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Comparative Analysis of Python-Based Machine Learning Algorithms for Stock Market Prediction: A Case Study on Forecast Accuracy and Methodological Insights 1

Comparative Analysis of Python-Based Machine Learning Algori...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rathore, Rachna Swetha, B. Saritha, P.S. Kumar, Ajay Singh, Vijay Vir Shyamu Jiwaji University Dept. of Mathematics Gwalior India Meenakshi A.H. Edu. and Research Dept. of Mathematics Chennai India Dhanalakshmi Srinivasan College Dept. of AI and Data Science Coimbatore India Manglayatan University Dept. of Computing and IT Aligarh India Lingayas Vidyapeeth Dept. of Mathematics Faridabad India J.S. University Dept. of Computer Science Shikohabad India

ISBN: (纸本)9798350356816

Predicting stock market movements is a complex task due to the high volatility and unpredictability of stock prices. In recent years, machine learning algorithms have proven to be effective tools for addressing this challenge. This study presents a detailed analysis of various Python-based machine learning techniques applied to stock market prediction. It examines the effectiveness of several algorithms in forecasting stock prices and evaluates their comparative performance. The findings reveal that utilizing machine learning methods can significantly improve the accuracy of stock market predictions. This research offers valuable insights for both researchers and practitioners aiming to leverage machine learning for stock market forecasting. By providing a comprehensive overview of different techniques and their applications, the study serves as a critical resource for those seeking to enhance predictive capabilities and make informed decisions in the dynamic and unpredictable environment of stock market analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Real-Time Stress Level Monitoring Using IOT

Real-Time Stress Level Monitoring Using IOT

引用

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

作者： Asha Patil, Nandini Preeti, B.M. Laxmi Sharnbasva University Dept. of Artificial Intelligence and Data Science Karnataka Kalaburagi India Sharnbasva University Dept. of Computer Science and Engineering Karnataka Kalaburagi India Sharnbasva University Dept. of Electrical and Electronics Engineering Karnataka Kalaburagi India

ISBN: (纸本)9798350315455

Stress is a prevalent and debilitating issue in contemporary society, contributing to a range of physical and mental health problems. As our lives become increasingly fastpaced and interconnected, addressing stress effectively has become imperative. This introduces a pioneering approach to tackle challenge through real-time stress level monitoring utilizing the Internet of Things (IoT). We propose a holistic system that merges IoT sensors, advanced data analytics, and machine learning techniques to provide continuous stress assessment and management solutions. The data is collected through the designed model then preprocessed, and essential features are extracted to construct a predictive model. The heart of the system is a machine learning model, which constantly updates itself based on user-specific stress patterns, yielding highly accurate real-time stress level estimations. Users are provided with actionable feedback and personalized recommendations, ranging from deep breathing exercises to lifestyle adjustments. Extensive experimentation demonstrates the system's robustness, with stress level predictions boasting a mean absolute error of under 5%. © 2023 IEEE.

关键词： data preprocessing feature extraction health monitoring Internet of Things (IoT) machine learning personalized recommendations physiological sensors real-time stress monitoring well-being

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Expert Patient Interaction Language Model (EPILM)

Expert Patient Interaction Language Model (EPILM)

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratham Gala Pooja Vartak Jay Vithlani Naman Chheda Dept. of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Today’s youth encounter numerous mental health challenges, many of which can be effectively addressed through professional intervention. However, there is a notable reluctance among young individuals to seek professional mental health services. The integration of language models in providing mental health support offers a non-intrusive and accessible approach for young people to engage with professional assistance for their mental health concerns. Although existing language models like GPT 3.5, Falcon-7B model have been effective, they also present certain challenges like high dependability of data from sources like Reddit, computational obstacles hindering access, and a preference for certain models over superior alternatives have impeded advancements in this area. that require thorough examination. Issues related to biases and the model’s performance across diverse scenarios underscore the necessity for more intelligent strategies. The proposed method involves enhancing the Falcon-7B model through the application of quantization low rank adaptation (Q-LoRA). This technique reduces the computational requirements and simplifies the accessibility for individuals equipped with the necessary technology while providing the average bertSCORE 85%. By addressing these issues, paper’s objective is to refine mental health support systems to benefit a broader audience.

关键词： Training Adaptation models Quantization (signal) Social networking (online) Computational modeling Graphics processing units Mental health Chatbots data models Tuning

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Designing of Creator's Information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Reinforcement Learning Approach for Prioritized Heterogeneous Object Tracking in UAV Swarms

Deep Reinforcement Learning Approach for Prioritized Heterog...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Nithya, B. Subash, Neethu Vignesh, Hari National Institute of Technology Dept. of Computer Science and Engg Tiruchirappalli India St.Joseph's College Department of Data Science Tiruchirappalli India

ISBN: (纸本)9798350314946

Deep Reinforcement learning(DRL) has recently showcased its boundless potential in Multi-agent collaboration. There exist DRL-based models that strive to unravel the Unmanned aerial vehicle (UAV) Multi-target tracking predicaments, however, the conundrum of tracing desired targets persists as most research endeavors solely focus on the agents' policies while neglecting the dynamic environment, which harbors elusive and unpredictable targets. The proposed model executes Prioritized Heterogeneous Object Tracking (PHOT) using DRL, to furnish a solution for preferred Multi-target tracking by upholding the policy of UAV swarms. Tracking preferred targets assumes paramount importance as these targets necessitate meticulous monitoring within the ever-changing environmental backdrop, particularly during surveillance missions employing UAV swarms, which can harness the full potential of the UAV swarms in real-time. A YOLO v4-based algorithm is put forth, which can trail prioritized targets in a tracking by detection paradigm. To maintain the policy of UAV swarms, a dueling double deep Q-learning algorithm is deployed, complemented by a reward function that stimulates UAVs to track the preferred targets instantaneously. The UAVDT dataset is employed for the simulations, and the results evince that PHOT exhibits robustness, suitable for deployment in a real-time environment, amassing a highly commendable average cumulative reward over time, and enabling actions predicated on unidentical prioritized targets. © 2023 IEEE.

