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Decoding Financial Markets: Unleashing the Power of Bi-LSTM in Sentiment Analysis for Cutting-Edge Stock Price Prediction

Decoding Financial Markets: Unleashing the Power of Bi-LSTM ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Aluvala, Srinivas Balamurugan, V. Jeyasudha, J. Kumar, R. Dinesh Pradeep Kumar, S. Sr University School of Computer Science & Artificial Intelligence Warangal India Sathyabama Institute of Science and Technology Department of Ece Chennai India Srm Insitute of Science and Technology Department of Computational Intelligence Chennai India Saveetha School of Engineering Department of Ece Thandalam India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Predicting stock prices accurately is a challenging task due to various influencing factors. This paper addresses the need for precise stock price prediction by presenting an enhanced sentiment analysis methodology using deep learning, particularly focusing on the Bi-LSTM model. The challenge of accurate sentiment analysis in stock prediction is overcome through advanced data preprocessing techniques tailored to maintain sentiment context effectively. The proposed Bi-LSTM (Bidirectional long Short-Term Memory) and CNN (Convolutional Neural Network) model demonstrates superior performance in classifying sentiments with high precision, recall, F1-score, and accuracy. The numerical validation demonstrates the effectiveness of the Bi-LSTM model in achieving a balance across various metrics, highlighting its potential for practical implementation in stock market prediction. The Bi-LSTM and CNN model achieves remarkable precision 90.00%, recall 90.00%, F1-score 90.00%, and accuracy 91.00%. In comparison to established methods, such as Multivariate Sequential Long Short-Term Memory Autoencoder (MSLSTMA), long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), the Bidirectional long Short-Term Memory (Bi-LSTM) model exhibits its competitive edge in stock price prediction as supported by evaluation metrics. The results emphasize the importance of tailored data preprocessing and deep learning techniques, showcasing the efficiency of the proposed methodology in enhancing stock price prediction accuracy while maintaining high percentages of precision, recall, and F1-score, along with impressive accuracy. © 2023 IEEE.

关键词： Bidirectional long Short-Term Memory Convolutional Neural Network Deep Learning Sentiment Analysis Stock Price Prediction

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Combine deep learning and artificial intelligence to optimize the application path of digital image processing technology 24

Combine deep learning and artificial intelligence to optimiz...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Pan, Linying Xu, Jingyu Sun, Wenjian Wan, Weixiang Zeng, Qiang Information Studies Trine university PhoenixAZ85258 United States Computer Information Technology Northern Arizona University FlagstaffAZ86011 United States Electronic and information engineering Yantai University Shandong Province Yantai264005 China Electronics & Communication Engineering University of Electronic Science and Technology of China ChengDu611731 China Computer Technology Zhejiang University Zhejiang Hangzhou310058 China

ISBN: (纸本)9798400717147

Artificial intelligence provides a new research concept for digital image processing. However, at present, artificial intelligence is rarely introduced into the teaching of digital image processing in colleges and universities, and there are problems such as obsolete teaching content, single teaching method and simple course experiment, which affect the teaching effect and are not conducive to the cultivation of comprehensive and innovative talents. Digital image processing technology brings more possibilities to communication engineering and makes people's communication more convenient. For example, video calls and photo transmission make people's communication methods in daily life more and more diversified. The limitation of time and space allows people to meet online, creating more communication possibilities. However, there are still many problems and methods worthy of in-depth exploration. Therefore, this paper has a comprehensive understanding and mastery of the traditional methods and deep learning methods of digital image processing, so as to improve the relevant project practice and scientific research exploration ability, and make reference for similar research conclusions. © 2024 ACM.

关键词： Image enhancement

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Enhanced Reversible Image Watermarking using Improved Moth Flame Optimization Algorithm

Enhanced Reversible Image Watermarking using Improved Moth F...

