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Exploring Person-Job Fit Influence on Innovation Behavior through Machine Learning Approach in Public Sector Banking Institutions

Exploring Person-Job Fit Influence on Innovation Behavior th...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Jagendra Singh Dinesh Prasad Sahu Preeti Sharma Vinish Kumar Meenakshi Sharma Ramendra Singh School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE (DS) Raj Kumar Goel Institute of Technology Ghaziabad India Department of CSE (AI-ML) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Electronics& Communication Engineering Inderprastha Engineering College Ghaziabad India Department of Computer Science and Engineering (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

This research explores the impact of person-job fit on innovation behavior in public sector banking institutions in India. This analysis relies on machine learning tools, such as regression and decision trees. The research is based on a sample of 388 professionals across three public sector banks. Job involvement serves as a mediating factor in the described relationship, while the moderating variable is the career commitment of the participants. According to the results of this study, the person- job fit impacted the innovation behavior. The Logistic Regression showed accuracy on average at 87.3%, precision at 89.0%, recall at 85.1%, and an F1 score at 87.0%. At the same time, it showed an average AUC-ROC of 0.92, as well as MSE at 0.13 and RMSE at 0.36. The Decision Trees showed accuracy on average at 84.7%, precision at 87.1%, recall at 82.7%, and an F1 score at 84.7%. The average AUC-ROC was 0.89; the MSE was 0.16, and RMSE was 0.40. Such findings suggest that person- job fit is a critical factor for innovation behavior among public sector bank employees. Additionally, it is evident that machine learning algorithms can be used to predict person- job fit performance, and such performance is likely to lead to innovation behavior. Furthermore, the study helps to understand the influence person- job fit has on innovation behavior through career commitment and job involvement. The findings, therefore, offer useful insights into organizational behavior in the banking sector.

关键词： Technological innovation Logistic regression Accuracy Machine learning algorithms engineering profession Banking Machine learning Predictive models Reliability Regression tree analysis

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Real-Time Anomaly Detection and Threat Mitigation in IoT Networks Using Convolutional Neural Networks (CNNs) for Enhanced Security

Real-Time Anomaly Detection and Threat Mitigation in IoT Net...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Jagendra Singh Jabir Ali Preeti Sharma Vinish Kumar Meenakshi Sharma Ramendra Singh School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of CSE (DS) Raj Kumar Goel Institute of Technology Ghaziabad India Department of CSE (AI-ML) Raj Kumar Goel Institute of Technology Ghaziabad India Department of Electronics & Communication Engineering Inderprastha Engineering College Ghaziabad India Department of Computer Science and Engineering (IoT) Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research evaluates the development of convolutional neural network (CNN) models, including 1D CNNs, deep CNNs, residual networks (ResNets), and dilated convolutions, to secure wearable technology capabilities specific to alerting for emergencies and tracking location. The performance is tested using evaluation metrics such as accuracy, precision, recall, and the F1-score as emergency-related anomaly detection. The deep CNNs performed the best, with accuracy, precision, recall, and F1-scores all exceeding 90%. These findings emphasize their ability to reliably detect crises at the onset with very few false positives. The residual networks were also impressive, with an accuracy of 93%, a precision of 89%, a recall of 91%, and a good F1-score, demonstrating their effectiveness in dealing with data complexity. The 1D CNNs demonstrated good performance for this task, with an accuracy of 92% and a precision of 87.9%. Recall of 90.3% and F1-Score as high as 89%, which showed its suitability in processing sequential data from wearable sensors When dilated convolutions are used, an accuracy of 91% is achieved, which is slightly behind the precision, recall, and F1-score. The results, thereby ensuring its ability to capture anomalies with changing temporal scales as proposed, The research outcomes highlight the importance of CNN architectures in increasing both the accuracy and speed of wearable technology, leading to improved user safety and operational efficiency in dynamic settings.

关键词： Measurement Accuracy Real-time systems Safety Convolutional neural networks Security Wearable devices Anomaly detection Wearable sensors Residual neural networks

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Analysis of Directional Projection Characteristics Based on Convolution Neural Network Algorithm and Application of Cultural Relics Authenticity Identification 2

Analysis of Directional Projection Characteristics Based on ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Devi, P.P. Hamad, Abdulsattar Abdullah Manikandan, N. Thejasree, P. Bhuvana, R. Kharmega Sundararaj, G. Department of AI and ML Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Physics University of Samarra College of Education Slah al deen Samarra city Iraq Dept of Mechanical Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Dept of Computer Science AM JAIN College Tamilnadu Chennai India Department of CSE Dr. T. Thimmaiah Institute of Technology Oorgaum KGF Karnataka India

ISBN: (纸本)9798350305418

In recent years, with the rapid development of computer technology, intelligent algorithms have been widely used in all walks of life. More and more data processing methods have been applied in ancient ceramic identification. This paper introduces the identification algorithm of archival cultural relics based on a cascaded deep convolution neural network, the smooth identification algorithm of cultural relics image, the shape identification based on geometric constants, the theoretical basis of cultural relics identification, cultural relics are physical relics combined with human values in a certain sense. We should not only respect the value and significance of cultural relics themselves but also transmit the ideological connotation and spiritual significance given by predecessors to the present through protection and restoration. © 2023 IEEE.

