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共 40920 条记录，以下是981-990 订阅

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AI Enhanced-Smart Genome Editing: Integration of CRISPR-Cas9 with Artificial Intelligence for Cancer Treatment 5

AI Enhanced-Smart Genome Editing: Integration of CRISPR-Cas9...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Kumar, D. Nitesh Chowdhary, D. Lavanya Pathuri, Trinadh Katta, Priyanka Arya, Leena Dept. of Computer Science & Engineering Koneru Lakshmaiah Education Foundation AP Guntur India

ISBN: (纸本)9798350361155

The CRISPR-Cas9 genome editing system is a new technology that allows the modification of secret vital material using disruptions insertions or amendments of DNA sequence. It is an easy, widely accepted, and correct representation of gene modification. The potential for treating different genetic diseases is enormous. This paper discusses the latest approach to CRISPR-Cas9 integration with AI, aimed at improving efficacy, as well as the safety of cancer treatment. This paper highlights the cutting-edge integration of CRISPRCas9 with Artificial Intelligence (AI) to enhance the efficacy and safety of cancer treatment. Artificial intelligence is a simulation of human intelligence for computers, indicates the latest developments in the field, and mentions clinical trials' ability to help further the treatment of cancer and overcome current and upcoming challenges. Finally, the application of (AI) in the modification of the CRISPR/Cas9 system is highlighted. Taking case scenarios and futuristic approaches, this paper reveals excellent implementation initiatives by learning from the Clinical revolution together with human technology and aimed at a future in which new technologies improve patient outcomes without sacrificing ethics or putting patients first. © 2024 IEEE.

关键词： Diseases

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Time-Dependent Multi-Objective Trip Planning by Ant Colony Optimization with Route API

Time-Dependent Multi-Objective Trip Planning by Ant Colony O...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Saeki, Etsushi Bao, Siya Takayama, Toshinori Togawa, Nozomu Waseda University Dept. of Computer Science and Communications Engineering Japan ZENRIN DataCom Japan

ISBN: (纸本)9798350324136

In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time stamps and computes time-dependent travel time by utilizing route API. Moreover, we reduced the response time from the route API calls. Compared with two conventional methods, our proposed method provided routes with high time-dependent values. Meanwhile, the number of API calls is reduced by 98.8% on average by introducing the API call reduction. © 2024 IEEE.

关键词： ant colony optimization past-trip record route API time-dependent multi-objective trip planning problem

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Bilinear Attention Based Learning on Graphs

Bilinear Attention Based Learning on Graphs

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Njanko, Adrien Rawat, Danda B. Howard University Dept. Electrical Engineering and Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

The attention mechanism is a core module in today's well-established models like Transformers and Graph Attention Networks (GAT). The attention coefficients needed typically requires heavy computational resources for stability and better predictions. This requirement limits the size of the sequence or graph a model can learn from, leading to separating connected graphs or sequences to ease training. In this paper, we introduce an attention mechanism that leverages Bilinear forms to handle sequences of arbitrary length, offering greater flexibility and scalability. This improvement not only optimizes computational resources but also offers better model performance. Furthermore, our Bilinear Attention Mechanism (BAM) can capture more complex relationships between nodes for less parameters count. © 2024 IEEE.

关键词： Contrastive Learning

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Tracking Dynamic Conditional Neural Correlation during Task Learning 46

Tracking Dynamic Conditional Neural Correlation during Task ...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Zixu Chen, Shuhang Li, Mingdong Wang, Yiwen The Hong Kong University of Science and Technology Dept. Electronic and Computer Engineering Hong Kong

ISBN: (纸本)9798350371499

Single neuron modulates the external stimuli, and neural population coordinates to encode information. An alternate method for examining the coordinated populational activity in neural encoding is conditional neural correlation (CNC). However, such correlations are not static during a new task learning process as neurons adapt their tunings over time for better performance. To investigate how neurons adjust their firing patterns during learning, it's essential to track the time-variant correlation. In this paper, we propose to mathematically model the dynamical CNC by implementing the integrated point process filter which incorporates neural correlation and single neural tuning into decoding. Specifically, we generate synthetic M1 neurons' firing data to simulate the dynamic change of the conditional neural correlation over days, while a rat learns a two-lever discrimination task. By comparing the tracked CNC with the designed CNC, our results show that the CNC can be better tracked over time by CIPPF than that of decoder assuming conditional independence among neurons, which indicates the possibility to better understand the brain dynamics during task learning. © 2024 IEEE.

关键词： Decoding

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Event Management System Using Generative AI 7

Event Management System Using Generative AI

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thummala, Sruthi Thammishetti, Saketh Varkol, Sharanya Thirunahari, Amarthya Lakshmi Kanthey, V.V.S. B V Raju Institute of Technology Dept. Computer Science and Engineering Narsapur India

ISBN: (纸本)9798350372816

This project depicts how Artificial intelligence is used in developing a "User-Engaging Event Management System". The developed system will spontaneously record all the events and online contests registered by an individual. Motivates the individual to participate the particular event by providing sufficient resources. It implements a persistent alarm or remainder system that operates continuously. The implementation of the system starts with choosing appropriate technologies (Deep learning Libraries, Natural Language Processor, DBMS, Web Development Framework and so on) focusing on generative AI followed by data collection (from existing event data, social media, crowdsourcing and so on) and preprocessing the data to ensure the quality for AI model training which integrates with app architecture. This study evaluates the effectiveness of the generative AI model in Event Management App by employing key numerical metrices. The accuracy metric gauges the model's overall correctness in generating event-related content, while precision focuses on the accuracy of positive predictions. It incorporates a robust feedback mechanism ,allowing users to provide input and receive corresponding responses, enhancing the overall user experience. The system will provide security for user information, access controls, and authentication protocols © 2024 IEEE.