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2023 International Conference on Integrated Intelligence and communication Systems, ICIICS 2023

作者： Pradeep Kumar, S. Almoussawi, Zainab Abed Sunil, G. Ananda Babu, J. Almusawi, Muntather Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Ahl Al Bayt University College of Islamic Science Karbala Iraq SR University School of Computer Science & Artificial Intelligence Warangal India Malnad College of Engineering Department of Ise Hassan India The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

With enhancing connectivity, undefended digital content is susceptible for attacks and hence watermarking is significant to protect copyright. In this research, an efficient image reversible watermarking method is proposed. The proposed method uses selection of optimum position for embedding in accordance with entropy and relevance threshold for selection of entropy is done through Particle Swarm Optimization (PSO) algorithm. A method in Integer Wavelet Transform (IWT) is implemented, next Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) is integrated to the process of embedding in whole selected blocks and Fractal encrypted watermark are combined to image coefficients utilizing mean proximity coefficient. The process of embedding is done through optimum scale that is produced through Improved Moth-Flame Optimization (IMFO) algorithm in accordance with fitness function. The proposed method attained superior image quality and high embedding capacity. The performance of proposed method id evaluated with performance measures of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Normalized Correlation (NC). The proposed method attained high PSNR of 60.32 dB, SSIM of 0.9948 and NC of 0.9872 in Lena image which is superior than other existing algorithms. © 2023 IEEE.

关键词： Discrete Cosine Transform Improved Moth Flame Optimization Integer Wavelet Transform Particle Swarm Optimization and Reversible Watermarking

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Border Detection for Fishermen Using IoT

Border Detection for Fishermen Using IoT

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Divya B K Vijayalakshmi Alivn Ancy A Priyadharshini J NaveenKrishna B B Harshavardhan R Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai Institute of Technology Dept of Computer Science and Engineering Aarupadai Veedu Institute of Technology (Vinayaka Mission Research Foundation) Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

At present, the marine security issue includes the protection of fishers and address the rescue in case of illegal border infiltration. To detect the border for fisherman, this study conducted a method of enhancement in border detection essentially utilising the Automatic Identification System (AIS) with the assistance of the Internet of Things (IoT). In the middle sea, to provide boat detection with high precision, the proposed model incorporates Very High Frequency (VHF) communication inspired by X-band radar. The system is able to detect and monitor the boats at sea. To provide the middle sea with a high level of detection and monitoring of boats, system architecture included VHF (Very High Frequency) communication, which was inspired by the X band radar system. The range of AIS was increased using VHF communication. The coastal monitoring stations receive the position and status data from fishing boats with the help of IoT devices. The new AIS technology includes IoT-enabled devices installed on fishing boats. The IoT devices situated on boats send information to coastal monitoring station after sending data. The advanced technology increases the software capabilities of AIS, which will be helpful in the situation where the only AIS information was not enough. The advanced software capabilities improved the range of AIS and the definition capability of the vessels which were identified in the ocean near borders. The Automatic Identification System (AIS) that is being used in the present day has been upgraded and the Internet of Things (IoT) has been introduced. As a result, the system is now capable of much more comprehensive and real-time tracking and monitoring, as well as communication for marine vessels. This combination of technologies strengthens the safety, efficiency, and management of maritime operations by giving ships the Opportunity to connect and exchange data with different sensors, devices, and networks, and thus offering a more detailed and dynam

关键词： Boats Radar detection Sea measurements Radar Real-time systems Software Internet of Things Security Artificial intelligence Monitoring

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Pioneering Pediatric Health - Machine Learning Ensembles for Appendicitis Detection

Pioneering Pediatric Health - Machine Learning Ensembles for...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tejasvin Vimal J M Amruth Ganesh S Yougesh Kumar S Abhishek T Anjali Department of Electronics and Communication Engineering Amrita School of Engineering Amritapuri India Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Children’s appendicitis is often diagnosed using outdated techniques that may not be as quick or accurate as they need to be. The present investigation employs sophisticated machine learning (ML) methodologies to mitigate these constraints and enhance the diagnostic results. We investigated the effectiveness of many ensemble machine learning (ML) models, such as Gradient Boosting Machine (GBM), CatBoost, LightGBM, AdaBoost, and Decision Trees, using a proprietary dataset that contains a diverse range of pediatric appendicitis cases. After extensive training and validation, these models showed amazing accuracy and precision rates—the best models reaching up to 99% accuracy. In addition to outlining each model’s effectiveness, our comparison study clarifies which models are applicable in clinical contexts. This research work addresses how these models might improve clinical processes, lower diagnostic mistake rates, and speed up and improve the accuracy of clinical decisionmaking. The findings of this study demonstrate the revolutionary potential of machine learning to improve pediatric healthcare by offering reliable instruments for intricate medical diagnosis.