关键词： Application of authenticity identification of cultural relics Based on convolution neural network algorithm Projection feature analysis

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Mitigating Distributed Denial of Service (DDoS) Attacks in Cloud Networks Using Neural Networks

Mitigating Distributed Denial of Service (DDoS) Attacks in C...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The research aims to prove the effectiveness of three neural network models the convolutional neural network (CNN) and Long Short-Term Memory (LSTM) Hybrid, Variational Autoencoders, and Bidirectional LSTM in neutralizing DDoS attacks on online shopping websites. The models are ranked using critical performance metrics relevant to cybersecurity. Firstly, the CNN-LSTM Hybrid has demonstrated a very high True positive rate (TPR), with a reading of 0.95, as well as a low False positive rate (FPR) of 0.03, resulting in a precision of 0.93 and a recall of 0.95. The readings show that the model is accurate in distinguishing between regular traffic and DDoS attacks, having an overall Fl-Score of 0.94. On the anomaly detection-based Variational Autoencoders (VAEs), the TPR was 0.92, the FPR was 0.05, the precision was 0.90, and the recall 0.92. The bidirectional LSTM model returned a TPR of 0.94 and an FPR of 0.04, and both precision and recall were also brought to 0.92 with an Fl-score of 0.93. These models improve online shopping website security from cyberattacks by preventing data availability and consistency. As DDoS attack discovery is a major step for continuing institutions, the designs serve administrators and greatly protect online buyers.

关键词： Measurement Neural networks Autoencoders Bidirectional long short term memory Denial-of-service attack Data models Convolutional neural networks Electronic commerce computer crime Anomaly detection

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Chatbot and its Impact on the Retail Industry

Chatbot and its Impact on the Retail Industry

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Reshmi Tatikonda M. Kempanna Ramya Thatikonda A. Bhuvanesh Ragupathi Thota R. Keerthanadevi Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including ai-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences.

关键词： Industries Training Reviews Psychology Transforms Inventory management Chatbots Multilingual Resource management Sustainable development

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A Design of Hybrid Model and Bayesian Neural Networks for Smart Grid Stability Prediction 1

A Design of Hybrid Model and Bayesian Neural Networks for Sm...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Sriramkumar, R. Dayananda Sagar University AI & ML Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bagalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

The latest evolution in power systems, is 'smart grids'that offers real time monitoring, control features as well as effective management of renewable energy sources. Nonetheless, with the increase in the system complexity, the chances for insta- bility also increase and thus it is crucial to forecast the stability accurately for the safety of the grid. Weather it is in terms of Gradient Boosting Tree Algorithm or any other traditional algorithm, these machine learning models are successful in achieving the objectives of the task but they lack a forthcoming metric to assist in high stake situations, uncertainty quantification. The paper is devoted to the analysis of the reliability of smart grid systems based on Bayesian Neural Networks (BNN) taking into account both the reliability of predictive modeling and forecasting uncertainty. The smart grid stability augmented dataset was used to benchmark the efficacy of using BNNs or BNNs combined hybrid models, one with XGBoost, for feature extraction and the other with LightGBM. The present study shows that the BNN used with the above mentioned smart grid system achieved an accuracy of 92% with precision of 0.89, recall of 0.91 and F1 of 0.90. These metrics are appending with the help of the additional features of BNN where they are capable of addressing the key questions around uncertainty and thus the metrics confirming the predictions are quite strong in terms of real-time grid stability evaluation. This article encapsulates a detailed study of BNN architecture, the role of uncertainty in machine learning models and the natural application of such models in the design of dependable and fault-tolerant smart grids. © 2025 IEEE.

关键词： Smart power grids

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Using Deep Learning to Unlock Data science Insights: An Exploration of Techniques

Using Deep Learning to Unlock Data Science Insights: An Expl...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Bhavuk Samrat Megha Pandey R Dushyanth V Babu R Elackiya Omkaresh S. Kulkarni Vaibhav Srivastav Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper explores deep getting-to-know-in records technology, introducing the numerous techniques that can be used to unlock information science insights. Deep learning is a subset of machine-gaining knowledge that specializes in using neural networks to increase representations of statistics. These representations can encode know-how about a machine or surroundings, making applying the representations to derive beneficial insights feasibly. The paper focuses on various deep getting-to-know strategies, inclusive of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative opposed networks (GANs). The paper also discusses the potential packages of deep getting-to-know statistics science, such as predicting time-collection statistics, detecting anomalies, and automated characteristic engineering. ultimately, the paper outlines how data technology professionals can leverage deep mastering to discover treasured records insights.

