Temporal Data Mining: Uncovering Patterns in Time-Series Data Streams with Machine Learning

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1539 - 1544

Published: Dec. 1, 2023

Temporal data mining is an advanced analytical field that focuses on the extraction of interpretable patterns, correlations, and trends over time within streams. This paper presents a comprehensive framework for uncovering hidden structures in time-series through sophisticated machine learning algorithms. The research introduces novel methodology integrates decomposition, anomaly detection, predictive modeling, leveraging inherent temporal dynamics enhanced intelligence. approach distinguished by its adaptability to real-time streams, robustness against noise, capability handling large-scale datasets prevalent era Big Data. framework's efficacy demonstrated application diverse industry scenarios, including financial markets forecasting, environmental monitoring, Internet Things (IoT) sensor analysis, providing actionable insights with high precision. proposed system employs combination unsupervised supervised techniques, particular emphasis deep models capitalize their ability learn complex representations. further discusses implications findings future potential integration into existing analysis pipelines real-world impact. Methodological advancements practical applications are explored, setting stage new directions analysis.

Language: Английский

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Mining Social Media Data for Sentiment Analysis and Trend Prediction

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1557 - 1562

Published: Dec. 1, 2023

The proliferation of social media platforms has ushered in a deluge user-generated content, encapsulating vast sentiments and trends that shape public discourse. This research endeavors to harness these digital traces through sophisticated data mining techniques predictive analytics distill forecast from datasets. Leveraging state-of-the-art Natural Language Processing (NLP) algorithms, the study develops robust framework systematically identifies, extracts, analyzes affective states opinions embedded within textual data. novel sentiment analysis model proposed here demonstrates significant advancements over traditional lexicon-based machine learning approaches by incorporating contextual embeddings deep architectures, enhancing granularity accuracy classification. Furthermore, paper presents an innovative trend prediction methodology combines time-series with network theory predict emergent topics shifts opinion. is validated extensive experiments on diverse platforms, showcasing its efficacy real-time scenario simulations. implications this work are manifold, providing valuable insights for businesses, policymakers, researchers understanding zeitgeist dynamics. not only contributes academic discourse but also serves as bellwether practical applications market sociopolitical strategizing.

Language: Английский

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Adaptive Signal Processing for Anomaly Detection in Industrial Systems

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1515 - 1520

Published: Dec. 1, 2023

The burgeoning complexity of industrial systems necessitates robust anomaly detection mechanisms to ensure operational integrity and safety. Traditional signal processing techniques often fall short in dynamic environments where characteristics evolve unpredictably. This paper introduces an innovative adaptive framework tailored for systems. proposed methodology synergizes filtering, machine learning algorithms, statistical analysis create a self-tuning architecture. It operates by continuously from the system's data, thus enabling identification subtle emergent anomalies that conventional methods might overlook. core lies its ability adjust new patterns real-time, distinguishing between benign variations genuine threats. A comprehensive evaluation is conducted across various scenarios, demonstrating framework's superior rates compared existing benchmarks. adaptability approach further highlighted through application with limited labeled it successfully leverages unsupervised discern anomalies. results indicate significant advancement early accurate detection, which critical preemptive maintenance risk mitigation operations. research not only contributes novel but also sets standard automated surveillance complex

Language: Английский

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Artificial Intelligence Based Examination of the Field of Ophthalmology

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1617 - 1623

Published: Dec. 1, 2023

Early and correct diagnosis is critical for optimal care prevention of visual loss due to glaucoma, the leading cause permanent blindness globally. New possibilities better glaucoma identification monitoring have emerged progress that has been made in field AI machine learning. Using a unique use both SVM CNN, or support vector machines convolutional neural networks, solve problem., this study automates examination ophthalmological results relevant glaucoma. In paper, we introduce hybrid SVM-CNN algorithm draws from most promising aspects existing these two approaches order lay solid groundwork further investigation, combined first- class classification feature extraction. CNN used refine improve accuracy since it potent deep learning architecture can automatically learn complicated characteristics raw data. The educated on large collection ophthalmic pictures diagnosis, such as fundus photos, examinations eye's acuity OCT scans (optical coherence tomography). Noise removed, contrast increased, format standardized preprocessed photos. presence absence subsequently determined by extraction using model. This study's findings reliable detection glaucoma-related pathology pictures. suggested AI-based methodology various benefits over conventional manual approaches, including increased efficiency, more consistency, possibility earlier detection. It also help ophthalmologists offering automated preliminary assessments, so specialists devote their time energy where needed. concludes promise particular algorithm, revolutionize ophthalmology, particularly treatment. We patient outcomes decrease burden disabling eye illness combining increase efficiency identification. paper highlights significance ongoing research development subject ophthalmology.

Language: Английский

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Leveraging Recurrent Neural Networks for Diabetes Progression Monitoring

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1859 - 1865

Published: Dec. 1, 2023

This study investigates the use of recurrent neural networks, commonly referred to as RNNs, in tracking development diabetes with goal revolutionizing tailored medical intervention. A deductive strategy is used, supported by a descriptive research methodology, and based on an interpretivist theoretical framework. An RNN-based model created trained identify temporal relationships data utilizing secondary sources, such persistent records patients, medication pasts aspects lifestyle. The regression network (RNN) outperforms traditional monitoring techniques its ability forecast long-term well short-term patterns course diabetes. Comparative analysis demonstrates superiority conventional methods potential revolutionize treatment combination several including comorbidities lifestyle factors, considerably improves prediction accuracy offers more complex picture how diseases develop. model's efficiency practical problems healthcare settings has been validated through clinical studies. Patients who approach benefit from better glucose control greater managing one's own confidence. Clinicians report improved patient outcomes faster decision-making procedures. significance meticulous verification comprehension highlighted critical analysis. incorporation multimodal dynamic adaptability provided RNN method, focused patients' are suggested areas for further investigation. revolutionary method advancement must still be advanced while taking ethical legal factors into account.

