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Financial Abnormal data Monitoring and Analysis Algorithm Based on data Mining and Neural Network 2

Financial Abnormal Data Monitoring and Analysis Algorithm Ba...

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2nd ieee international conference on advanced Technologies in intelligent Control, Environment, computing and Communication Engineering, ICATIECE 2022

作者： Jun'E, He Yunnan University of Business Management Kunming City China

ISBN: (纸本)9781665493963

The monitoring and analysis algorithm of financial abnormal data based on data mining and neural network is a machine learning algorithm to monitor the abnormal data of financial market. It also uses historical stock price data to analyze the performance of various trading strategies to determine whether any strategy is better or lower than its benchmark. These algorithms are intended to be used as early warning systems for potential security problems in financial markets, such as fraud, insider trading, manipulation or other illegal activities. This technology uses AI to identify unusual trading patterns in the market, so that traders can make better decisions. What are the types of financial abnormal data monitoring and analysis algorithms based on data mining? There are two kinds of financial anomaly data monitoring and analysis algorithms based on data Mining: Time series anomaly detection and event based anomaly detection. Financial anomaly detection (fade) is a term that describes an automated process designed to detect abnormal patterns in financial market activities. © 2022 ieee.

关键词： data mining

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Air Quality Sensor data Collection and Analytics With IoT for an Apartment With Mechanical Ventilation 11

Air Quality Sensor Data Collection and Analytics With IoT fo...

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11th ieee international conference on intelligent data acquisition and advanced computing systems - Technology and Applications (IDAACS)

作者： Scislo, Lukasz Szczepanik-Scislo, Nina Cracow Univ Technol Fac Elect & Comp Engn Warszawska 24 PL-31155 Krakow Poland Cracow Univ Technol Fac Environm & Power Engn Warszawska 24 PL-31155 Krakow Poland

ISBN: (纸本)9781665426053

The aim of the research was to develop a concept of a remote measurement system for air quality management using IoT for an apartment with a mechanical exhaust system. The constant monitoring system with the possibility of manual control by the occupants allows teaching the occupants how to chose optimal settings with the biggest impact on the air quality. Additionally, using cloud data analysis, the measurements can be compared with simulations performed before the building construction. This allows choosing the proper apartment depending on the foreseen occupancy schedule and the family size.

关键词： air quality variable air volume VAV cooling and ventilation control strategy

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Assessing the Potential of Google Location History (GLH) data for Travel Behavior Research in the Context of Developing Country 27

Assessing the Potential of Google Location History (GLH) Dat...

引用

27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Hamid, Kaiser Noor, Md. Sayem Enam, Annesha Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh

ISBN: (纸本)9798331505929

Using passive data to investigate travel behavior is becoming increasingly prevalent, owing to its convenient data acquisition process. This study seeks to evaluate the feasibility of leveraging Google Location History (GLH) data for analyzing travel behavior within the context of a developing nation like Bangladesh, characterized by high population density, diverse land use, and heterogeneous traffic patterns, including a significant presence of non-motorized vehicles, and relatively low motorized vehicle speeds. A group of 60 individuals willing to share their GLH data stored in the Google Maps application was recruited to accomplish this. A dedicated mobile phone application named Trip Tracker was developed to facilitate the collection of ground truth data. Validation of the GLH data was carried out through a three-step procedure. Initially, the identification of home and work locations from GLH, based on visit frequency and duration, was cross-verified against user-provided inputs, demonstrating 100% accuracy. Subsequently, the accuracy of day-to-day travel data, including arrival and departure times and locations, was assessed against GLH information, yielding a spatial and temporal matching accuracy of 82%. Thirdly, the modes of transportation extracted from ground truth data were compared with those provided by GLH, revealing a mode prediction accuracy of 53 % for GLH data. This discrepancy was attributed to the intricate nature of Dhaka's traffic system and the prevalence of non-motorized transportation modes like rickshaws. Additionally, GLH tends to aggregate multimodal trips, revealing only the high-speed mode and neglecting the mode(s) used for the last/first-mile connection. Consequently, two predictive models were developed utilizing Random Forest (RF), a tree-based machine learning (ML) algorithm, and a long short-term memory neural network (LSTM-based NN) to refine the GLH-predicted travel mode information. The RF and LSTM models achieved mode predic

关键词： Nonmotorized transportation

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Model Privacy Preservation in Distributed systems through Machine Learning

Model Privacy Preservation in Distributed Systems through Ma...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Naga Nymisha, Tummala Venkata Nanda Krishna, Ch. Mahita, Kantamneni Department of Information Technology Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350319200

