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Prediction of human Resource Demand based on Backpropagation Neural Network 1

Prediction of Human Resource Demand based on Backpropagation...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Wu, Wenjian Officers College of Pap Chengdu China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Accurate forecasting of human resource (HR) demand is essential for strategic workforce planning and operational efficiency. By leveraging data-driven insights, organizations can enhance strategic workforce planning, improve employee retention, and align staffing levels with evolving business goals and market demands. Additionally, precise HR forecasting helps mitigate risks associated with workforce fluctuations, ensuring operational efficiency and long-term organizational sustainability. Traditional HR demand prediction models often fail to capture complex patterns within workforce data, leading to inefficiencies in talent acquisition and retention. This research proposes an advanced HR demand prediction model leveraging a Backpropagation Neural Network (BPNN) trained on a HR dataset. The dataset encompasses key employee attributes, including demographic details, job positions, performance scores, engagement metrics, and absenteeism records, providing a comprehensive basis for predictive modeling. Despite conventional statistical approaches, HR demand forecasting remains challenging due to dynamic workforce behaviors and fluctuating industry requirements. The BPNN effectively learns from historical patterns to predict future HR demand with high accuracy. The proposed model achieves a prediction accuracy of 98.9%, with key performance metrics like Mean Absolute Error (MAE) of 1.8, Root Mean Square Error (RMSE) of 2.9, and an R2 score of 0.98, demonstrating its robustness and reliability. Experimental results validate the model's superiority over traditional regression and machine learning methods. The findings highlight the potential of AI-driven HR analytics in optimizing recruitment strategies, minimizing turnover risks, and enhancing workforce planning for sustainable organizational growth. © 2025 IEEE.

关键词： Prediction models

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Achieving Balanced Allocation in human Resources through LSTM Framework Method 1

Achieving Balanced Allocation in Human Resources through LST...

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Giriprakash, A. Arunadevi, V. Rajalakshmi, K. Khan, Shamim Ahmad Jeyalakshmi, R. Ponni Valavan, M. Sakthi Institute of Information and Management Studies Department of Mba Pollachi India Dr. Mahalingam College of Engineering and Technology Department of Science and Humanities Pollachi India Glocal University Glocal School of Science & Technology Department of Ece Saharanpur India Affiliated to JNTU-K Mylavaram India K. Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798331529246

This paper proposes a novel human resource management system leveraging Long Short-Term Memory (LSTM) networks for optimal HR allocation. The process begins with data acquisition, including performance metrics, attendance records, and employee details, followed by Min-Max normalization for uniform formatting. Principal Component Analysis (PCA) is employed for feature selection, reducing variables while retaining critical information. LSTM networks, with their ability to analyze sequential data and capture temporal dependencies, enable accurate predictions of workforce demands and optimal resource distribution. Experimental results demonstrate the model's superiority over conventional methods, achieving balanced HR allocation, improved workforce control, enhanced employee satisfaction, and higher organizational performance. This scalable approach aligns staffing with organizational objectives, preventing overstaffing or understaffing. By integrating advanced machine learning into HR management, the proposed system offers a significant step toward smarter workforce planning and improved HR practices, paving the way for efficient and adaptable organizational management. © 2025 IEEE.

关键词： Personnel selection

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An Algorithmic and machine Learning Approach for Yoga Asanas Feedback

An Algorithmic and Machine Learning Approach for Yoga Asanas...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Prakhar Saxena Vishnu Sharma Anuj Yadav Rohit Pratap Singh Laiphrakpam Dolendro Singh Department of Computer Science and Engineering National Institute of Technology Silchar Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Yoga, rooted in the ancient traditions of India, epitomizes the pursuit of holistic harmony, seeking to integrate life’s physical, mental, and spiritual aspects. Innovative solutions are emerging to enhance yoga practices in response to the growing interest in integrating artificial intelligence and machine learning into physical well-being. The proposed work addresses the limitations of human-based feedback systems in yoga sessions by proposing an algorithmic approach aided by machine learning models to provide unbiased feedback on Yoga Asanas. Moreover, the proposed work mitigates injuries resulting from incorrect yoga practices and the inherent subjectivity in human assessments and offers feedback on yoga performances based on predefined criteria.

关键词： machine learning algorithms machine learning intelligent systems Injuries

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Classifying human Emotions through EEG data with machine Learning

Classifying Human Emotions through EEG data with Machine Lea...

