub, Elias College of Engineering Qatar University Dept. of Computer Engineering Doha Qatar

ISBN: (纸本)9798350378078

Epilepsy is a neurological disease, that randomly affects individuals of all ages and usually starts to appear in childhood, but it can also begin to appear in adulthood. Statistically, epileptic individuals confront a premature death that is three times higher compared to the general population. Furthermore, an overwhelming 80% of those individuals are in countries with limited health resources, making effective seizure management difficult. This paper aims to propose a device in response to the medical challenge of healthcare providers coping with and monitoring epileptic seizures. The device is equipped with integrated parameters that are focused on eye blinking, heart rate, and Electroencephalography (EEG) signals. This unique combination offers an effective way to monitor individuals with epilepsy, providing critical data for managing their conditions. Utilizing advanced assistive technology, the proposed device monitors and detects seizure episodes early with high sensitivity, allowing immediate assistance by caregivers and health providers. In addition to data collection, this device is an alternative approach that may lower the chance of harm and facilitate better monitoring and seizure detection. Finally, the monitoring device can constantly check the status of the patient's disease making assistance reachable and easier. © 2024 IEEE.

关键词： Assistive technology

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Five Shades of BERT: Assessing Performance in Question Answering through Comparative Analysis 4

Five Shades of BERT: Assessing Performance in Question Answe...

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

作者： Raikwar, Kanishka Rajusth, Priyansh Prabhu, Sania Shrivastava, Navya Kj Somaiya College of Engineering Dept. Computer Engineering Mumbai India

ISBN: (纸本)9798350354218

The Bidirectional Encoder Representations from Transformers (BERT) model has transformed natural language processing, achieving great results across a wide range of applications. In this research paper, we conduct a comprehensive comparative analysis of the five BERT models and their performance for question answering tasks. For the purpose of our comparison, the models we have chosen are BERT, RoBERTa, ELECTRA, ALBERT and DistilBERT. Our methodology involves fine-tuning these models using the SQuAD dataset for extractive question answering tasks and evaluating their performance based on their training and validation losses. Our research provides insights into the similarities, differences and results of fine-tuning on the different BERT models. Our findings provide guidance for selecting an appropriate BERT model to utilise its full potential in real-world applications such as question answering tasks as NLP continues to evolve. © 2024 IEEE.

关键词： Natural language processing systems

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Recommendation System For Social Platform Using Deep Learning Techniques 8

Recommendation System For Social Platform Using Deep Learnin...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nishchal Narayan, S. SriVidya, M.S. Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505462

This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and mutual friends, the system aims to provide accurate friend suggestions. This recommendation methodology can be applied across various domains, including search engine recommendations (Google, Explorer, Microsoft Edge), music recommendations (Wynk), video recommendations (YouTube), movie recommendations (Amazon Prime), and product recommendations (Flipkart, Amazon). The study employs similarity coefficient calculations like Jaccard Distance and Cosine Similarity, alongside ranking measures such as PageRank. Furthermore, the system's performance is evaluated using advanced deep learning techniques, including Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Comprehensive experimentation involves measuring the F'l-Score and comparing the accuracy of various machine learning and deep learning algorithms to identify the most effective methods for friend recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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