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Informatica (Slovenia) 2025年第14期49卷 93-110页

作者： Kaushal, Arush Gupta, Ashok Kumar Sehgal, Vivek Kumar Department of Computer Science and Information Technology Jaypee University of Information Technology Solan171234 India Department of Civil Engineering Jaypee University of Information Technology Solan171234 India

Earthquakes have the potential to cause catastrophic structural and economic damage. This research explores the application of machine learning for earthquake prediction using LANL (Los Alamos National Laboratory) dataset. The data, obtained from a laboratory stick-slip friction experiment, simulate real earthquakes through digitized acoustic signals recorded against the time to failure of a granular layer. We introduced a hybrid model combining CatBoost and Support Vector Regression (SVR) to predict the time of the next earthquake, evaluating its performance against individual CatBoost and SVR models. The hybrid model demonstrated superior accuracy with a Mean Absolute Error (MAE) of 0.0825, outperforming the individual models. We implemented feature engineering to optimize the predictive capability of the models. Additionally, we compared our hybrid model's performance with previous studies to validate its efficacy. Our findings underscore the potential of machine learning, particularly hybrid models, in enhancing earthquake prediction accuracy. This study highlights the robustness and effectiveness of the hybrid CatBoost-SVR model, paving the way for advanced AI algorithms in seismology and contributing to improved disaster preparedness and mitigation strategies. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Stick-slip

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Exploring Green Inhibitors from Microbes and Plants: Current Trends and Future Perspectives in Sustainable Approach

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Journal of Bio- and Tribo-Corrosion 2025年第2期11卷 1-9页

作者： Gautam, Priya Saurav Shankar, Jata Department of Biotechnology and Bioinformatics Genomic Laboratory Jaypee University of Information Technology Solan India Department of Civil Engineering Civil Engineering Laboratory Jaypee University of Information Technology Solan India

Corrosion control of metals is essential for the durability and security of many industrial infrastructures. Given the traditional corrosion inhibitors, which can harm the environment and human health because of their non-biodegradable nature and toxicity. This review focuses on green inhibitors, which come from natural sources, and have become a possible alternative in response. The application of natural substances, especially plant extracts, as environmentally acceptable corrosion inhibitors is the main topic of this paper. Additionally, more research is being done to determine whether biogenic extracts made from agricultural wastes can be used as efficient inhibitors, emphasizing the role these extracts can play in reducing corrosion in acidic settings and stifling the growth of bacteria linked to biocorrosion. These green inhibitors have advantages over their conventional equivalents in terms of the environment and economy, which highlight their potential for long-term corrosion protection in a variety of industries, including the food and petroleum sectors. In summary, the extract derived from plant sources, such as leaf extract, fruit waste, exhibited bioactive compounds like acetophenone, methyl commate E, phytol, and stigmasterol from Calyptocarpus vialis (CV) Delonix regia, ethanolic extract from Ammi visnaga, banana peel from Musa species (fruit waste), Argemone Mexicana (plant and root extract), Terminalia catappa (dry product from leaf extract), and compounds from Brugmansia suaveolens such as norhyoscine, noratropine, apohyoscine, and Cassia paradisiacal are reviewed as green inhibitors. The necessity for more optimization and scaling-up of green inhibitor technologies is emphasized for research and development in this field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Fruits

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SORT-secured optimal routing technique for smart cities using IoTenabled wireless sensor networks

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Multimedia Tools and Applications 2025年 1-32页

作者： Gandhi, Charu Mohindra, Anubhuti Department of Engineering and Technology Gurugram University Gurugram India Department of CSE & IT Jaypee Institute of Information Technology Noida India