关键词： Access control

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SafeSync: A Comprehensive Safety and Monitoring System for Two-Wheeler Users Using Machine Learning, ESP32, GPS, and GSM 6

SafeSync: A Comprehensive Safety and Monitoring System for T...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational systems, RAICS 2024

作者： Sunil, K.S. Adithyan, P. Krishnan, Anjana Hashim Sharaf, C.P. Paul, Jerin John, Salvi Hanna Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350381689

Our work, 'SafeSync' proposes a complete safety and monitoring system aimed to address the considerable hazards encountered by two-wheeler users, such as accidents and rash driving scenarios. Unlike other systems, SafeSync combines powerful technology such as ESP32, GPS, and GSM with an innovative application of machine learning methods. This system specializes in detecting and alerting to accidents, tracking vehicles in real time, and identifying risky driving behaviour. SafeSync goes beyond traditional safety measures by not only identifying ongoing accidents but also forecasting potential events. This predictive feature enables the system to proactively inform users or authorities in advance, assisting in the prevention of accidents. The Flutter-built dedicated mobile app serves as a user-friendly interface, offering real-time updates on the vehicle's status and location. The program also allows users to customise monitoring settings and add parental restrictions. By actively recognizing and warning users to dangerous practises, SafeSync strives to improve rider safety and promote responsible driving behaviour. Finally, the initiative helps overall road safety by providing a solid solution for two-wheeler users to monitor and enhance their road safety. © 2024 IEEE.

关键词： Accident Alert ESP32 GPRS GPS GSM MPU6050 Rash Driving

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Proposed Model for Prediction of Stock Market Price of Netflix

Proposed Model for Prediction of Stock Market Price of Netfl...

引用

2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023

作者： Patwal, Pratap Singh Srivastava, Anoop Kumar Laxmi Devi Institute of Engineering & Technology Dept. of Computer Science & Engineering Rajasthan Alwar India Aced Alliance University Dept. of Computer Science & Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665456272

Accurate prediction of stock market price is highly challenging. This paper presents a proposed model for prediction of stock market price of Netflix. We have considered a five-year data set (April, 2017 - April, 2022) of Netflix. An Exploratory Data Analysis (EDA) of Netflix's stock price data for predicting its stock market prices using time series is done. The implementation of the model is done using Python language. We imported five-years data and applied several techniques: importing libraries, calculating stock return, line plot, plot all, plot return year wise, plot histogram, plot kernel density, plot box plot, differencing method, resample daily to monthly data etc. EDA proved that using time series technique achieved better results in prediction of stock price and visualizing. © 2023 IEEE.

关键词： Forecasting

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Efficient Path Planning for Emergency Medical Services With Stable Connectivity Demands

Efficient Path Planning for Emergency Medical Services With ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Wen-Pin Hu, Xuan-Zhang Nguyen, Van-Linh Nguyen, Lan-Huong Hwang, Ren-Hung Dept. of Computer Science and Information Engineering Chiayi Taiwan College of Artificial Intelligence Taiwan

ISBN: (纸本)9798350351255

Currently, online map navigation applications are essential to our daily demands, such as route finding in a big city or booking a taxi trip. Further, the applications also play a pivotal role in finding access to the nearest medical aid in big cities with complex traffic networks. However, existing commercial map programs are unable to provide route suggestions in areas where a continuous network connection is assured, which is essential for distant emergency medical services. This study introduces a highly effective personalized shortest-path algorithm for finding the fastest path of an emergency vehicle (EV) while also ensuring stable network connections for potential remote robotic surgery. By using a customized A-star algorithm, the experimental findings demonstrate that this technique can outperform current search approaches with a 5% shorter path length per 5km. For rescue missions in a large city, the enhancement is remarkable and has the potential to save lives. Due to high compatibility with the existing search algorithms, this method can easily be included as a critical feature for existing commercial map apps. © 2024 IEEE.

关键词： Robotic surgery

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Accessing Visual Information for Divyangjan 4

Accessing Visual Information for Divyangjan

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Rani, M. S. Divya Ghodke, Hrishikesh Vivekanand Manikandan, M. Manaswini, R. Shivani, K. Dhara, Sri Sai Venkata Gayathri Shreya Presidency University Dept. of Electronics and Communication Engineering Bengaluru560064 India Presidency University Dept. of Computer Science Engineering Bengaluru560064 India

ISBN: (纸本)9798331518981

Approximately 1.3 billion people worldwide suffer from a visual impairment. Typically, they must use Braille to read printed materials. However, when the content is not printed in Braille, these individuals have difficulties. Even though there are a lot of electronic reading aids available, the costs remain prohibitive. Therefore, our work suggests a low-cost document reader that uses a webcam to take pictures of printed materials. The Optical Character Recognition (OCR) framework's image-to-text conversion is then used to turn the acquired image into text. Finally, the Text to Speech (TTS) framework's text-to-speech conversion will be used to read the text aloud in speech format. Consequently, a person with a visual impairment can use hearing rather than touch to comprehend printed materials that are not written in Braille. Users may view documents printed in five different languages. The choice of different document sizes is this work's greatest novelty. The system is equipped with a Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, a web camera and speaker. The Raspberry Pi, Arduino Uno, NEMA 17 stepper motors, webcam, and speaker are all part of the system. © 2024 IEEE.

关键词： Stepping motors

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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