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Committed-programming reductions: formalizations,implications and relations

science China(Information sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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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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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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CNN-LSTM: A Novel Hybrid Deep Neural Network Model for Brain Tumor Classification

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Intelligent Automation & Soft computing 2023年第7期37卷 1129-1143页

作者： R.D.Dhaniya K.M.Umamaheswari Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603202India Department of Computing technologies SRM Institute of Science and TechnologyKattankulathur603202India

Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.

关键词： Brain tumor segmentation particle swarm optimization CNN-LSTM convolution neural network

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Optimal weight support vector regression ensemble with cluster-based subsampling for electricity price forecasting

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Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 1-16页

作者： Li, Yuerong Zhang, Yuhua Che, Jinxing School of Science Nanchang Institute of Technology Jiangxi Nanchang China Key Laboratory of Engineering Mathematics and Advanced Computing Nanchang Institute of Technology Jiangxi Nanchang China

Accurate prediction of short-term electricity price is the key to obtain economic benefit and also an important index of power system planning and management. Support vector regression (SVR) based ensemble works have gained remarkable achievements in terms of high accuracy and steady performance, but they are highly dependent on data representativeness and have a high computational complexity O (k ∗ N 3) of data samples and parameter selection. To further improve the data representativeness and reduce its computational complexity, this paper develops a new approach to forecast electricity price via optimal weighted ensemble. In the model, the cluster-based subsampling algorithm is proposed to categorize the inputs being seasonally decomposed into several groups, and representative data are drawn from each group in a certain proportion to ensure that each subset trained with SVR has the same representativeness and features. Moreover, the optimal weighted combination method is presented to assign weights to the sub-SVRs to obtain the optimal support vector regression ensemble model (OWSSVRE). The experimental results show that the improved support vector regression ensemble model with the same features and representativeness of the subset has better performance in electricity price forecasting. As a result, it is suitable to support decision making in the energy and other sectors. © 2024 - IOS Press. All rights reserved.

关键词： K-means clustering

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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An Optimized YOLO-based License Plate Recognition System with Integrated Privacy Safeguards 2

An Optimized YOLO-based License Plate Recognition System wit...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Putluru, Aravinda Reddy Pannuru, Vivekananda Reddy Ashokkumar, C. Kumar, C. Jothi School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies Chennai India

ISBN: (纸本)9798331505745

Efficient and accurate license plate recognition (LPR) is crucial for intelligent transportation systems, surveillance, and security applications. This work proposes a Powered Smart License Plate Recognition System utilizing YOLOv8 for real-time detection and Optical Character Recognition (OCR) for character extraction. The system integrates publicly available datasets, including OpenALPR, CCPD, and UFPR-ALPR, along with custom-annotated data to enhance robustness. Data preprocessing involves normalization, augmentation, and privacy protection measures such as Gaussian blurring and encryption to ensure compliance with data protection regulations. The architecture is optimized using convolutional layers for feature extraction and attention mechanisms for improved character recognition. A privacy protection module ensures sensitive information masking, while Edge AI deployment on Jetson Nano or Raspberry Pi enables real-time inference with minimal latency. Model training employs transfer learning with pre-trained YOLOv8 weights, fine-tuned on license plate datasets. Optimizers such as Adam and SGD, along with IoU loss, enhance bounding box accuracy, ensuring precise detection. Performance evaluation demonstrates high mean Average Precision (mAP) exceeding 95% and OCR accuracy surpassing 90%, validating the system's efficiency across diverse environmental conditions. Applications include automated toll collection, traffic monitoring, vehicle tracking, and secure entry management in smart cities. Law enforcement agencies can utilize the system for stolen vehicle identification and traffic rule enforcement. The proposed approach offers a scalable, privacy-aware, and high-accuracy solution for modern LPR applications, ensuring compliance with security and regulatory standards. © 2025 IEEE.

关键词： Optical character recognition

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From Buzz to Blockbuster: Predicting Movie Revenue Using a Hybrid Approach Combining Machine Learning and Sentiment Analysis

From Buzz to Blockbuster: Predicting Movie Revenue Using a H...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vardhan, Senapathi Vishnu Balaji, Killemsetty Viswa Sai Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable opportunity to understand audience engagement and sentiment, which are critical in forecasting box office performance. By analyzing data from various social media platforms alongside movie metadata from sources like IMDb and TMDb, this study seeks to create a comprehensive model that can predict a movie's opening weekend revenue and classify it as a 'Hit,' 'Average,' or 'Flop.' The work highlights the value of audience sentiment, pre-release engagement, and historical box office trends in predicting financial outcomes. By using a combination of quantitative metrics (likes, shares, views) and qualitative data (sentiment scores, discussion themes), the study aims to refine revenue predictions and improve classification accuracy. The hybrid model's ability to incorporate both traditional and real-time data offers several advantages, including better responsiveness to audience perceptions, the ability to identify emerging trends, and more accurate revenue forecasts. This research provides actionable insights that can aid movie studios, marketers, and investors in decision-making processes, ultimately reducing financial risks associated with movie releases. The findings contribute to the growing field of predictive analytics in the entertainment industry, offering a novel and data-driven approach to understanding and forecasting movie success. © 2025 IEEE.

关键词： Predictive analytics

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A Transformer-Based Approach for Fake News and Spam Detection in Social Media Using RoBERTa

A Transformer-Based Approach for Fake News and Spam Detectio...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Pal, Ankit Abhijit Mondal, Sudin Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

The spread of misinformation and spam on social media has become a critical challenge, undermining information integrity and online security. Addressing this pressing issue, this study introduces an advanced solution utilizing RoBERTa, a transformer-based deep learning model, for the effective detection of fake news and spam. By employing sophisticated Natural Language Processing (NLP) techniques, the proposed method demonstrates exceptional accuracy in classifying unreliable content, making it a valuable tool in today's digital communication landscape. The approach is designed to provide real-time detection capabilities, ensuring swift identification and mitigation of misinformation and spam. Its adaptability allows seamless application across different domains, enhancing its versatility. Furthermore, the solution emphasizes sustainability by incorporating continuous monitoring and retraining mechanisms, which enable the model to maintain its performance even as the nature of misinformation evolves. This work underscores the transformative potential of deep learning models like RoBERTa in combating the widespread dissemination of online threats. By ensuring precise, scalable, and timely detection, the study contributes to creating a safer, more trustworthy digital ecosystem. In doing so, it addresses a critical need in the current era, where the reliability of information is paramount for societal and individual well-being. © 2025 IEEE.

关键词： Contrastive Learning

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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Computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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