关键词： Training Measurement Accuracy Machine learning Boosting Decision trees Reliability Medical diagnosis Medical diagnostic imaging Context modeling

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Machine Learning for Signal Processing in communication Systems

Machine Learning for Signal Processing in Communication Syst...

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Innovative Computing, Intelligent communication and Smart Electrical Systems (ICSES), International Conference on

作者： PoliLokeshwara Reddy Chanda Shreyas Pamulapati. Bhargavi Raja G V D. Leela Rani S. Maheswari Department of Electronics and Communication Engineering Anurag University Hyderabad Telangana India Department of Computer Science Engineering KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bengaluru Karnataka Department of Electronics and Communication Engineering Mohan Babu University Tirupati Andhra Pradesh India Department of Electrical and Electronic Engineering Kongu Engineering College Perundurai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The traditional styles of signal processing are passing constraints in their capability to handle the different and dynamic character of ultramodern data transfers. This is because communication systems are getting increasingly complicated. The operation of machine literacy presents a potentially useful answer since it enables data-driven, adaptive approaches that have the eventuality to ameliorate signal processing performance. This composition provides a comprehensive examination of a variety of machine literacy ways and their operations in important disciplines such as modulation recognition, noise reduction, channel estimation, and signal discovery. The study demonstrates how machine literacy ways may vastly increase the effectiveness, delicacy, and robustness of signal-processing tasks in communication systems. This is fulfilled through detailed analysis and comparison. With the help of machine literacy, unborn communication systems will be suitable to attain advanced situations of robotization and intelligence, which will pave the way for further developments in areas similar to wireless dispatches, the Internet of Effects(IoT), and other areas. In light of these findings, it's clear that machine literacy can fully transfigure assiduity by furnishing new shoes and approaches to the optimization of signal processing in the unborn generation of communication networks.

关键词： Wireless communication communication systems Computational modeling Heuristic algorithms Noise reduction Modulation Channel estimation Signal processing algorithms Machine learning Robustness

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Flood Risk Assesment System using Logistic Regression

Flood Risk Assesment System using Logistic Regression

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International conference of electronics, communication and Aerospace Technology (ICECA)

作者： P Manikandan S Tanmaya Sai Vivek M Thejaswi B Tejaswini Sykam Manoj P Tejaswar Reddy Department of Electronics and Communication Engineering Kalasalingam Academy ofResearch and Education Virudhunagar India Department of Computer Science and Engineering Kalasalingam Academy ofResearch and Education Virudhunagar India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

One of the most frequent natural disasters in the world, flooding affects millions of people annually and causes significant economic losses as well as human tragedies, with India bearing a disproportionate amount of the burden. Reducing these effects requires effective early warning systems, yet many vulnerable communities still struggle to make use of them. Scientists and programmers are using advances in machine learning (ML) and artificial intelligence (AI) to bridge this gap by creating a trustworthy flood prediction system. This state-of-the-art system aims to offer more cost-effective choices that perform better at predicting floods caused by precipitation. This prediction system’s primary job is to forecast potential flood events by utilizing previous rainfall data to create predictive models. The model looks at rainfall patterns specific to different locations to determine the likelihood of a flood event. Its growth depends on the utilization of extensive district-level rainfall information from India, which serve as the foundation for training and enhancing the prediction models. A range of machine learning (ML) techniques, such as XGBoost and K-Nearest Neighbors (KNN), are used to train predictive algorithms. These algorithms were chosen because of their ability to handle complex datasets and extract meaningful patterns that relate the amount of rainfall with the probability of floods. Through recurrent training and validation against past flood events, the models are improved to reliably determine whether a region is in danger of flooding based on measured rainfall amounts.

关键词： Training Logistic regression Rain Machine learning algorithms Weather forecasting Machine learning Predictive models Nearest neighbor methods Prediction algorithms Floods

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Cultural Intelligence in AI Ethics: A Systematic Framework for Implementation Across Global Contexts

Cultural Intelligence in AI Ethics: A Systematic Framework f...