The increasing integration of machine learning models into distributed systems raises a pressing concern: how to safeguard privacy of the data and secure the model during sharing. In response to the challenge, a robust solution was proposed. The project's central problem is ensuring the privacy and security of machine learning models within distributed environments. To solve the issue, a machine learning based model is developed for predictive tasks while emphasizing the paramount importance of data privacy and security. With dataset selection, a k-nearest neighbors (KNN) model was constructed. For secure sharing of the model across distributed systems, the advanced Encryption Standard (AES) algorithm was used to encrypt the model feature and dataset. AES, being a symmetric key algorithm, necessitates secure encryption key exchange. The Rivest-Shamir-Adleman (RSA) algorithm is used to solve the problem, an asymmetric key system with public and private keys. The AES encryption key is securely transmitted via the recipient's public key encryption. Upon reception, decryption enables the reconstruction and utilization of the machine learning based model for further predictions. The project offers a comprehensive solution to the overarching problem of preserving data privacy and model security in distributed systems. By combining various encryption algorithms, the confidentiality and integrity of sensitive machine-learning models within distributed environments were ensured. © 2023 ieee.

关键词： AES encryption data privacy Distributed systems KNN model RSA encryption Secure model sharing

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Structured Compressive Sensing Based Joint User Activity and data Detection for Grant-Free mMTC 9

Structured Compressive Sensing Based Joint User Activity and...

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9th international conference on intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Yuanyuan Luo, Yongtian Xu, Jin Liu, Weihua Huang, Lixun Zhang, Zhe College of Electronic Information Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9798350376548

This work deals with the active user and data detection problem in the uplink of grant-free massive machine-type communication (mMTC) systems. We formulate joint active user and data detection as a problem of equivalent sparse signal recovery problem represented by the multiple measurement vector (MMV) model. To address this issue, we develop a Sparse Pre-estimation Subspace Pursuit (SPeSP) algorithm based on greedy search. First, the algorithm employs variable step-size matching pursuit to estimate the signal sparsity, setting the initial selection step size to a small value and gradually increasing it to approximate the true sparsity. Then, by utilizing subspace tracking, the algorithm reduces reconstruction errors and progressively updates the number of atoms to the estimated sparsity, thereby enhancing the reconstruction performance of sparse signals. Simulation results demonstrate that the SPeSP algorithm outperforms traditional Sparse Adaptive Matching Pursuit algorithms in terms of active user detection rate and signal reconstruction performance. © 2024 ieee.

关键词： Compressed sensing

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ResuMatcher: An intelligent Resume Ranking System 3

ResuMatcher: An Intelligent Resume Ranking System

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dhobale, Prasad Bhoir, Vinayak Vyavhare, Abhijeet Yelkar, Pratik Dharmadhikari, Pramod A. School of Computer Engineering MIT Academy of Engineering Alandi Pune India

ISBN: (纸本)9798331527549

The growing complexity of recruitment processes necessitates advanced solutions for resume screening and ranking. In this paper, we present ResuMatcher, an AI-powered resume ranking system that leverages the power of Large Language Models (LLMs) for semantic understanding. Unlike traditional keyword-based systems, ResuMatcher evaluates the context and meaning behind terms in resumes and job descriptions, providing a more accurate and efficient matching process. Using models such as BERT and transformer-based architectures, ResuMatcher can assess candidate qualifications by considering the underlying semantics of job requirements and candidate skills. The system was evaluated on a diverse dataset across industries such as IT, healthcare, and finance, and showed a significant improvement in both accuracy and efficiency compared to conventional Applicant Tracking systems (ATS). Results from user testing demonstrated high satisfaction rates among both job seekers and recruiters, highlighting ease of use, relevance of matches, and efficiency gains in the hiring process. © 2025 ieee.

关键词： Semantics

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An Integrated Development Of IoT Based Machine Learning For data-Driven Travel Recommendations

An Integrated Development Of IoT Based Machine Learning For ...

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Rifath, Shakeel Sridhar Xavier, Constance Brindasri, S. Hindustan Institute Of Technology And Science Computer Science And Engineering Department Chennai India

ISBN: (纸本)9798350364828

An innovative methodology is introduced in this Research to enhance data-driven travel decision-making by integrating IoT technology and data science techniques. With a specific focus on air and sound pollution, the research utilizes real-time sensor data from various urban sources to develop predictive models for forecasting future pollution levels. These models serve as the backbone for decision support tools seamlessly integrated into open-source platforms like ThingSpeak and IFTTT, facilitating proactive travel planning for both the general public and patients. The core of this methodology lies in the real-time aggregation of IoT sensor data, offering users comprehensive insights into prevailing air pollution levels at their intended destinations. Augmented by predictive machine learning algorithms such as logistic regression and decision trees, the system's real-time monitoring capabilities are significantly enhanced, with the decision tree algorithm particularly noted for its accuracy in assessing destination safety for patients. The forecasting accuracy and reliability of the model are upheld through rigorous training on historical data. Notably, the synergy between ThingSpeak and IFTTT enables the delivery of personalized travel recommendations grounded in predictive analytics and real-time environmental monitoring. This research underscores the pivotal role of data-driven decision-making in travel planning, empowering patients with tailored guidance derived from robust data analysis. © 2024 ieee.

