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检索条件"机构=Dept. of Computer Science and Data Analytics"

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A Study on Innovative Work Behavior with Reference to Software Development Companies 4

A Study on Innovative Work Behavior with Reference to Softwa...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Nanda, Kavita Jaimini Kapoor, Timsy Dept. of Computer Science and Data Analytics Madhya Pradesh Indore India

ISBN: (纸本)9798331518981

Innovation is a vital component of success in the software development industry, where companies constantly seek to improve processes, products, and services. While individual creativity and motivation are essential, organizational factors also play a significant role in shaping innovative work behavior. This study delves into the complex interplay between individual and organizational factors that influence innovative work behavior in software development companies. Using a mixed-methods approach, we collected survey data from software developers, conducted in-dept. interviews, and observed work practices. Our results reveal those individual factors like creativity, autonomy, and motivation, as well as organizational factors like leadership support, resources, and a culture of innovation, significantly impact innovative work behavior. We also found that organizational factors moderate the relationship between individual factors and innovative work *** study contributes to the understanding of innovative work behavior in software development, highlighting the importance of aligning individual and organizational factors to foster a culture of innovation. Our findings provide actionable insights for software development companies to develop strategies that promote innovative work behavior, drive competitiveness, and enhance success in the industry. By understanding the interplay between individual and organizational factors, companies can create an environment that encourages creativity, experimentation, and innovation, leading to the development of novel software solutions and improved business outcomes. © 2024 IEEE.

关键词： Autonomy Creativity Individual Factors Innovative Work Behavior Leadership Support Motivation Organizational Factors Resources Software Development

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Leveraging AI-Generated Image for Scene Classification: A transfer Learning Approach 11

Leveraging AI-Generated Image for Scene Classification: A tr...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Khan, Seher Singh, Krishna Kumar Symbiosis Centre for Information Technology Dept. of Data Science and Data Analytics Pune India

ISBN: (纸本)9798350350357

When it comes to scene classification, the lack of labelled data is a significant obstacle that forces researchers to think about innovative solutions. This work presents a revolutionary method to strengthen scene classification models by combining transfer learning with AI-generated images. A key component of this project is the construction of a carefully curated dataset of 3672 augmented AI-generated photos in a total of 6 scenes. This database not only serves as a helpful resource for additional research but also makes a substantial contribution to this area of research. Using this dataset, a VGG16 model is trained, demonstrating the usefulness of synthetic data with a notable increase in accuracy. The model's performance is further improved through subsequent training using realistic photos through transfer learning, thereby surpassing the initial baseline. This work demonstrates the transformative effect of transfer learning on scene categorization accuracy and verifies its effectiveness using synthetic data through rigorous testing and evaluation. The results open the door for AI-generated photos to be used as an effective approach to address data scarcity issues in scene classification, which will help real-world applications improve. © 2024 IEEE.

关键词： computer vision

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Detecting Feet's Bending Patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

引用

5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. The smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

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2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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Interactive Touch Me Not Game: Arduino UNO and Closed Circuit Principles

Interactive Touch Me Not Game: Arduino UNO and Closed Circui...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Reddy, Vinay Kumar Sabarmathi, G. Dept. of computer science Bangalore India Faculty of Business Analytics Dept. Business and Management Bangalore India

ISBN: (纸本)9798350379525

This research paper explores the innovative integration of electronics and gaming by implementing a closed circuit current passing mechanism within the popular game 'Touch Me Not' using the Arduino Uno microcontroller. 'Touch Me Not' is a classic electronic game that challenges players to complete a circuit without touching the wire in the middle with the copper handle. Traditionally, this game relies on passive components like wires and metal contacts. In this study, we present a novel approach by incorporating the Arduino Uno platform to enhance the gameplay experience and educational value. © 2024 IEEE.

关键词： Timing circuits

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on Engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： data visualization

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Real Time Driver Drowsiness Detecion using Transfer learning 3

Real Time Driver Drowsiness Detecion using Transfer learning

引用

3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Gupta, Neha Khan, Faizan Saini, Bhavna Central University of Rajasthan Dept. of Data Science and Analytics Kishangarh India

ISBN: (纸本)9781665453578

According to statistics, drowsy driving is the leading cause of accidents worldwide that result in the loss of precious lives and worsen public health. When a driver is fatigued, cameras can be employed to detect their drowsiness and inform them well before which can help in decreasing accidents. This work employes a transfer Learning model DenseNet to identify the driver drowsiness in real time. The MRL eye dataset of 84923 images has been used and the model works well with 91.56% accuracy. © 2023 IEEE.

关键词： Accidents

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From data to Harvest through Machine Learning based Crop Yield Forecasting 2

From Data to Harvest through Machine Learning based Crop Yie...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Budarapu, Aadithya Vikram Gaddam, John Hyde Boyedi, Ramu Muthyala, Thoshan Kumer Kathirisetty, Nikhila Dept. of Information Technology Hyderabad India Dept. of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The maintenance and enhancement of dynamic soil characteristics are the primary focus of soil management in agriculture to increase crop productivity. Higher productivity may result from efficient soil control of resources and corrective micronutrient treatments. Using CNN and 'KNN' algorithms, the 'soil land classification and crop prediction system' application was created. In this study, two datasets are used: one to obtain crop prediction and the other for soil land categorization. The kind of soils are trained using CNN ('VGG- 19') algorithm, and the accuracy of the model is calculated. The trained model is then utilized in the Flask web app to forecast the type of soil. Another data set with nitrogen, phosphorus, potassium, pH, and temperature as features and the class type of crop as a label is used to forecast crop production. These two algorithms are used to create the flask website, which accepts inputs such as soil picture, soil type prediction, and land parameter inputs for crop prediction. © 2024 IEEE.

关键词： Soils

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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Predictive Maintenance with Large Language Models and Transformer-based Survival Analysis 50

Predictive Maintenance with Large Language Models and Transf...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Esteban, Aurora Cobilean, Victor Nawaratne, Rashmika Dept. of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Dept. of Computer Science Virginia Commonwealth University RichmondVA United States Research Center for Data Analytics and Cognition La Trobe University VIC Australia

ISBN: (纸本)9781665464543

Predictive maintenance involves proactive monitoring and prediction of equipment failures to optimize maintenance schedules to minimize downtime, reduce losses, and ensure optimal equipment performance. However, traditional methods often rely on classification or regression, overlooking the temporal aspect of failure prediction. Survival analysis is focused on modeling time-to-event data, making it well-suited for predicting the time until equipment failures. The primary goal of this paper is to evaluate the feasibility and effectiveness of Transformer-based model for survival analysis in the industrial domain. Additionally, this work aims to explore the integration of Large Language Models as virtual assistants to facilitate user interaction and decision-making in the predictive maintenance process. The proposed system was tested on a public predictive maintenance problem and the Transformer-based model obtained better survival predictions than the baseline statistical and machine learning models, with improvements of up to 20%. Furthermore, the virtual assistant based on the large language model demonstrated the ability to retrieve precise information based on natural language prompts. © 2024 IEEE.

关键词： Predictive maintenance

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