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Verification of news and certificate, and plagiarism detector

Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease Detection

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SN computer Science 2024年第1期5卷 1-14页

作者： Goyal, Palak Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

A progressive neurodegenerative ailment called Parkinson's disease (PD) is marked by the death of dopamine-producing cells in the substantia nigra area of the brain. The exact etiology of PD remains elusive, but it is believed to involve the presence of Lewy bodies, abnormal protein aggregates, in affected brain regions, leading to the mobile symptoms of PD. Hence, as the management of PD continues to evolve, there is a growing demand for the establishment of a descriptive system that enables the early detection of PD. In this study, we conducted an extensive analysis using machine learning, ensemble learning, and deep learning models with different hyperparameters to develop accurate classification models for PD prediction. To enhance classifier performance and address overfitting, we employed principal component analysis (PCA) for feature selection along with various preprocessing techniques. The dataset used consisted of voice samples, comprising 188 PD patients and 64 normal individuals. Our results demonstrated that the Random Forest (RF) model with accuracy of 82.37% outperformed the other base classifiers Among the ensemble classifiers, the LGBM model exhibited the highest accuracy of 85.90% when compared to both base and ensemble classifiers. Notably, the deep learning model has 91.33% training accuracy and 85.02% testing accuracy, suggesting that deep learning models perform comparably equivalent on small datasets compared to machine learning classifiers. Overall, our findings underscore the effectiveness of machine learning, ensemble techniques and deep learning models in accurately predicting PD. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Hyperparameters LGBM Machine learning Parkinson’s disease Random forest

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Improved hybrid text summarization system using deep contextualized embeddings and statistical features

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Multimedia Tools and Applications 2025年第14期84卷 13929-13958页

作者： Gambhir, Mahak Gupta, Vishal Thapar Institute of Engineering and Technology Punjab Patiala India University Institute of Engineering and Technology Panjab University Chandigarh India

In this digital world where an enormous volume of textual material is growing on the internet every single day, there is a great need for systems that can produce human-like summaries automatically. In the past, a number of extractive text summarization methods have been proposed that either used statistical or semantic techniques. Therefore, we have developed a novel hybrid model of text summarization, AttSum-Hybrid that takes into consideration language context and relationship between the text as well as captures structural information of the sentences while creating an extractive summary of the document. This hybrid summarization framework combines a deep learning-based contextual model with the statistical feature-based model. BERT (Bidirectional Encoder Representations from Transformers) is used as a feature extractor in the contextualized representation model. For learning semantic and syntactic relationships from the textual sequence, this model also uses a Convolutional Bi-LSTM network (Bidirectional Long Short Term Memory). On the other hand, we have developed a statistical feature representation framework that incorporates a few better-performing sentence scoring features. With the Daily Mail corpus, ROUGE recall scores for R-1, R-2, R-L measures are generated as 38.67, 15.37, 34.12, respectively whereas, with DUC 2002 dataset, ROUGE scores are computed as 59.74, 28.80, 57.87, respectively. Full-length ROUGE F1 scores of 43.15, 20.08, and 39.55 have been obtained by carrying out experiments with the combined CNN/Daily Mail dataset. Overall, our proposed hybrid model of summarization has achieved improved results as compared to the state-of-the-art baseline techniques. These results have been further validated by two summary validation tasks (Question answering task and Number of common sentences in the extract) performed by three human experts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Long short-term memory

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Application of fly ash-TiO2 composites for enhanced adsorption and photocatalytic degradation of fuchsin dye

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Environmental Science and Pollution Research 2024年第58期31卷 66248-66261页

作者： Sharma, Ridhima Pal, Bonamali Barman, Sanghamitra Department of Chemistry and Biochemistry Thapar Institute of Engineering and Technology Punjab Patiala147004 India Department of Chemical Engineering Thapar Institute of Engineering and Technology Punjab Patiala147004 India