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International Symposium on Advanced Computing and communication (ISACC)

作者： Devesh Kumar Kamlesh Kumar Rahul Kumar Choudhary Varnika Singh Dept. of Computer Science & Engineering NIT Hamirpur India Dept. of Electrical Engineering NIT Hamirpur India Dept. of Electronics & Communication Engineering UIET Panjab University Chandigarh India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research examines artificial intelligence ethics implementation across diverse cultural contexts through case studies in healthcare, finance, education, and corporate sectors. Using mixed-methods analysis across six cultural regions, the study reveals fundamental variations in approaches to AI ethics, particularly regarding data privacy and human-technology relationships. The integration of Western, Eastern, and African perspectives yields three frameworks: a cultural intelligence model for AI development, an adaptive governance mechanism, and a cross-cultural collaboration approach. Results demonstrate that successful AI implementation requires balancing diverse cultural perspectives with technical innovation through flexible governance structures, providing practical pathways for culturally sensitive AI development.

关键词： Measurement Ethics Technological innovation Systematics Standards organizations Medical services Cultural differences Global communication Stakeholders Artificial intelligence

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Identification of Tumor using Segmentation & Object Detection: A Comparison and an Analysis

Identification of Tumor using Segmentation & Object Detectio...

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International Conference on Computing communication Control and Automation (ICCUBEA)

作者： Debabrata Swain Ashwini Ramanuj Vaishvi Shah Manish Kumar Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India Department of Electronics and Communication Engineering Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Abnormal cells proliferate in the brain to form brain tumors, which frequently cause death. Localising and segmenting brain tumors using MRI is a difficult task in medical analysis, yet it is necessary for many applications. Accurate segmentation of brain tumor images is required for the identification and treatment of patients with these tumors. Even though they achieved promising segmentation results on the BRATS 2020 dataset, several of them need more testing and training time due to their complicated structure. In order to help doctors identify tumors in their early stages with high accuracy, we have proposed a two-step deep learning method for the diagnosis of three different types of tumors: necrotic, edema, enhancing, as well as brains free of tumors, using MRI images. The metrics used for evaluating the performance of these models are intersection over union, recall, and precision. The research indicates the precision of YOLO model to be 0.97 showcasing the accurate tumor detections, while recall and IoU of UNet segmentation to be 0.99 and 0.83 respectively. In this study, we addressed the issue of inaccuracy and latency in current artificial diagnosis systems by proposing a robust method for classifying brain tumors using deep learning techniques.

关键词： YOLO Deep learning Image segmentation Analytical models Accuracy Computational modeling Magnetic resonance imaging Malignant tumors Brain modeling Medical diagnostic imaging

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Investigation on Efficient Machine Learning Algorithm for DDoS Attack Detection

Investigation on Efficient Machine Learning Algorithm for DD...

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International Conference on computer, Electrical & communication engineering (ICCECE)

作者： R. Sahila Devi R. Bharathi P. Krishna Kumar Department of Computer Science and Engineering Rohini College of Engineering and Technology Palkulam India Department of Electronics and Communication Engineering University College of Engineering Konam India Department of Computer Science and Engineering Amrita College of Engineering and Technology Erachakulam India

Internet of Things (IOT) is a general term for all interconnected devices as well as the technology that enables object-to-object and cloud-to-object communication. However, there are several regular and dangerous threats to the development of this technology. The Distributed DoS (DDOS) attacks are extremely innovative and complex, making them almost inevitable to detect by the existing technology or detection system. Due to their complexity and difficulty, novel types of DDoS attacks are practically impossible for intrusion detection systems to detect or mitigate. Effective DDoS traffic detection is made feasible by Machine Learning (ML) technologies. In this paper, the popular ML methods were tested on the CICDoS2019 dataset to determine the most effective one for DDoS detection. A hybrid MLDDoS detection approach using estimator functions is also proposed. The framework for multi-classifying different DDoS attack types can be improved in future research, and a hybrid algorithm can be tested using updated datasets for DDoS attacks.

关键词： Machine learning algorithms Stochastic processes Support vector machine classification Intrusion detection Telecommunication traffic Machine learning Denial-of-service attack

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