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SM2PNet: A Deep Learning Approach Towards Indian Stock Market Movement Prediction 8

SM2PNet: A Deep Learning Approach Towards Indian Stock Marke...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy SVNIT Department of CSE Surat India FLAME University School of Computing and Data Science Pune India

ISBN: (纸本)9798350334012

The objective of this study is to forecast the future price movements of equities listed on the National Stock Exchange (NSE) of India. To anticipate the future price movement, all prior statistical and machine learning techniques have focused on identifying latent temporal dependencies. In actuality, stock prices reflect all available information and are influenced by several external variables, making forecasting difficult. One such major element is the interdependence that exists between stock pairings from the same industry. By incorporating this relational dependence combined with the temporal dependency, we may attain improved prediction performance. In order to demonstrate this, we have created a model, SM2PNet (Stock Market Movement Prediction Network), which takes into consideration the characteristics of highly correlated firms in order to examine their impact on the stock price of the selected target company. We've analysed predefined sector-industry linkages in order to identify the most relevant peer firms. Sequential model long short-term memory (LSTM) is used to represent temporal dependence. The investigation confirms that SM2PNet beats all other available deep learning methods. © 2023 IEEE.

关键词： Financial markets

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A Machine Learning-Based Job Forecasting and Trend Analysis System to Predict Future Job Markets Using Historical Data 8

A Machine Learning-Based Job Forecasting and Trend Analysis ...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Senthurvelautham, Sharanaja Senanayake, Nipuna Institute of Technology Robert Gordon University Department of Computing Informatics Colombo Sri Lanka

ISBN: (纸本)9798350334012

Over the last two decades, technological advancements have created more job markets and job opportunities than ever. With the ever-increasing demand, it has become vital for academic institutions and businesses to keep up with employment requirements. The problem is more severe for modern and rapidly evolving industries such as software development. This study implements a prediction system for job trends to enable job seekers, organizations, and academic institutions to understand and align their endeavors to match the market requirement. Employers can benefit from the system by using it to identify potential talent shortages and proactively address them. Policymakers can also use the system to understand the potential impacts of changes in the job market on the economy, enabling them to make informed decisions about supporting employment *** prediction is made possible by creating a rich dataset based on more than 522,180 job listings from the past 24 months in the software industry. The dataset is fed to a Bidirectional LSTM model to predict the future trends of the job market for various roles and technologies. Auto-aggressive prediction is implemented using the bi-directional LSTM model as this combination proves to produce the most accurate results after multiple quantitative analyses and evaluations. This study evaluated the proposed solution against known real-world data and it was concluded that the system can predict the job trend for at least the next 12 months with a relatively high accuracy of 95.71%. © 2023 IEEE.

关键词： Forecasting

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An Efficient Incentive Mechanism for Collaborative Anomaly Detection in Internet of Things 25th

An Efficient Incentive Mechanism for Collaborative Anomaly ...

引用

25th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： He, Wenhai Li, Jinzhao Qiao, Cheng Zhao, Bowen School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Guangzhou Institute of Technology Xidian University Guangzhou China

ISBN: (纸本)9789819642069

The Internet of Things (IoT) is transforming industries by connecting a myriad of devices to collect and exchange data. However, this connectivity increases vulnerability to cyber threats, making anomaly detection a critical and challenging research topic. Collaborative abnormal detection leverages multiple IoT devices to identify irregularities, but its success hinges on active participation from these devices. This paper explores the development of an efficient incentive mechanism to encourage device participation in collaborative abnormal detection, ensuring robust security in IoT networks. We consider two different knowledge levels of agents and integrate the success detection rate into the utility function of the incentive mechanisms. In particular, we proposed a lightweight method to compute the contribution of agents. Our evaluations reinforce the effectiveness of the proposed methods in enhancing critical performance metrics (by as much as 14% on average), albeit at the cost of longer convergence times (increased by around 11% on average). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Robot Path Planning Based on Grid Map Using Improved Ant Colony Algorithm 3

Robot Path Planning Based on Grid Map Using Improved Ant Col...