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intelligent systems and Computational Networks (ICISCN), international conference on

作者： Gaganjot Kaur Meenu Gupta Rakesh Kumar Department of Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The Psychological and neurobiological phenomenon is called an emotion. The capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, it is challenging to identify a human’s real emotional state by observing physical appearance. Nowadays, using emotional engineering to predict human emotions is one of the major attempts of machine Learning (ML). This study aims to investigate the usage of techniques of machine learning for efficient emotion prediction that relies on electroencephalography (EEG) signals. EEG dataset is complex and sometimes noisy, making it difficult to interpret using traditional methods. So, in this research the Kaggle dataset collected through a Muse headband, five distinct ML methods are applied to classify emotions. Performance metrics such as precision, recall, F1 score, support, and accuracy are employed to differentiate between different classification techniques. Random Forest turned out to be the best method due to its high accuracy 99%. Three emotions are predicted based on the results of performance metrics. This study benefits mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploring this field and helps develop more sophisticated control systems in the future.

关键词： Support vector machines Radio frequency Accuracy Nearest neighbor methods Electroencephalography Object recognition Noise measurement Random forests Neurofeedback Monitoring

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Pre-contextualized Augmented Language Instructions for Autonomous Vision-and-Language Navigation

Pre-contextualized Augmented Language Instructions for Auton...

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Disruptive Technologies (ICDT), international conference on

作者： Olivia Piazza Sudhir Shrestha Intelligent Systems Lab Sonoma State University Rohnert Park CA USA

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Advances in natural language has allowed for the automation of robotic tasks with minimal supervision, even in ambiguous situations. In the field of robotic navigation, human instruction can be utilized to allow a robotic agent to traverse unknown environments using instructional commands that mimic human-to-human interaction. This paper demonstrates the success of one such machine learning algorithms, delineating each machine learning architecture, and processes alternate fields where learning techniques can be applied for robotic automation.

关键词： Automation machine learning algorithms Navigation machine learning Natural language processing Disruptive technologies Robots

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Decision systems for Adaptive Cybersecurity Incident Response 12th

Decision Systems for Adaptive Cybersecurity Incident Respons...

引用

12th international conference on Frontiers of intelligent Computing: Theory and Applications, FICTA 2024

作者： Goyal, Dinesh Gandhi, Yatin Dongre, Deepika Shailesh Bhagat, Gorakh P. Pawar, Rohit Department of Computer Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India Competent Softwares Maharashtra Pune India Artificial Intelligence and Data Science VIIT Pune India Electronics and Communication Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Traditional methods of responding to cybersecurity incidents often use set playbooks or routines that don’t change, which might not work well with the constantly changing nature of modern cyber dangers. The idea behind this paper is to create and use decision systems that are specifically designed for adapting how to handle hacking incidents. Advanced artificial intelligence (AI) methods, such as machine learning and natural language processing, are used by these decision systems to analyze security events in real time and help people make quick, well-informed decisions. These systems improve the speed and efficiency of hacking reaction by constantly learning from past events and responding to new threats. Automatic danger recognition, risk assessment, and reaction planning are important parts of the suggested decision systems. Automated systems that look for threats use AI algorithms to look for signs of bad behavior in network traffic, system logs, and other relevant data sources. The risk assessment tools look at the dangers and how bad they could be, considering things like how sensitive the data is, how important the system is, and the rules that need to be followed. Decision systems actively plan responses based on danger recognition and risk assessment results. These responses may include separating affected systems, blocking suspicious IP addresses, or sending events to human experts for further study. Over time, these systems get better at making decisions by using adaptive learning methods and constant feedback loops. This makes it easier for them to spot and stop new cyber threats. The creating and using decision systems for adaptive cybersecurity incident reaction is a big step toward making organizations’ defenses more resilient and effective in the face of changing cyber dangers. By using AI and machine learning, these systems stop threats before they happen, giving cybersecurity teams the tools they need to stay ahead of attackers in the ongoing battl

关键词： Cyber attacks

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Beyond Emotions: Affective Computing, VR, and Robotics in Mutual Awareness and Safe Interaction

Beyond Emotions: Affective Computing, VR, and Robotics in Mu...

引用

IEEE international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Paulo Menezes University of Coimbra | ISR-UC Coimbra Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

This talk explores the evolution of human-machine interaction, emphasizing affective computing for enhanced user experiences. Beyond basic emotion detection, it introduces “emotional activations” as crucial for intuitive, engaging systems. humans naturally respond with attraction or rejection to stimuli based on emotions, guiding interaction design toward simplicity and fidelity to foster trust and reduce frustration. This principle has influenced game-based approaches in VR/AR and robotics, enhancing engagement in learning, therapy, and *** is key to interactive systems, mirroring human-animal relationships where predictability and mutual awareness ensure safe coexistence. Just as pets recognize human presence and adapt, robots must perceive human cuessuch as gaze and body languageand express internal states. The talk presents applications in therapeutic and industrial contexts, emphasizing mutual awareness as a foundation for safer, more trustworthy, and engaging intelligent systems

关键词： Robots Robot sensing systems Service robots Affective computing Industries Technological innovation Research and development Medical services Interactive systems intelligent systems

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Harmonizing human-Centric Collaborative Hybrid Intelligence: A Deep Dive into the Transition from Industry 4.0 to Industry 5.0—A Case Study Experiment 4th

Harmonizing Human-Centric Collaborative Hybrid Intelligence:...

