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Data Visualisation of Time Tradable Assets Using Machine Learning

Data Visualisation of Time Tradable Assets Using Machine Lea...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Mallikarjunaiah, Nirupama Jain, Rishab S. Vinay, K.R. Navada, Sharan S. Pramod, T.C. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumkur India

ISBN: (纸本)9798350300826

Forecasting financial instruments accurately is a complex task due to the volatile, non-linear and dynamic nature of financial markets. Numerous factors, including political events, global economic conditions, and company performance, contribute to the difficulty of predicting outcomes. However, this challenge also provides a wealth of data that can be leveraged to uncover patterns. With the advancement of AI and increased computational power, programmable prediction techniques have emerged as more effective tools for forecasting tradable assets. This paper explores the application of various regression techniques, such as Linear, Support Vector, Polynomial, and Tree Regression, in financial prediction. Additionally, the study incorporates Long Short-Term Memory (LSTM), a method of recurrent neural network model, to analyse an asset's historical behaviour and generate reliable conclusions. The proposed approach aims to address the practical implementation of prediction systems in real-world scenarios while addressing challenges related to the accuracy of the provided values. Furthermore, appropriate visualization techniques are employed to enhance the interpretation and understanding of the results. By combining advanced prediction models and visualization methods, this research endeavours to improve the accuracy of financial predictions and provide valuable insights for decision-making infinancial markets. © 2023 IEEE.

关键词： Data visualization

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets 4

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

引用

4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Ikram, Muhammad Ahmed, Salman Khan Marwat, Safdar Nawaz University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets. © 2023 IEEE.

关键词： Smart power grids

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Cross-Database Facial Expression Recognition using CNN with Attention Mechanism 14

Cross-Database Facial Expression Recognition using CNN with ...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Jatin Annappa, B. Rashmi Adyapady, R. National Institute of Technology Dept. of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350335095

Facial expression is one of the most effective and universal ways to express emotions and intentions. It reflects what a person is thinking or experiencing. Thus, the expression recognition is one of the key aspects of understanding non-verbal communication and interpreting emotions in social interactions. Some emotions are very confusing, and separating the features between them becomes difficult because they share the same feature space. For example, the distinction between fear, anger, and disgust is confusing. This work tried to improve the model's class-wise performance to detect each class correctly. A distinct combination of deep-learning models is used to calculate the performance of the model, such as ResNet, XceptionNet, DenseNet, etc. The datasets like Real-world Affective Faces Database (RAF-DB), Japanese Female Facial Expression (JAFFE) & Facial Expression Recognition 2013 Plus (FER+) are used to evaluate the model's performance. The proposed model achieved better results and overcame the previous work's limitations. CDE's performance on RAF-DB and FER+ evaluations was significantly better than the current SOTA methods, with an increase in accuracy of 5.18% and 3.98%, respectively. © 2023 IEEE.

关键词： Face recognition

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Generating Textual Video Summaries using Modified Bi-Modal Transformer and Whisper Model 3

Generating Textual Video Summaries using Modified Bi-Modal T...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Madkaikar, Siddhesh Belhekar, Vipul Dharmadhikari, Yash Tripathy, Amiya Kumar Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Online video material availability has grown significantly over the past few years, making it challenging for viewers to consume and process information effectively. Video summaries written in text form have shown to be a helpful technique for solving this problem. The key aspects of a video can be accurately and briefly summarised by utilizing methods like video summarising and natural language processing. The purpose of the model is to display a short textual summary of the video to the user before the user watches the complete video. It is frequently difficult and time-consuming for students to locate a suitable educational video. It is occasionally seen on video streaming platforms such as YouTube that the title and the content of the video are not precisely the same. So the model will generate a textual summary of the video and the user can read the summary and get to know about the content of the video. Also, this approach to summarizing videos is helpful in other tasks like video content moderation and could also enable visually-impaired people to consume video material and improve their quality of life. © 2023 IEEE.

关键词： Video recording

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Alumni Management and Networking System 2

Alumni Management and Networking System

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Rajini, S. Hari, Prasad B Upendrasingh, A. Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

The Alumni Management and Networking System aims to address the challenges of alumni disconnection and facilitate effective communication and engagement between educational institutions and their alumni. The system provides a platform for institutes to create profiles, connect with fellow alumni and offers opportunities for alumni to contribute to the institution's success through mentorship, volunteering, and other forms of support. This paper reviews existing research on alumni management systems, highlighting their strengths, limitations, and gaps. It evaluates different approaches such as centralized systems, data collection methods, interactive questionnaires, secure messaging, and tag recommendation systems. The proposed Alumni Network Management System incorporates content-based tagging, utilizing a fine-tuned BERT model trained on a dataset of 10,000 Twitter posts. This approach generates relevant tags for new social media posts, enhancing content visibility and discoverability. Additionally, it employs end-to-end encryption for secure messaging, ensuring privacy and confidentiality. The system's implementation has been tested successfully, showcasing its user-friendliness and effectiveness in managing alumni databases. The results indicate that the system provides a convenient platform for alumni to update their information, stay connected, and engage with their alma mater. © 2023 IEEE.