It is important to accomplish both photocatalytic efficiency and adsorption capacity in the catalyst to produce a highly effective photocatalyst for complete dye removal from wastewater. In the current investigation, TiO2 and fly ash-TiO2 composites (0.5–5 wt%) were fabricated by sol–gel and wet-impregnation process, and their catalytic performance was evaluated for the effective adsorption and photocatalytic degradation of fuchsin blue dye. The catalysts were characterized by XRD, UV-DRS, SEM, EDS, RAMAN spectroscopy, and N2 adsorption analysis Adsorption and degradation studies were conducted to investigate factors such as pH (2 − 10), adsorbent dose (1–9 mg), contact time (30–180 min), and initial adsorbate concentration (5–30 mg/L) to eradicate fuchsin dye from water. The 5 wt% fly ash-TiO2 catalyst showed a maximum capacity for adsorption of 20.32 mg/g and 76% removal of fuchsin dye. The Langmuir isotherm model fitted well to the experimental data, whereas the adsorption process obeyed pseudo-first-order kinetics. When exposed to UV light, a 5 wt% fly ash-TiO2 composite demonstrated maximum photocatalytic degradation of 88% after 180 min, followed by a pseudo-first-order kinetic model. The catalyst was easily reusable for five cycles without losing dye removal effectiveness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Titanium dioxide

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Compression-Encryption Based Efficient Dual Image Watermarking for e-Healthcare Applications 1

Compression-Encryption Based Efficient Dual Image Watermarki...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Malhotra, Gautam Atri, Abhimanyu Rani, Jyoti Anand, Ashima Thapar Institute of Engineering and Technology Computer Science and Engineering India

ISBN: (纸本)9798350364590

The requirement to protect the authenticity and accuracy of images motivates the implementation of water-marking on these images. Any alteration or interference with medical images might lead to erroneous diagnosis or treatment, as these images are vital for diagnosing and tracking diseases. Moreover, these photographs are regularly transmitted across the internet, increasing the potential for unauthorized access and alteration. Therefore, medical image watermarking is essential to safeguard the reliability and accuracy of these medical images in research and clinical environments. This paper presents a framework of a highly safe and reliable dual watermarking technique that utilizes the NSST-Radon transform to improve medical data security. The ultimate watermarked image is created by incorporating fingerprint and patient information into a medical image, resulting in dual watermarking. After embedding, the final watermarked image is compressed and encrypted to add a security layer. Moreover, our approach uses the best scaling factor to manage the robustness and imperceptibility tradeoffs. The experimental results indicate that the suggested approach demonstrates exceptional overall performance, a high level of imperceptibility, and resilience. The proposed scheme uses dual watermarking to safeguard original medical and non-medical images. The experimental and comparison results show that this strategy produces superior results with remarkable resilience, invisibility, and capacity. © 2024 IEEE.

关键词： Medical imaging

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Revolution of Self-Learning in Education from Traditional Pedagogy

Revolution of Self-Learning in Education from Traditional Pe...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Aggarwal, Aarush Pannu, Husanbir Singh Kaur, Varleen Sidhu, Sanmeet Thapar Institute of Engineering and Technology Computer Science and Engineering Patiala India Thapar Institute of Engineering and Technology Centre for Training & Development Patiala India

ISBN: (纸本)9798331508685

The transition from traditional pedagogy to self-learning represents a significant shift in educational practice, propelled by technological advancements and the evolving needs of learners. This study explores the evolution from ancient Gurukul systems to modern digital learning platforms, incorporating data from student survey responses. The analysis reveals a marked preference for hybrid learning models (62.4%) and the widespread use of online resources among participants (91%). Supported by established learning theories and quantitative analysis, the findings indicate that self-learning enhances critical thinking, adaptability, and lifelong learning competencies. The results suggest a future where technology-integrated, personalised learning complements conventional classroom instruction, fostering more independent and proficient learners. © 2025 IEEE.