关键词： Deep learning Support vector machines Knowledge engineering Information science Recurrent neural networks Gears Input variables Neurons Predictive models Convolutional neural networks

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Geospatial Analysis and Machine Learning for Site Suitability of Geothermal Energy Plants

Geospatial Analysis and Machine Learning for Site Suitabilit...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Anandhi R J Vanya Arun Navdeep Singh P Sai Kumar Yagateela Pandu Rangaiah Saurabh Sharma Fatimah Dhari Jabbar Department of Information Science Engineering New Horizon College of Engineering Bangalore India Department of Electrical & Electronics Engineering IILM University Greater Noida Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lloyd Law College Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The use of geospatial data and machine learning have upturned the evaluation of site suitability for geothermal energy plants. This purpose of this study is to determine the conditions of geothermal energy production using statistical and Resilience analysis models. Large datasets of geophysical and thermal properties can be found in the public domain and Geopandas and Rasterio libraries in Python allow working with such large datasets to create prediction models. Random Forests and Convolutional Neural Network (CNN) prefilter further improves the accuracy by exploring the spatial distribution and thermal signature of the sites. The methodology discussed in this study can be seen to have a strong potential of enhancing the level of accuracy in the identification of geothermal sites hence minimising the uncertainty that may be encountered in exploratory drilling. The study advances the knowledge in the field of renewable energy by including a rich context for geospatial analysis as well as a framework for using machine learning to categorize the geothermal prospects of different regions. The findings therefore call for multi-disciplinary studies in enhancing the viable renewable energy sources, and promoting sustainable energies.

关键词： Drilling Renewable energy sources Accuracy Uncertainty Computational modeling Geothermal energy Predictive models Geospatial analysis Convolutional neural networks Random forests

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Design and Implementation of Lane Detection using Hough Transformation

Design and Implementation of Lane Detection using Hough Tran...

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Applied Artificial Intelligence and Computing (ICAaiC), International Conference on

作者： Madhuri Pagale Sunanda Mulik Richa Purohit Anuradha Thakare Rohini Hanchate Sulaxan Jadhav D Y Patil International University Pune India Department of CSE(AI &ML) Pimpri Chinchwad College of Engineering Pune India Department School of Computer Science Engineering and Applications D Y Patil International University Pune India Dept School of Computer Studies Sri Balaji University Pune India Dept of CSE (AI & ML) Pimpri Chinchwad College of Engineering Pune India Dept of computer Science Nutan Maharashtra Institute of Technology Pune India Dept School of Computer Science D Y Patil International University Pune India

Typically, road lanes are solid or dashed line formations that are continuous on the surface. As the driving sceneries are continuous as well as substantially overlap, the placement of lanes in one frame is highly correlated with their position in the next frame. computer vision-related machine learning algorithms have also advanced rapidly in recent years, becoming both more efficient & more effective as high-precision optical and electronic sensors become more commonplace, and real-time driving scene recognition is becoming more feasible. Recent years have seen several technical breakthroughs in the field of road safety, as the number of accidents has risen at an alarming pace, with driver inattention being one of the primary causes. To minimize the incidence of accidents as well as maintain safety, technological advances are required. Lane Detection Systems, which operate to recognize the lane boundaries on the road as well as alert the driver if he changes and goes to incorrect lane markings, are one method of achieving this goal. A lane detection system is a crucial element of many technologically advanced transportation systems. This research uses the Hough Transform technique for lane identification.

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Artificial Intelligence driven Deep Learning for Competitive Intelligence to enhance Market Analysis and Strategic Positioning

Artificial Intelligence driven Deep Learning for Competitive...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Sunil Shukla Jagendra Singh Vinay Kumar Nassa Masarath Saba Jitesh Bhatia Muniyandy Elangovan School of Computing Graphic Era Hill University Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India Dept. of Information Communication Technology Tecnia Institute of Advanced Studies GGSI University Delhi India Department of CSE (AI&ML) CVR College of Engineering JNTUH Hyderabad India Department of Client Engagement Epsilon Massachusetts United States Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

This research provides insight into the successful application of modern deep learning techniques such as Transfer Learning, BERT, and Autoencoders to develop a powerful tool to sustain market analysis and strategic positioning in competitive intelligence. The conducted analysis proved that BERT was the most effective technique, as it produced the highest results in regards of accuracy, precision, recall, and F1 score values in all ten trials. The results of the experiment showed that with accuracy ranging from 90.5% to 92.3%, precision from 91.2% to 93.0%, recall from 89.7% to 91.7%, and F1 score from 90.4% to 92.2%, BERT proved to be the best technique for extracting insights from various textual data sources. In and of itself, Transfer Learning also yielded promising results, and its accuracy ranged from 85.2% to 88.6%, precision from 86.5% to 89.9%, recall from 83.7% to 87.3%, and F1 score from 85.0% to 88.5%. Additionally, given its ability to adapt to particular tasks with quite limited labeled data, Transfer Learning is a valuable tool for market analysis. In this experiment for Autoencoders, it has been shown that they provided a slightly lower performance than BERT and Transfer Learning. The reconstruction error of the deep learning method used in this experiment ranged from 0.018 to 0.025, while accuracy varied from 87.9% to 90.3%. Nevertheless, further optimization can make autoencoders a promising approach for market analysis. Thus, this research indicates the vital importance of leveraging advanced deep learning techniques for gaining a competitive advantage in the present-day business environment.

关键词： Deep learning Technological innovation Accuracy Soft sensors Transfer learning Autoencoders Distance measurement Competitive intelligence Natural language processing Optimization

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