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检索条件"机构=Dept. Computer Technology and Computer Graphics"

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Advanced Cryptocurrency and Stock Prices Forecasting via Bidirectional Long Short-Term Memory Model

Advanced Cryptocurrency and Stock Prices Forecasting via Bid...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Ramsriprasaath D R. Bhavani S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Truthful prediction of the values of the cryptocurrencies and stocks is critical in the financial marketplace. We developed a Bi-Lstm model to forecast the next day prices of Bitcoin, Ethereum, and Microsoft stock. The model expands the temporal resolution to the 60-day sequence window, making predictions of financial time series more reliable due to higher temporal dependencies. The effectiveness of the model is evaluated using three error metrics: Hence, use of Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error was employed to guarantee accurate evaluation. Anticipating prices for the year 2025, the proposed system provides information for overall long-term tendencies and possible gains from investments. The findings reveal that the application of Bi-Lstm model can avert the categories of fluctuation that characterises the financial market and is a powerful instrument for making future predictions. The future work might improve the accuracy of the predictions by incorporating other features like market sentiment and macroeconomic indicators.

关键词： Sentiment analysis Recurrent neural networks Accuracy Operating systems Time series analysis Bitcoin Predictive models Macroeconomics Blockchains Long short term memory

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AI Academic Research Aggregator

AI Academic Research Aggregator

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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： Anupama T. A Chandragupta Kumar Ashay Amal Aman Kumar Dept. of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

In responding to the challenges of making the discovery and analysis of academic research papers efficient, APARA is an AI-based academic research aggregator. Classical search engines leave the researchers inundated with gigantic waves of unstructured information. Usually, they lack per-sonalized recommendations and less cumbersome summaries. By means of the arXiv API and natural language processing (NLP), APARA is formulated to automatically extract metadata, summarize research contents, and improve search efficiency. The platform is built on the Django framework which offers valuable user interface capabilities and gives space for content-based research paper recommendations. Because APARA streamlines the research discovery process, relevant studies can be found faster, and academic exploration becomes much more efficient. The work presents the core methodology, system architecture, and evaluation metrics of APARA in parenthesis with the prospect of generating idle works of academic research.

关键词： Soft sensors Systems architecture Metadata Data retrieval Data aggregation Search engines Smart systems Natural language processing User experience Data mining

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Intrutuion Detection System Using Machine Learning Based IDS

Intrutuion Detection System Using Machine Learning Based IDS

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Bhavani R Veeramalai Sankardass Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The number of network attacks has increased in today's dynamic environment. Network assaults are unlawful actions taken against virtual assets within a corporate network. Community attacks are typically performed by malicious parties to change, destroy, or gets the private data. Attackers that want access to internal systems usually target network perimeters in their attacks on communities. Network attacks can be categorized into two major types: passive attack and active attack. In passive network attacks, unauthorized users get access to networks, display personal information, and steal it without making any changes. Active network attacks may include material that has been edited, encrypted, or is undesirable. Upon infiltration, malicious events may additionally leverage different hacking activities, inclusive of malware and endpoint assaults, to assault an organizational network. With greater organizations adopting far off operating, networks have end up greater liable to data theft and destruction. As a end result, our proposed device can monitor community visitors for suspicious pastime and come across the presence of any intrusion.

关键词： Passive networks Machine learning algorithms Intrusion detection Organizations Malware Decision trees Cryptography computer crime Random forests Monitoring

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Alternative Pain Management and Control with VR - The Case for HCI with Gradual Calming Effects

Alternative Pain Management and Control with VR - The Case f...

引用

International Conference on New Trends in Computing Sciences (ICTCS)

作者： Raghad Awajneh Ammar Elhassan Dept. of Computer Science Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Current VR-based pain management solutions often do not address the habituation issue, which can reduce their effectiveness over time and limit their long-term efficacy. Furthermore, existing research has not explored adaptive VR that dynamically responds to pain levels or participant focus in real time. To address these limitations, we developed an adaptive VR system that integrates dynamic calming elements in gradual and real-time that adjust in response to participants' behavior. A pilot experiment was conducted with three groups, each experiencing a different condition: a static VR environment (control), gradual calming elements that unfold over time, and instant dynamic calming elements that adapt in real time based on the focus of the participants. Pain tolerance, heart rate, and blood pressure were measured before and after the VR session to evaluate effectiveness. Findings showed that the dynamic VR group exhibited the highest pain tolerance and a greater reduction in heart rate, suggesting enhanced relaxation and focus. Although blood pressure changes were minimal, heart rate differences indicate its potential as a more responsive physiological metric for pain management. Participant feedback also provided insights for refining the VR experience. Future research will focus on expanding the sample size and optimizing VR comfort to enhance immersion.