关键词： Federated learning

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A Deep Learning Approach for Intelligent Diagnosis of Lung Diseases

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SN computer Science 2024年第8期5卷 1026页

作者： Paswan, Jai Dev Bhatia, Tarunpreet Lamba, Sonu Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala 147001 India

According to the World Health Organization (WHO), lung diseases contribute to millions of fatalities globally each year. Pneumonia stands out as a leading cause, claiming the lives of approximately 2.5 million individuals. Tuberculosis accounted for 1.4 million deaths in 2019 alone. Furthermore, COVID-19, the coronavirus, has resulted in over 9 million reported fatalities worldwide as of July 2024. Addressing the burden of lung diseases is crucial for public health, emphasizing the necessity of initiatives aimed at prevention and management to enhance global health outcomes. Deep learning for medical image analysis serves as a valuable resource for both academic and industrial researchers keen on delving into medical image analysis. This study explores a range of image pre-processing techniques, including Gaussian blur, total variation denoising, histogram equalization, and CLAHE, applied to a chest X-ray image dataset. These techniques aim to enhance image quality, thereby facilitating more accurate disease classification by the model. The study focuses on multiclass classification en-compassing normal, viral pneumonia, bacterial pneumonia, tuberculosis, and COVID-19 classes. To accomplish this, four deep learning models-VGG16, VGG19, ResNet50, and DenseNet121 are employed for disease detection from chest X-ray images, with their performances compared. Results indicate that VGG16 achieves the highest accuracy, with 94.04% for training and 87.80% for testing. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chest X-ray Deep learning models Image pre-processing Lung disease

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Soundtrack Success: Unveiling Song Popularity Patterns Using Machine Learning Implementation

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SN computer Science 2024年第3期5卷 278页

作者： Arora, Shruti Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Music streaming services are getting increasingly popular as a result of the prevalent need for web and smart gadgets. Melophiles are drawn toward a range of musical genres and create a unique digital footprint. The emotional reactions of the music listeners may cause physiological changes as well as neurological benefits. However, any musician should avoid being influenced by an algorithmic recommendation presented in the research. The most prominent aspect for any musician is to stay true to their own musical style and to be novel, honest, and sincere. The main objective of this research work is to provide precise music predictions to the artists before they launch their music in the market. This work also aims to improve the accuracy of the prediction models using some feature-engineering-based techniques to manipulate the Spotify dataset and predict whether the song will be a hit or not. The proposed work leverages state-of-the-art machine learning models, including Linear Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest, GBM, and neural networks to explore the predictive factors influencing the inclusion of a particular music track in the featured Spotify Hit-50 list. Furthermore, it offers descriptive statistical analysis of the features that can help in popularity prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Hit song science audio features Music information retrieval Music streaming data Regression

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Enhancing Indian sign language recognition through data augmentation and visual transformer

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Neural Computing and Applications 2024年第24期36卷 15103-15116页

作者： Singla, Venus Bawa, Seema Singh, Jasmeet Department of Computer Science Thapar Institute of Engineering and Technology Adarsh Nagar Punjab Patiala147004 India

This paper introduces a novel approach to Indian Sign Language Recognition (ISLR) by integrating Keras, Visual Transformers (ViT), and sophisticated data augmentation techniques. Our methodology emphasizes the development of a Vision Transformer model trained on a comprehensive dataset, enhanced with both image data and keypoint information, leveraging the capabilities of the Mediapipe library. To improve the model’s ability to generalize, we applied advanced augmentation strategies, including ImageDataGenerator, among others. Through extensive experimentation, involving rigorous hyperparameter optimization across numerous epochs, we sought to determine the most effective model configuration. The results of our validation process revealed a promising evaluation loss of 0.2941 and an impressive accuracy rate of 97.52%. This integration of data augmentation techniques with ViT transformers establishes a groundbreaking framework in the field of ISLR, underscoring its significant potential for practical implementation and marking a substantial progression in the technology for sign language recognition. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image enhancement

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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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computer Modeling in engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi Krishan Kumar Neeraj Kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

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