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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Influence of friction stir processing parameters on the mechanical properties of commercially available aluminium tubes

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Interactions 2024年第1期245卷 1-8页

作者： Kulkarni, Sudhanwa Mahesh Mali, Kiran D. Kulkarni, Dhananjay M. Thiruselvam, Iniyan Department of Mechanical Engineering Birla Institute of Technology and Science BITS-Pilani K. K. Birla Goa Campus Sancoale Goa Vasco-Da-Gama India

‘Friction Stir Processing (FSP)’ is an unconventional grain refinement technique. This metallurgical process is derived from a welding technique known as ‘Friction Stir Welding (FSW).’ FSP changes various mechanical properties of the base material, including ductility/elongation, fatigue properties, static mechanical properties, corrosion properties, hardness, etc. This study focuses on determining the effect of FSP on mechanical properties of commercially available square aluminium tubes. Different combinations of processing parameters namely, tool rotation speed and tool travel rate are considered during the experimentation. In all five different combinations of processing parameters are tested here. Other parameters such as tool depth, tool path, tool geometry etc. are not altered in the entire course of this investigation. The tensile strength was measured to evaluate mechanical properties of the tube material. Tensile specimens are sectioned from the friction stir processed side-walls of aluminium tubes in such a way so that the processed area occupies the central region along the length the tensile specimen. All the specimens are tested for their tensile behaviour under uni-axial quasi-static tensile loading using Universal Testing Machine (UTM). An appreciable difference is observed in tensile behaviour of specimens due to friction stir processing. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Grain refinement

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Remediation of Waste Engine Oil Contaminated Soil using Rhamnolipid based Detergent Formulation

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Materials Today: Proceedings 2023年 77卷 31-38页

作者： Gaur, Shailee Sahani, Aditya Chattopadhyay, Pradipta Gupta, Suresh Jain, Amit Department of Chemical Engineering Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan 333031 India

The utilization of waste substrates for rhamnolipid synthesis is a worthy alternative to conventional substrates to reduce the production cost of rhamnolipids. Rhamnolipid produced by Pseudomonas aeruginosa gi |KP 163922| using waste engine oil as substrate was investigated in batch and semi-batch studies for soil bioremediation. Green liquid detergent formulations were prepared by using environment-friendly builder (sodium citrate) and filler (isopropyl alcohol). Rhamnolipid, a biosurfactant was utilized in place of chemical surfactant to prepare the liquid detergent formulation. The formulations at different rhamnolipid concentrations i.e., below critical micelle concentration (CMC), at CMC, and above CMC, were tested for soil remediation efficiency. Each detergent formulation was characterized based on emulsification index (EI24%), surface tension reduction, foam ability, and foam stability. The in-house rhamnolipid based formulations above CMC, recovered oil up to 82.02 ± 0.938 % from contaminated soil with maximum surface tension reduction and foam volume as 26.5 ± 0.412 mN/m and 51.10 ± 1.37 mL respectively. The proposed remediation strategy demonstrated that the recovery of oil is possible at room temperature conditions. The performance properties including detergency and foaming of rhamnolipid based liquid detergent formulations were also compared with commercial rhamnolipid and other detergents. © 2022

关键词： Critical micelle concentration

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A multilayer network diffusion-based model for reviewer recommendation

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Chinese Physics B 2024年第3期33卷 700-717页

作者：黄羿炜徐舒琪蔡世民吕琳媛 Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Dataspace Hefei Comprehensive National Science CenterHefei 230088China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230026China

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy *** systems are powerful tools developed in computer science and information science to deal with this ***,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform *** this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual *** network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among *** results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking *** work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

关键词： reviewer recommendation multilayer network network diffusion model recommender systems complex networks

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An Efficient Multi-Objective Task Scheduling in Fog-Cloud Environment

An Efficient Multi-Objective Task Scheduling in Fog-Cloud En...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Mukherjee, Sayan Sengupta, Jayasree Bit, Sipra Das Indian Institute of Engineering Science and Technology Shibpur India Birla Institute of Technology Mesra India

ISBN: (纸本)9798350364866

The high communication bandwidth between end-devices and cloud servers along with the associated network latency, renders it infeasible for the modern day Internet of Things (IoT) applications to meet their stringent latency requirements. Hence, it is crucial to process application data closer to the network edge, both optimally and efficiently. To address this concern, we first design a multilayer fog-based loT system architecture. Next, we propose an optimal task scheduling scheme to uniformly distribute tasks amongst the fog nodes on the basis of Quality of Service (QoS) requirements of the application. Our proposed scheduling scheme optimizes delay, energy consumption and fog node utilization simultaneously while considering dependencies amongst sub-tasks in a way, such that delay and energy consumption is minimized whereas fog node utilization is maximized. We evaluate the performance of our proposed scheme both theoretically and experimentally through simulation using the iFogSim simulator. Our results establish that the proposed scheduling scheme efficiently distributes tasks amongst the available fog nodes as well as reduces the energy consumption of the fog nodes by 1.26% compared to a state-of-the-art competitor at the cost of a little extra latency. © 2024 IEEE.