The advancement of smart cities and IoT technologies poses challenges such as the need for eco-friendly solutions and efficient data collection and communication in these environments. The objective is to propose a new architecture for data collection and communication in IoT smart applications, focusing on green IoT solutions and addressing the challenges of quality definition in smart cities. The proposed methods include the Secured Optimal Routing Technique (SORT) using Wireless Sensor Networks (WSN) with IoT, the CMBS algorithm for IoT sensor cluster formation, the Enhanced Differentiation Search (EDS) algorithm to estimate sensor node trust, and a lightweight signcrypt approach for data encryption. Additionally, the Optimum Decisive Former (ODF) method is used to compute the best route in the IoT platform. The SORT technique is implemented and evaluated using the NS2 tool for network simulation. The performance of the SORT technique is compared with existing techniques, demonstrating its effectiveness in addressing the challenges of data collection, communication, and eco-friendly IoT solutions in smart cities. The study concludes that the proposed SORT technique, along with the CMBS algorithm, EDS algorithm, and lightweight signcrypt approach, contributes to the advancement of intelligent and sustainable smart cities by improving data collection, communication efficiency, and eco-friendliness in IoT applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Sensor nodes

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Design of a Multifactor Unidentified Remote End User Authentication Mechanism for IoT Network

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第10期49卷 191-204页

作者： Sharma, Neha Dhiman, Pankaj Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat H. P. Solan India

The rapid proliferation of Internet of Things (IoT) devices, coupled with the rollout of advanced 5G networks, has generated significant concerns regarding security breaches. These concerns stem from the expanded attack surfaces that come with improved connectivity, making IoT systems more vulnerable to malicious threats. A crucial strategy to counter these security challenges is the implementation of robust user authentication methods. Despite numerous proposals for multi-factor authentication mechanisms, many of these systems exhibit weaknesses, particularly in their susceptibility to user impersonation attacks and the risks posed by stolen mobile devices. Furthermore, several schemes fail to incorporate essential features such as session key agreements or backup solutions for instances of lost or stolen devices and compromised private keys. To address urgent security challenges in IoT environments, we developed a three-factor user authentication system tailored for low-cost IoT devices. This system tackles critical vulnerabilities while maintaining low computing and communication costs, enhancing security without compromising usability and efficiency. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 5G mobile communication systems

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Advanced perspectives on big data security: Navigating challenges in cloud computing 4th

Advanced perspectives on big data security: Navigating chall...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Verma, Richa Bhatt, Ravindara Department of Computer Engineering Jaypee University of Information Technology Solan India

ISBN: (纸本)9781032911571

Recent years have seen the rise of big data workflow management solutions as widespread data analytic platforms for handling massive amounts of data in the cloud. However, keeping information private and ensuring the integrity of workflow applications is still a major and difficult issue. This document reviews and organizes research on large data workflow management privacy and security breaches and remedies. In this case, data encryption is utilized to safeguard information at rest and in transit. These points of view would allow researchers to better comprehend crucial research areas and create novel approaches. according to research question improve parameters of security by proposed approach. In this paper review of different workflows and security issue in cloud and other frame works © 2025 the Author(s).

关键词： Cloud security

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An Enhanced Aspect-Based Sentiment Analysis Model Based on RoBERTa For Text Sentiment Analysis

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 193-202页

作者： Chauhan, Amit Sharma, Aman Mohana, Rajni Jaypee University of Information Technology HP Solan173234 India Department of Computer Science Amity School of Engineering and Technology Amity University Punjab Punjab Mohali140306 India

Using an aspect-based sentiment analysis task, sentiment polarity towards specific aspect phrases within the same sentence or document is to be identified. The process of mechanically determining the underlying attitude or opinion indicated in the text is known as sentiment analysis. One of the most important aspects of natural language processing is sentiment analysis. The RoBERTa transformer model was pretrained in a self-supervised manner using a substantial corpus of English data. This means it was pretrained solely with raw texts and an algorithmic process to generate inputs and labels from those texts. No human labelling was involved, allowing it to utilise a vast amount of publicly available data. The authors of this work provide a thorough investigation of aspect-based sentiment analysis with RoBERTa. The RoBERTa model and its salient characteristics are outlined in this work, followed by an analysis of the model's optimisation by the authors for aspect-based sentiment analysis. The authors compare the RoBERTa model with other state-of-the-art models and evaluate its performance on multiple benchmark datasets. Our experimental results show that the RoBERTa model is effective for this important natural language processing task, outperforming competing models on sentiment analysis tasks. Based on the SemEval-2014 variant benchmarking datasets, the restaurant and laptop domains have the highest accuracy, scoring 92.35 % and 82.33 %, respectively. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Natural language processing systems

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Statistical Analysis and Data-Driven Modeling of Hole Circularity in EDD of Aerospace Material

Statistical Analysis and Data-Driven Modeling of Hole Circul...