关键词： Reinforcement learning

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Deep Neural Networks in Social Media Forensics: Unveiling Suspicious Patterns and Advancing Investigations on Twitter 3

Deep Neural Networks in Social Media Forensics: Unveiling Su...

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3rd Intelligent Cybersecurity Conference, ICSC 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Alsmirat, Mohammad Isra University Dept. of Data Science and Artificial Intelligence Amman Jordan Isra University Dept.of Software Engineering Amman Jordan University of Sharjah Dept. of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798350382174

Text data forensics, a rapidly developing field that focuses on analyzing textual content to identify criminal or suspicious activities, is becoming increasingly important due to the popularity and the huge number of text posts on social media platforms. This study aims to improve the detection of suspicious text on social media using deep neural networks. Suspicious text is defined as any text that is likely to be associated with criminal activity or is unusual or out of the ordinary. This study could make a significant contribution to the field of text data forensics by helping to improve the detection of such text. We leveraged the 'CIC Truth Seeker dataset 2023' [1], which is widely recognized as a comprehensive and representative dataset for text data forensics research. The dataset contains over 180,000 tweets related to 700 real and 700 fake pieces of news, labeled by experts. In this study, we enhance text data forensics in social media by leveraging the powerful analytical capabilities of deep neural networks. More specifically, we investigate the effectiveness of Long Short-Term Memory (LSTM) in the detection of suspicious text. The results are very promising as we achieved an accuracy of 96% during preliminary evaluations. We plan to explore future work on the model's potential applications, including criminal activity identification, misinformation detection, and online harassment prevention. © 2023 IEEE.

关键词： Sentiment analysis

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Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness 4

Efficient MILP Decomposition in Quantum Computing for ReLU N...

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Franco, Nicola Wollschläger, Tom Poggel, Benedikt Günnemann, Stephan Lorenz, Jeanette Miriam Fraunhofer Institute for Cognitive Systems Iks Munich Germany Technical Univ. of Munich Dept. of Computer Science Munich Data Science Institute Germany

ISBN: (纸本)9798350343236

Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. However, limitations in qubit availability, noise, and errors pose challenges for practical implementation. In this study, we examine two decomposition methods for Mixed-Integer Linear Programming (MILP) designed to reduce the original problem size and utilize available NISQ devices more efficiently. We concentrate on breaking down the original problem into smaller subproblems, which are then solved iteratively using a combined quantum-classical hardware approach. We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods. In our analysis, we show that the number of qubits required to solve Benders is exponentially large in the worst-case, while remains constant for Dantzig-Wolfe. Additionally, we leverage Dantzig-Wolfe decomposition on the use-case of certifying the robustness of ReLU networks. Our experimental results demonstrate that this approach can save up to 90% of qubits compared to existing methods on quantum annealing and gate-based quantum computers. © 2023 IEEE.

关键词： Qubits

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Mukmin, Amirul Adnan Jalil, Abdul Pallawabonang, Mahabintang Prakasa, Esa Yuyun Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Handayani University of Makassar Dept. of computer system Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331508579

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation;however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts. © 2024 IEEE.

关键词： Speech recognition

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Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection

Diffusion Denoised Smoothing for Certified and Adversarial R...

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2023 IJCAI Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning, AISafety-SafeRL 2023

作者： Franco, Nicola Korth, Daniel Lorenz, Jeanette Miriam Roscher, Karsten Günnemann, Stephan Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a given sample is from the training distribution, or is an "Out-Of-Distribution" (OOD) sample. In addition, adversaries can manipulate OOD samples in ways that lead a classifier to make a confident prediction. In this study, we present a novel approach for certifying the robustness of OOD detection within a 2-norm around the input, regardless of network architecture and without the need for specific components or additional training. Further, we improve current techniques for detecting adversarial attacks on OOD samples, while providing high levels of certified and adversarial robustness on in-distribution samples. The average of all OOD detection metrics on CIFAR10/100 shows an increase of-13%/5% relative to previous approaches. Code: https://***/FraunhoferIKS/distro © 2023 CEUR-WS. All rights reserved.

关键词： Machine learning

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HGDL: Heterogeneous Graph Label Distribution Learning 38

HGDL: Heterogeneous Graph Label Distribution Learning

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Yufei Lian, Heng He, Yi Zhu, Xingquan Dept. of Elec. Eng. & Computer Sci. Florida Atlantic University Boca RatonFL33431 United States Dept. of Data Science William & Mary WilliamsburgVA23185 United States

Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification. This paper advances LDL into graph domains and aims to tackle a novel and fundamental heterogeneous graph label distribution learning (HGDL) problem. We argue that the graph heterogeneity reflected on node types, node attributes, and neighborhood structures can impose significant challenges for generalizing LDL onto graphs. To address the challenges, we propose a new learning framework with two key components: 1) proactive graph topology homogenization, and 2) topology and content consistency-aware graph transformer. Specifically, the former learns optimal information aggregation between meta-paths, so that the node heterogeneity can be proactively addressed prior to the succeeding embedding learning;the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, HGDL delivers an end-to-end solution for learning and predicting label distribution for nodes. Both theoretical and empirical studies substantiate the effectiveness of our HGDL approach. Our code and datasets are available at https://***/Listener-Watcher/HGDL. © 2024 Neural information processing systems foundation. All rights reserved.

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