关键词： Multiobjective optimization

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Dual reforming of model biogas for syngas production on Ni/γ-Al2O3 and Ni-C/ZSM-5 cordierite monolith catalysts

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Materials Today: Proceedings 2023年 76卷 393-397页

作者： Chava, Ramakrishna Mehta, Harshil Roy, Banasri Appari, Srinivas Department of Chemical Engineering Birla Institute of Technology& Science Pilani Pilani 333031 India

This work attempts to convert the model biogas on Ni/γ-Al2O3 and Ni-C/ZSM-5 into syngas using a dual-bed catalytic monolith reactor. The monolith is wash-coated with alumina and ZSM-5, respectively, followed by Ni and glucose-assisted Ni (Ni-C) loading using the wet impregnation technique. These two monoliths are loaded in an Inconel reactor and placed in a two-zone heating furnace. In dual reforming, either Ni/γ-Al2O3 or Ni-C/ZSM-5 monolith is used for dry reforming, and then Ni/γ-Al2O3 is used for steam reforming. A distance of ∼ 10 cm is maintained between these two monoliths. The exhaust gases from the first monolith are combined with steam before passing to the second monolith. The biogas reforming is carried out for a feed ratio (CH4:CO2) 1.5, GHSV of 1440 h−1 and 2880 h−1, at 800℃ and 1 atm pressure. The steam to CH4 ratio (S/C) is optimized to maximize the conversions (greater than80 %) of both CH4 and CO2. It was observed that the CH4 conversions increase with an increased S/C ratio due to the steam reforming in the second monolith. The TGA results show 7.6 % carbon formation on Ni-C/ZSM-5 and 35 % on Ni/γ-Al2O3 in dry reforming on the first monolith bed. © 2022

关键词： Zeolites

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Study on utilization of machine learning techniques for geological CO2 sequestration simulations

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Materials Today: Proceedings 2023年 72卷 378-385页

作者： Punnam, Pradeep Reddy Dutta, Arnab Krishnamurthy, Balaji Surasani, Vikranth Kumar Department of Chemical Engineering Birla Institute of Technology and Science Pilani Hyderabad Campus Hyderabad 500078 India

In Geological Carbon Sequestration (GCS), CO2 is injected and stored in geological formations. Conducting these studies through experiments could be highly impractical both economically and scientifically. Employing conventional numerical techniques was proven to be computationally intensive and slow with complex geometrical models. In this work, a study is conducted to integrate traditional numerical simulation results with machine learning techniques to forecast the futuristic trend of output parameters. Primary simulations are performed using multiphase flow modelling for the entire geological time scale. Then the time series neural network is used by considering CO2 sequestration parameter values as input and target data to forecast the trend of output parameters during the post-injection time. Both recurrent neural network models have shown a reasonable forecast of the output variables. Among the different training algorithms used, the Levenberg-Marquardt (LM) algorithm has given a good prediction for the output variable;the results are validated with the RMSE and R-squared values for obtained values. The R2 and RMSE values of the NAR model for structural trapping are 0.9801 and 0.0515, respectively, and for residual trapping, they are 0.9805 and 0.0506, respectively. This work will provide the initial understanding of integrated machine learning techniques in the GCS analysis for a heterogeneous reservoir model. © 2022

关键词： Carbon dioxide

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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

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Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Catalytic reduction of organic pollutants using novel Ni-Ce-Zr trimetallic metal organic framework

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Materials Today: Proceedings 2023年 72卷 47-54页

作者： Meshram, Anjali A Sontakke, Sharad M Department of Chemical Engineering Birla Institute of Technology and Science Pilani K. K. Birla Goa Campus Goa 403726 India

A novel trimetallic metal organic framework (MOF) was synthesized using a solvothermal method. Standard analytical techniques were used to characterize the synthesized materials for crystal structure, functional groups, morphology, surface area, pore size distribution and thermogravimetric analysis. The trimetallic Ni-Ce-Zr MOF was used to investigate the NaBH4-mediated catalytic reduction of dyes such as methylene blue (MB), Rhodamine B (Rh B) and methyl orange (MO) and phenols such as 2-nitrophenol (2-NP) and 4-nitrophenol (4-NP). The synthesized MOF imparted superior capacity to reduce all the selected organic pollutants. The recyclability of the synthesized MOF was studied upto four cycles. © 2022

关键词： Morphology

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