关键词： Predictive analytics

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Revolutionizing School Transportation Safety with Real-Time data and advanced Sensors 2

Revolutionizing School Transportation Safety with Real-Time ...

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2nd international conference on intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Deepak, F. X. Edwin Anitha, P. Kumar, K. Karthik Kamaraja, A. S. Natl Engn Coll Dept Elect & Elect Engn Kovilpatti Tamil Nadu India Univ VOC Coll Engn Dept Elect & Elect Engn Tuticorin Tamil Nadu India Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Krishnankoil Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Ensuring student safety during school transportation demands a reliable and secure travel service. The proposed system revolutionizes this by providing real-time data on critical bus parameters such as location, speed, and passenger data. Using an ESP32 microcontroller, RFID, and GPS sensors, information is transmitted to a remote server via WiFi. The NEO-6M GPS module ensures precise tracking of the bus location and speed, while the MFRC522 RFID reader accurately identifies students boarding and deboarding. Additionally, sensors like K-0135 liquid level sensor and MQ-2 gas sensor are used to monitor fuel levels and engine emissions, respectively by enhancing the operational safety. The obtained data is then uploaded to a web server database for enabling centralized management and analysis. Parents can access this information via a mobile application, ensuring peace of mind as they track their children's commute in real-time. School management can also utilize the system to proactively intervene in emergencies or deviations from prescribed routes, further enhancing student safety. The proposed solution represents a paradigm shift in school transportation safety, leveraging advanced sensor technologies and seamless connectivity to ensure the utmost protection for students with seamless connectivity to deliver unparalleled peace of mind to parents, administrators, and drivers.

关键词： Vehicle Monitoring ESP32 GPS IoT RFID

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Research on Fault Diagnosis of Spacecraft Control System Based on Graph Neural Network 8

Research on Fault Diagnosis of Spacecraft Control System Bas...

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8th international conference on intelligent computing and Signal Processing, ICSP 2023

作者： Ning, Zhicheng Liu, Xiaoxiang Li, Xinyuan Liu, Jun Beijing Insitute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China Beijing University of Posts and Telecommunications Intelligent Perception and Computing Research Center Beijing China

ISBN: (纸本)9798350302455

The fault diagnosis technology of spacecraft control systems is a crucial component of spacecraft health management. Traditional fault diagnosis technologies, such as model-based diagnosis and signal processing, are relatively mature but are limited in their ability to fully utilize information in big data environments. Neural network-based fault diagnosis methods often overlook the sequential nature of spacecraft data as time series data. Given that the state of a spacecraft at any given time is closely related to preceding data, it is possible to conduct fault diagnosis by extracting relevant features from fault sample data. In this paper, we propose a graph neural network-based fault diagnosis method for spacecraft control systems. To address the issue of limited fault samples, we construct a spacecraft control system model and generate various types of gyro and flywheel fault data through fault injection. Utilizing the Graphstar model - a powerful graph neural network with strong information mining capabilities - we transform one-dimensional fault data into graph data with spatial structure and extract relevant characteristics through deep learning to diagnose faults. Simulation results demonstrate that our method can effectively diagnose faults with high accuracy. © 2023 ieee.

关键词： Failure analysis

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A Mixed-Methods Study on Self-Management Support: Exploring Patient and Healthcare Professional Perspectives Alongside a Health Condition Recommender System 4

A Mixed-Methods Study on Self-Management Support: Exploring ...

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4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Srivani, N. Alekhya, Reminisetti Rohith, Vankayalapati Indira, Uppalapu Gopichand, Tanuri Sr Gudlavalleru Engineering College Dept. of Cse Andhra Pradesh Gudlavalleru521356 India

ISBN: (纸本)9798350349856

Enabling individuals with asthma to proactively identify signs of worsening condition and respond promptly is pivotal in mitigating the risk of asthma attacks. The fusion of smartphones, intelligent monitoring tools, and machine learning holds the potential to amplify self-management capabilities by anticipatively predicting asthma attacks and delivering personalized guidance. In this pursuit, our objective is to conceptualize and evaluate the viability of an asthma attack prediction system rooted in data harnessed from diverse smart devices. This approach presents a comprehensive mixed-methods study that investigates patient self-management support by integrating insights from patients and healthcare professionals, alongside the implementation of an IoT device, cloud infrastructure, and deep learning *** study aims to understand the challenges and needs of patient self-management while leveraging contemporary technological advancements. Through qualitative interviews, focus groups, surveys, and experiments, the study captures diverse perspectives and evaluates the effectiveness of a health condition recommender system. The integration of an IoT device enables real-time data collection, which is processed using cloud infrastructure and deep learning models to provide personalized recommendations for self-management. The study's findings shed light on enhancing patient self-management through a multidimensional approach that combines human insights with cutting-edge technology. © 2024 ieee.

关键词： Recommender systems

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