关键词： Cryptography

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DEF-PIPE: Domain Specific Language Visualization for Big Data Pipelines

DEF-PIPE: Domain Specific Language Visualization for Big Dat...

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2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

作者： Kheirabadi, Amirhossein Layegh Mitrovic, Vlado Dinh, Khoa Chang, Yilin Matskin, Mihhail Royal Institute of Technology Kth Dept. of Computer Science Stockholm Sweden

ISBN: (纸本)9798350361513

The complexity of Big Data analysis requires a combination of different software components into a pipeline performing different analysis steps. Supporting such pipelines requires different expertise provided by different actors: domain experts and technical/computing experts. The main objective of this work is to support domain experts with a tool for pipeline description, which does not require deep technical knowledge about the deployment and execution of Big Data pipelines. We present a solution to visualize Big Data pipeline description using the DEF-PIPE tool. The solution shows that the process of pipeline description is simple and intuitive for users who are not experts in computing. At the same time, DEF-PIPE automatically generates a textual description of the designed data pipelines, which contains the necessary information for simulation, adaptation, deployment, and resource management of the pipeline. In this case, a separation of concerns between the design and run-time phases of the Big Data pipeline lifecycle is supported. This solution allows us to bridge the gap between domain and technical experts. Providing libraries of steps and pipelines also allows the reusing of previously developed solutions. © 2023 IEEE.

关键词： Big data

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model 14

Stock Market Prediction Using Genetic Algorithm Assisted LST...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nikhil, Shreyas Sah, Rahul Kumar Kumar Parki, Santosh Tamang, Til Bikram Somashekhara Reddy, D. Mahesh, T.R. Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values. © 2023 IEEE.

关键词： Genetic algorithms

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Enhanced Unimodal Continuous Authentication Architecture on Smartphones for User Identification through Behavioral Biometrics 2

Enhanced Unimodal Continuous Authentication Architecture on ...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rayani, Praveen Kumar Changder, Suvamoy National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350347982

Nowadays intruders are smart and intelligent to initiate various attacks on the smartphone's entry-point au- thentication methods to access the user's stored information on the smartphone. Accessing users' confidential information rather than personal information by attackers can create a monetary or non-monetary gain. Thus, continuous authentication was proposed on smartphones to recognize the user from the entry point to the end of the session. Even though, we noticed that earlier works on unimodal-based continuous authentication were limited to specific applications on smartphones. This can create a vulnerable gap to access the users' information on the smartphone. Thus, we propose an enhanced unimodal-based continuous authentication architecture for user identification on the smartphone. The proposed approach instantaneously chooses the unimodal behavioral biometrics based on the foreground application of the device and performs the user identification through a machine learning model. The experimental results pro- posed approach show better results than the existing literature. © 2023 IEEE.

关键词： Smartphones

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Tourist Place Classification Using Topic Modeling Algorithms 56

Tourist Place Classification Using Topic Modeling Algorithms

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Wadhe, Apeksha Arun Suratkar, Shraddha S. Vjti Mumbai Dept. of Computer Engineering & Information Technology 400019 India

ISBN: (纸本)9798350315875

Today social media is one of the most powerful and popular tool which is expanding rapidly in the world. Many tourism websites publish tourist places data for describing places in the form of Description. Over this data, Analysis can be performed and meaningful insights can be drawn. By analyzing this data we can find the subject or topic discussed in Description. By performing topic modeling over these data we can predict the category of tourist place. Also topic Modeling can help to cluster textual content into groups as per the subject of the text. In research study, Comparative analysis of topic modeling algorithms has been performed which will find a category of tourist places. Tourist place description blog data was gathered from tourism websites. Using tourist place Description dataset, comparative study of Topic modeling algorithms i.e. LDA Variational Inference(VI) algorithm and LDA Gibbs Sampling(GS) algorithm has been performed. Results of experiments has been analyzed with performance evaluation using various factors such as coherence score, perplexity, training execution time and accuracy percentage. LDA Gibbs Sampling(GS) algorithm outperforms LDA Variational Inference (VI) algorithm in terms of coherence score, perplexity, accuracy percentage. Whereas, LDA Variational Inference(VI) algorithm requires less execution time as compared to the LDA Gibbs Sampling(GS) algorithm © 2023 IEEE.

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