Language: Английский

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Identifying Early Biomarkers of Multiple Sclerosis with Feature Selection Algorithms

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1853 - 1858

Published: Dec. 1, 2023

The project has done literature used to recognize the early biomarkers of Multiple Sclerosis with several feature selection algorithms using ML. It also explained critical assessment is focused here on use algorithms. discussed research that mainly focuses understanding complex neurological disorders and it encompasses clinical presentations a diverse range symptoms by making detection intervention for long-term scheme patients' well-being. applied sophisticated choosing features varied dataset made up medical, genetic, in addition, neuroimaging scans allowed researchers find discriminatory disorder multiple sclerosis (MS). biomarkers, journal article, knowledge, data collection, model development

Language: Английский

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Novel Signal Processing Techniques for Non-Invasive Brain-Computer Interfaces

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1503 - 1508

Published: Dec. 1, 2023

The burgeoning field of Brain-Computer Interfaces (BCIs) holds immense potential for revolutionizing human-computer interaction, particularly through non-invasive methodologies. This paper introduces innovative signal processing techniques aimed at enhancing the performance, accuracy, and reliability BCIs. Traditional methods often grapple with inherent challenges posed by low signal-to-noise ratio susceptibility to artifacts in electroencephalographic (EEG) data. To address these issues, proposed leverage advanced machine learning algorithms sophisticated decomposition extract interpret neural signals unprecedented precision. A comprehensive evaluation is conducted using a diverse dataset, encompassing various cognitive states tasks. results demonstrate marked improvement classification interpretation outperforming existing establishing new benchmark Furthermore, delves into implications advancements real-world applications, including neurorehabilitation, assistive technologies, interaction. By pushing boundaries what possible BCIs, this research paves way more intuitive, responsive, reliable brain-computer interfaces, ultimately fostering seamless integration technology everyday life.

Language: Английский

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Multi-Modal Signal Fusion: Enhancing Speech Recognition in Noisy Environments

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1509 - 1514

Published: Dec. 1, 2023

In the realm of automated speech recognition (ASR), robustness systems operating within noisy environments remains a pivotal challenge. This paper introduces an innovative approach to multi-modal signal fusion, aimed at enhancing intelligibility and accuracy ASR in acoustically adverse settings. By integrating auditory visual streams, proposed framework leverages complementary strengths each modality. A novel fusion algorithm is presented, which employs deep neural networks synchronize process disparate types, effectively reducing impact ambient noise. The component utilizes dynamic lip movement patterns, while aspect applies advanced noise suppression techniques, including spectral subtraction beamforming. further refined through application cross-modal attention mechanism, dynamically adjusts contribution modality real-time, based on contextual characteristics. Extensive evaluations conducted various scenarios demonstrate significant improvement word rates compared traditional single-modality systems. findings suggest that not only enhances resilience against environmental but also paves way for more natural human-computer interaction realworld applications.

Language: Английский

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Preserving Privacy in Big Data Analytics: A Differential Privacy Approach in Cyber Physical System

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1533 - 1538

Published: Dec. 1, 2023

The burgeoning growth of Big Data in cyber-physical systems (CPS) has precipitated an imperative for robust privacy-preserving mechanisms. This paper introduces a novel framework big data analytics within CPS that employs differential privacy as its cornerstone. Differential provides quantifiable approach to ensure the individual entries is protected while still permitting aggregate be analyzed. By integrating this methodology into CPS, proposed addresses dichotomy utility and privacy. research delineates application techniques variety mining tasks specific such real-time monitoring predictive maintenance, maintaining fidelity analysis. Furthermore, evaluated against several metrics reflect privacy-utility trade-off, demonstrating it significantly mitigates risk breaches. adaptability showcased through diverse scenarios, emphasizing potential widespread adoption. advances discourse on by presenting solution balances competing needs utility, ensuring can leverage full without compromising

Language: Английский

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Quantum Signal Processing: A New Frontier in Information Processing

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1527 - 1532

Published: Dec. 1, 2023

Quantum Signal Processing (QSP) emerges as a groundbreaking paradigm, exploiting the principles of quantum mechanics to revolutionize analysis, manipulation, and interpretation signals. This paper introduces novel framework for QSP, delineating its theoretical foundations potential surpass classical signal processing capabilities. The research delves into development algorithms that exhibit superior efficiency in frequency domain analysis temporally entangled structures. A pivotal aspect this work is introduction Fourier transforms mechanism achieve exponential speed-ups decomposition. Furthermore, explores implementation error correction techniques enhance robustness presence noise decoherence. practicality QSP demonstrated through simulated circuits, providing blueprint future computing hardware applications. implications are profound, suggesting transformative impact on fields ranging from secure communications biomedical imaging. By harnessing entanglement superposition properties inherent systems, poised redefine limits what computationally feasible within information processing.

Language: Английский

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