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4th international conference on Electronic Engineering and Renewable Energy systems, ICEERE 2024

作者： Kihel, Ali El Embarki, Soufiane Laboratory of Industrial Engineering and Seismic Engineering National School of Applied Sciences ENSA University Mohammed First Oujda Oujda Morocco

ISBN: (纸本)9789819799749

This study explores the evolution of the manufacturing industry, focusing on the transition to Industry 5.0 and its integration of advanced technologies like artificial intelligence, big data, and the Internet of Things (IoT). Industry 5.0 represents a radical shift towards adaptive, autonomous systems, building upon the advancements of its predecessors, particularly Industry 4.0. The analysis compares and contrasts Industry 4.0 and Industry 5.0, highlighting significant data-driven insights, including notable increases in AI applications and corresponding improvements in production efficiency and cost reduction during Industry 4.0. The study also investigates the rapid deployment of transformative technologies like 5G connectivity and quantum computing within Industry 5.0, projecting substantial growth in human–machine cooperation and decreases in error rates by 2033. Ultimately, the abstract outlines how Industry 5.0 will revolutionize smart manufacturing into intelligent, flexible, and responsive production systems, addressing both its potential benefits and challenges. This paper presents a detailed case study exploring the practical applications of IoT-driven innovations in Industry 5.0. Through analysis of IoT sensor data, specific operational conditions including factory temperature, warehouse humidity, and manufacturing line pressure are revealed. A data-driven strategy led to notable reductions in energy use and CO2 levels, facilitated by the diverse skills of case study participants. Key indicators of Industry 5.0, such as production efficiency, energy usage, quality control, and inventory turnover, demonstrate significant improvements, highlighting the importance of metrics evaluation and data-driven methodologies in showcasing the transformative potential of IoT-driven innovations within Industry 5.0. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality control

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FedScale: A Federated Unlearning Method Mimicking human Forgetting Processes 18th

FedScale: A Federated Unlearning Method Mimicking Human Forg...

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18th international conference on Wireless Artificial intelligent Computing systems and Applications, WASA 2024

作者： Huang, Wenshu Wu, Huiwen Fang, Liming Zhou, Lu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen China Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou311000 China

ISBN: (纸本)9783031714634

The heightened demands for privacy protection, combined with legal mandates granting users the right to be forgotten, emphasize the growing importance of endowing models with the capability to forget specific sample information. Unlearning is akin to the reverse process of model training, which involves the elimination of the influence of training data on a machine learning model. In the context of federated learning, conducting unlearning is notably more demanding, as data is dispersed across multiple client endpoints. Due to privacy and security concerns, many conventional machine unlearning methods are no longer applicable. This paper draws inspiration from the forgetting processes in the human brain, highlighting that memory attrition is not solely a passive process. Therefore, we dichotomize the federated unlearning process into two components: active forgetting and passive forgetting. We make respective modifications to the updates of these two components to achieve efficient unlearning. We propose an approach called FedScale, which only sacrifices a fraction of storage space, enabling a rapid federated unlearning process without the need for retraining the federated model when it becomes necessary to forget a user’s data. Experimental evaluations are conducted on four datasets, demonstrating the effectiveness of FedScale unlearning. Furthermore, we show its resilience against backdoor attacks and membership inference attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A Semantic Approach to Clinical Risk Detection: Early Warning systems with Knowledge Graphs

A Semantic Approach to Clinical Risk Detection: Early Warnin...

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cybernetics and Innovations (ICCI), international conference on

作者： Nutpraphut Prapatsirikul Siwasit Seangnikun Worayot Liamkeaw Jirawat Thawornphanthip Tharathon Utasri Akkharawoot Takhom Faculty of Engineering Thammasat School of Engineering Pathum Thani Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

Detecting early signs of clinical deterioration is vital for ensuring patient safety and enabling timely intervention. This paper introduces a hybrid Early Warning System (EWS) that integrates physiological monitoring with semantic symp-tom reasoning to support clinical decision-making. The system leverages automated machine learning to identify nonlinear patterns in historical vital sign data, while a curated knowledge base links patient-reported symptoms to standardized clinical concepts. A conversational large language model guides user interaction, asking context-aware questions to clarify ambiguous input and improve data completeness. Once both physiological and symptom-based inputs are harmonized, the system generates an interpretable EWS score and suggests potential diagnoses or symptom clusters. This approach enhances predictive accuracy while maintaining transparency and promoting trust among clin-icians. By combining data-driven modeling with semantic reasoning and interactive AI, the proposed framework demonstrates a promising direction for human-centered, next-generation clinical decision support systems.

关键词： Technological innovation Large language models Semantics Knowledge graphs Alarm systems Automated machine learning Cognition Safety Biomedical monitoring Next generation networking

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