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International Conference on Futuristic Advancements in Materials, Manufacturing, and Thermal Sciences, ICFAMMT 2024

作者： Dhaker, Kedari Lal Sharma, Amit Arif, Tasnim Shukla, Gaurav Shukla, Akshat Avikal, Shwetank Jaypee University of Engineering and Technology MP Guna473226 India University Dehradun India

ISBN: (纸本)9789819771134

Electric discharge drilling (EDD) plays an important role in the advancement of aerospace manufacturing due to its ability to produce complex hole geometries with high precision. In this study, a statistical analysis and data-driven modeling of hole circularity have been proposed during EDD of aerospace alloy. Statistical analysis-based prediction model is developed where the experimental results of hole circularity have been considered. These are the functions of four EDD process variables (i.e., Discharge current, Pulse on time, Pulse off time, and Flushing pressure). Through training and validation, the statistically learned model exhibits the complex relationships between input EDD process parameters and output geometry, enabling it to accurately predict the hole circularity. Reliability and effectiveness of the developed model are represented by R-Square and Adjusted R-Square value which are quite at suitable level of 88.8% and 75.7%, respectively. Furthermore, contour plots of interactive effect of process parameters are developed and influence of EDD interactive process parameters on hole circularity is discussed. Finally, optimal range of process parameters was found as: Discharge Current: 8–9 A, Pulse on time: 7.3–8 µs, Pulse off time: 2–2.5 µs, Flushing pressure: 75–80 bar. The proposed study demonstrates the precise prediction and optimization of drilled geometry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network security

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Hybrid Sampling Technique for Imbalanced Android Malware Family Classification

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SN Computer Science 2025年第3期6卷 1-16页

作者： Chauhan, Kshamta Gandotra, Ekta Department of Computer Science and Engineering and Information Technology Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

The rapid explosion of Android-based devices has led to a disturbing surge in the volume and sophistication of Android malware. Effective classification of these malicious applications is essential for safeguarding user security and privacy. However, the imbalanced nature of the Android malware data, where some of the families outnumber the other ones, poses a significant challenge to traditional machine learning algorithms. The issue of imbalanced data can be handled by oversampling and undersampling techniques. Oversampling raises the number of instances in the minority class and undersampling decreases the number of instances in the majority class in order to balance the class distribution. Oversampling and undersampling can lead to overfitting and loss of information. To combine the strengths of both techniques, hybrid sampling is used. This paper proposes hybrid sampling techniques that combine oversampling and undersampling methods. Two oversampling techniques, i.e., synthetic minority oversampling technique (SMOTE), and random oversampling (ROS), and three undersampling techniques, i.e., tomek's links (TOMEK), edited nearest neighbours (ENN), and neighborhood cleaning rule (NCR), are used. The proposed hybrid sampling methods are obtained after combining each oversampling technique with an undersampling one. All the hybrid sampling techniques are evaluated on a self-created malware dataset that consists of multiple imbalanced classes of malware families. Four machine learning models, i.e., support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbours (K-NN), are trained after applying the hybrid sampling techniques. From the experimental results, it is concluded that ROS-NCR outperforms other methods in handling imbalanced Android malware families. It achieves 95.81% accuracy and an F-measure of 0.95 using RF. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android malware Hybrid sampling Imbalanced data Machine learning Multi-class classification

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West university of Timisoara). All rights reserved.

关键词： COVID-19

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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