引用

3rd international conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Kou, Farong Xiao, Wei He, Haiyang Hu, Kailun School of Mechanical Engineering Xi'An University of Science and Technology Shanxi China

ISBN: (纸本)9798350331578

For the problems of slow convergence and easy to fall into local optimum of traditional ant colony algorithm (ACO) for robot path planning, An improved ant colony algorithm (IACO)based on grid map is proposed in this paper. Firstly, in order to improve the positive feedback ability of pheromone in the later period, an adaptive adjustment factor is introduced into the heuristic function. Secondly, the rotation function is introduced into the pheromone state transition probability to balance the relationship between path length and angle. Finally, in order to ensure the quality of participating pheromone update nodes, local optimization strategies are designed based on cross optimization and redundant point deletion, and different quality paths are updated with pheromone difference mechanism to achieve convergence of high-quality nodes. The experimental results show that IACO can make the robot obtain the global optimal path, and it will have good stability and environmental adaptability. © 2023 IEEE.

关键词： Motion planning

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A Fractional-Order Logarithmic Adaptive Filtering Algorithm Under α-Stable Noise 6

A Fractional-Order Logarithmic Adaptive Filtering Algorithm ...

引用

6th IEEE international conference on Electronic Information and Communication technology, ICEICT 2023

作者： Sun, Dazhi Yu, Canping Li, Li Li, Yingsong Xiao, Wudang Chen, Yong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology Center of Aviation Safety Technology Cast Beijing101399 China

ISBN: (纸本)9798350399059

In practical applications, noise is more appropriately modelled as an a-stable random process than as a Gaussian process. To cope with a-stable noise, a fractional-order stochastic gradient descent (SGD) method is invented to adaptive filtering (AF) algorithms. When both input signal and noise follow α-stable random processes, the AF algorithms based on mean square error (MSE) criteria can be affected. Nevertheless, the fractional-order AF (FoAF) algorithm can effectively address this issue. For the purpose of better stability performance of the FoAF algorithm, different cost functions (CFs) are introduced into fractional-order SGD method. Based on the fractional-order derivative of a logarithmic CF (LCF), a fractional-order logarithmic adaptive filtering (FoLAF) algorithm is formed in this condition. Trials show that the FoLAF algorithm achieves faster convergence, excellent steady-state performance and superior tracking performance. © 2023 IEEE.

关键词： Adaptive filtering

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Distributed Resource Optimization and Allocation for UAV-Assisted MEC in Internet of Vehicles

Distributed Resource Optimization and Allocation for UAV-Ass...

引用

2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Fang, Yujie Li, Meng Si, Pengbo Yang, Ruizhe Wang, Zhuwei Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350345384

As an essential element of intelligent transport systems, the Internet of vehicles (IoV) has brought an immersive user experience recently. Meanwhile, the emergence of mobile edge computing (MEC) has enhanced the computational capability of the vehicle which reduces task processing latency and energy consumption effectively and meets the quality of service requirements of vehicle users. However, there are still some problems in the MEC-assisted IoV system such as poor connectivity of vehicle networks and high cost of traditional infrastructure deployment and maintenance. Unmanned aerial vehicles (UAVs) equipped with MEC servers have become a promising approach for providing communication and computing services to mobile vehicles because of their agility, low cost, and readily to deployment. Hence, in this article, an optimal framework for the UAV-assisted MEC system for the IoV to minimize the average system cost is presented. Through joint consideration of computational offloading decisions and computational resource allocation, the optimization problem of our proposed architecture is presented to reduce system energy consumption and delay. For purpose of tackling this issue, the original non-convex issue is converted into a convex issue and the alternating direction method of multipliers-based distributed optimal scheme is developed. The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes, and the convergence of the proposed scheme is also significant. © 2023 IEEE.

关键词： Mobile edge computing

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Early Prediction of Dengue Cases Using Time Series Model 8

Early Prediction of Dengue Cases Using Time Series Model

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Tejaswi, M. Supritha, V. Thangam, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798350334012

Dengue, is a seasonal infectious disease has been spreading widely throughout the world. Because of this many of the people are losing their lives. So public Health organizations and many other government organizations have been conducting surveys of dengue cases and they got to know that because of weather conditions like rain, precipitation, humidity are some of the main causes for dengue prediction. Also they use some ML algorithms and some Time Series models to predict dengue cases. Time series models they have used as (ARIMA) model and SARIMA model and seasonality. But out of this ARIMA model looks best to predict dengue cases. So we have taken ARIMA model to predicting the forecast of dengue cases. In ARIMA we have done two methods to find out the order of the model where it has values as autoregressive, moving average and non-seasonal in seasonality autocorrelation, partial autocorrelation, and AUTO-ARIMA for find out the best model for the order then we have checked for stationary also. If its stationary then we have proceeded to prediction of forecasting dengue cases, if it's not then we have converted it into stationary and then we moved on to do ARIMA best fit which gives the forecasting cases. © 2023 IEEE.