关键词： Heart rate Hands Visualization Pain Refining Virtual reality Real-time systems Blood pressure Reliability Testing

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Towards Touchless Interaction: Implementing Hand Gesture Recognition for Presentation and Media Control

Towards Touchless Interaction: Implementing Hand Gesture Rec...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Shailaja Uke Atifa Shaikh Harshada Rayate Anjali Kamble Sanavi Rahane Dept. Computer Science and Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The integration of hand gesture recognition with modern multimedia systems offers a novel and intuitive interface for users to interact with. This research implements a reliable framework for gesture-based control of slide presentations and media functionalities, using real-time hand detection and tracking through computer vision technologies such as OpenCV and cvzone. Recognizing individual hand gestures, the system allows for smooth navigation through slides in a presentation, annotating on slides, changing the brightness, managing windows like window minimization and maximization, and controlling media playback. The solution uses machine learning-based hand pose estimation along with optimal gesture mappings to make the recognition highly accurate and responsive. This research is meant to represent the possibility of hands-free interaction in professional and academic settings. The approach also studies applications in remote work scenarios, thereby increasing the incorporation of gesture recognition technology into everyday computing work. Moreover, the system is flexible and scalable and has the potential to integrate with virtual reality systems, remote learning platforms, and interactive kiosks. The proposed system, under controlled conditions, achieves an average accuracy of about 85 percent to 90 percent. It recognizes predefined hand gestures for navigation and interaction. When multiple gestures are shown at the same time, the system might experience temporary lags, but it recovers functionality within a few seconds. Thus, this research underlines the role of gesture recognition technology in transforming the way a user experience is provided.

关键词： Hands Productivity computer vision Technological innovation Accuracy Navigation Gesture recognition Media Real-time systems Windows

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IoT-Enabled Real Time Intravenous Monitoring and Alert System for Healthcare Management

IoT-Enabled Real Time Intravenous Monitoring and Alert Syste...

引用

Advanced Computing Technologies (ICoACT), International Conference on

作者： P Pabitha J Selvakumar Dept. of Computer Technology Anna University MIT Campus Chennai India Dept. of Computer and Science Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

This paper presents the design and implementation of a Smart-drip Infusion Monitoring System incorporating various sensors and IoT technologies. The system aims to enhance intravenous infusion monitoring by providing precise control and timely alerts during the infusion process. The load cell sensor measures fluid volume, while the air bubble detection sensor detects air bubble status in the intravenous line, ensuring accurate and safe medication delivery. The Arduino Uno coordinates sensor data acquisition, processing, and infusion control, dynamically adjusting the solenoid valve to maintain the prescribed infusion rate. Additionally, the buzzer alerts healthcare providers to critical events such as dose completion or system malfunctions. This integrated approach offers a comprehensive solution for optimizing intravenous therapy, enhancing patient safety, and improving healthcare outcomes.

关键词： Volume measurement Process control Valves Real-time systems Software Sensor systems Sensors Safety Solenoids Monitoring

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Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse 29

Using Machine Learning Algorithms in Edge Intelligence for U...

引用

29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Mohammadvand, Reza Mozayani, Naser Dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency. © 2025 IEEE.

关键词： Digital twin Edge intelligence low-latency communication Metaverse Reinforcement learning Ultra-reliable

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Activity Prediction for Localizing the Events in Imagined Speech EEG Signals

Activity Prediction for Localizing the Events in Imagined Sp...

引用

International Winter Workshop on Brain-computer Interface (BCI)

作者： Arun Balasubramanian Kartik Pandey Gautam Veer Debasis Samanta Dept. of Computer Science and Enginnering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Imagined speech electroencephalogram (EEG) signals are often collected for longer durations than necessary, leading to a difficulty in understanding the generation of EEG during the task as it is likely that most of the data collected is that of resting state. Developing a filter to identify the segments of the trials with actual information and remove those that contain the EEG from resting states could significantly advance our understanding of EEG signals in neuroscience and biomedical engineering. This work uses derivatives-based features to form the feature vectors used to train the classifiers. Based on feature importance analysis, it has been found that the derivative-based features contributed more to the classification than the traditionally preferred feature, band power in the alpha frequency band. Using the proposed features, the classification performance experienced a significant enhancement, surpassing the results reported in previous studies. The greater accuracy of the classifiers with the proposed features implies that they are effective at filtering out the resting state segments from imagined speech EEG signals.

关键词： Location awareness Image segmentation Accuracy Neuroscience Signal processing Information filters Feature extraction Electroencephalography Vectors Speech processing

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Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Supritha Kulkarni Sanchit Sharma Manavi J Vachana U M S Bhargavi Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments.

关键词： Deep learning Adaptation models Ultrasonic imaging Accuracy Surgery Imaging Medical services Robustness Vectors Convolutional neural networks

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Multi-Layered Approach to IoT Cybersecurity in Smart Cities

Multi-Layered Approach to IoT Cybersecurity in Smart Cities

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Daniel Anto Benjamine Madhavan K Pavithra S Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Internet of Things (IoT) has been rapidly growing over the past years. There have been many cybersecurity challenges faced by IoT-based smart cities in existing work for the energy, health and transportation sectors. These cybersecurity challenges are crucial to remove in the IoT sector to implement a safe and secure IoT based smart city. Vulnerabilities across layers in IoT are analyzed and a multi-layered model of security is proposed to enhance data integrity and communication security by using AI, blockchain and ML. This paper would also present several policy recommendations to stakeholders and IoT developers for building a safe urban IoT ecosystem.

关键词： Smart cities Ecosystems Transportation Data models Blockchains Internet of Things Stakeholders computer security Artificial intelligence Resilience

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