关键词： Forecasting

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Kannada Textual Error Correction Using T5 Model 8

Kannada Textual Error Correction Using T5 Model

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Ramaneedi, Sushmitha Pati, Peeta Basa Amrita School of Computing Department of Computer Science & Engineering Bengaluru Amrita Vishwa Vidyapeetham India Amrita School of Engineering Department of Computer Science & Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350334012

Error creeps into text in various ways. Typing error may come due to either mis-typing or due to poor language expertise. Similarly, recognition technologies while converting textual images and speech into text may generate error due to their limitations. Irrespective of the channel of error induction, presence of error poses a huge challenge for downstream consumption of such textual content. Additionally, error present in Indian language textual documents come with their own set of issues. This necessitates focused study on the textual errors in Indian language documents and the various technologies which may be employed to eliminate *** work proposes to employ mT5, a very popular deep learning based multi-lingual language model, to eliminate errors present in Kannada, an Indian Language, text. A pretrained model of mT5 is enhanced with transfer learning for a Kannada dataset. The ability of the enhanced mT5 model to reduce error is studied at various levels of noise. Character Error Rate (CER) is employed as the metric. It's observed that the enhanced mT5 model is effectively able to reduce noise by 12% for input text with 25% CER. © 2023 IEEE.

关键词： Deep learning

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Prescribed-time Model-free Adaptive Attitude-tracking Control of Spacecraft System under DoS Attacks

Prescribed-time Model-free Adaptive Attitude-tracking Contro...

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2023 IEEE international conference on Real-Time computing and Robotics, RCAR 2023

作者： Chen, Run-Ze Xu, Sheng Li, Yuan-Xin Guo, Wei-Lun Shandong Institute of Advanced Technology Chinese Academy of Sciences Jinan250102 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Liaoning University of Technology Department of Science Jinzhou121001 China

ISBN: (纸本)9798350327182

This paper presented a prescribed-time model-free adaptive control (PTMFAC) scheme to achieve attitude-tracking control of a spacecraft system under denial-of-service (DoS) attacks. By using a finite-time performance function (FTPF), an improved dynamic linearization module was presented to attain the prescribed-time convergence of output signal. At the same time, we presented a counter-attack strategy to reduce the effect of attacks. By employing the counter-attack strategy, a PTMFAC control algorithm was proposed to achieve attitude-tracking control within a prescribed time. Finally, the boundness analyses and simulations confirmed the effectiveness of developed control algorithm. © 2023 IEEE.

关键词： Denial-of-service attack

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Architecture and Implementation of a patient criticality aware Edge-Cloud offloading technique 8

Architecture and Implementation of a patient criticality awa...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Menon, Athira B Rajeev, Krishna Jyothika, S. Ratheesh, Karthika Pathinarupothi, Rahul Krishnan Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Amritapuri India

ISBN: (纸本)9798350334012

The Internet of Things (IoT)-based remote health monitoring is one of the most promising technological interventions that is emerging to address the unique challenges of affordability, accessibility, and availability in global health, which denote equitable access to healthcare, particularly in remote-rural-developing regions. IoT devices can monitor multiple vital signs, which are then used for AI-assisted decision-making systems, which in turn assist physicians in forecasting remotely. However, there are many difficulties, like bandwidth issues, data loss, and overburdened doctors due to the massive amount of data. Shifting data from cloud to edge improves performance, cost efficiency, privacy, reduces communication between distant servers and the edge, resulting in less processing delay. We developed a clinical response requirement based cloud-to-edge offloading technique. As a use case, we developed an edge AI application for acute hypotensive episode prediction and compared its performance both in the cloud as well as on the edge. © 2023 IEEE.

关键词： Internet of things

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