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Study on Implementation of Shor’s Factorization Algorithm on Quantum computer

SN computer Science 2024年第4期5卷 413页

作者： Kumar, Mandeep Mondal, Bhaskar Department of Computer Science and Engineering National Institute of Technology Patna Patna 800005 India

The fundamental security aspect of the classical crypto-system depends on integer factorization and discrete logarithm problems. The quantum factorization problem is a crucial problem in quantum computing as it has significant implications for cryptography and security. Shos’s paper on “Polynomial-time algorithms for discrete logarithms and factoring on a quantum compute” has become a threat to the classical crypto-system, influencing many researchers to work on factorization problems using quantum computing. Quantum computers (QC) can be essential in running different factorization algorithms in polynomial time. However, practical implementation on larger numbers is still a major challenge due to the requirement for error correction and massive quantum devices. Although the quantum factorization issue puts traditional cryptographic systems at risk, it also opens up new possibilities for quantum communication and encryption. The challenge of factorization opens up an entirely new field for research into quantum communication protocol security, including quantum key distribution and the development of quantum-resistant cryptographic systems. This paper surveys the various quantum algorithms for factoring a number into prime integers. We present a simulation study of the Quantum factorization algorithm to determine the period of a process. Turning the factoring problem into the challenge of determining a function’s period is the essential strategy of practical implementation using the quantum circuit. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Factorization Quantum Fourier Transformation Quantum period finding Quantum phase estimation Shor’s algorithm

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An Integrated Approach of Efficient Edge Task Offloading Using Deep RL, Attention and MDS Techniques

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SN computer Science 2024年第6期5卷 681页

作者： Priyadarshni Kumar, Praveen Kadavala, Dhruvan Tripathi, Shivani Misra, Rajiv Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

In Distributed Computation Optimization (DCO) networks, where clients distribute computational jobs among heterogeneous helpers with different capacities and pricing models, efficient task offloading and cost reduction continue to be major issues. In order to tackle this problem, a unique method utilizing Deep Reinforcement Learning (DRL) is presented in this study. The DRL algorithm allows clients to independently identify the best method of action without requiring prior knowledge of network dynamics since it adjusts to the dynamic and stochastic nature of DCO environments. In distributed computing environments, the combination of Maximum Distance Separable (MDS) and DRL approaches provides a stable framework for task offloading. The suggested method finds the best policy for helper selection and task offloading by combining reward estimation, action selection, environment modeling, and iterative learning approaches. In particular, energy consumption, density, and total rewards show that the DRL algorithm performs better in experimental evaluations than traditional techniques like Q-learning and random selection. The algorithm’s performance is further improved by the integration of DQN with the attention mechanism, highlighting its potential to completely transform the efficiency of DCO networks. This work highlights how DRL approaches have a significant effect on network operations optimization and offers insightful information about future developments in distributed computation paradigms. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep reinforcement learning Distributed computation optimization Edge computing MDS techniques Task offloading

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Decision Tree Ensemble Approach for Recidivism Risk Classification Among Heinous Crime Convicts

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SN computer Science 2024年第1期5卷 195页

作者： Singh, Aman Mohapatra, Subrajeet Department of Computer Science Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

Early prediction of violent recidivism is one of the most essential tasks of the judiciary system. Being able to accurately forecast violent recidivism in offenders with heinous crime conviction can facilitate judiciary in taking decisions about sentencing, release, parole, and probation. Additionally, because violent offenders have a higher recidivism rate than nonviolent offenders, the development of intelligent risk assessment tools for early prediction of violent recidivism becomes highly essential. However, few such approaches exist, and their efficacy in the Indian context is often limited. As a result, in the present work, the aim is to develop an computational learning-based risk assessment tool for early prediction of violent recidivism among criminals with heinous crime convictions. In particular, we classify individual offenders as high, low, or moderate risk based on a variety of factors such as anger level (a psychological emotion), demography, and psycho-social factors. Effectiveness of different machine learning models ***, PNN, SVM, KNN, LDA have been simulated for recidivism risk assessment of offenders. The current findings demonstrate that violent recidivism at the individual level can be anticipated using a bagged tree ensemble classifier achieving maximum accuracy of 89 % . © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Ensemble learning Machine learning Quantitative risk assessment Violent recidivism

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Strategies for Building and Maintaining Secure Web Applications 5

Strategies for Building and Maintaining Secure Web Applicati...

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5th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2024

作者： Sathiyapriyan, C. Manikandan, B. Gokul, S. Satheesh, Rewin Nivetha, S. Leelavathy, S. Aarupadai Veedu Institute of Technology Cyber Security Department of Computer Science and Engineering Chennai603104 India Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai603104 India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350395235

Ensuring the security of web applications is paramount in safeguarding sensitive data and maintaining user trust. This study presents a comprehensive overview of strategies for building and maintaining secure web applications, acknowledging their inherent limitations and outlining potential areas for future work. The strategies discussed encompass fundamental aspects of web application security, including input validation and sanitization, robust authentication and authorization mechanisms, secure communication through HTTPS, timely security patching, implementation of security headers, monitoring and logging, client-side security measures, and vigilance in handling third-party components.. This research employed algorithms include Routing Algorithms, Middleware Execution Order Algorithms, Eloquent ORM Algorithms, Encryption and Hashing Algorithms, Session Handling Algorithms, Queue Management Algorithms, and Dependency Injection Algorithms. © 2024 IEEE.

关键词： Sensitive data

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Open-source solutions for real-time data retrieval in industrial automation and IoT environments

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International Journal of Internet Manufacturing and Services 2025年第1期11卷 73-92页

作者： Hegde, Seema B. Reddy, Tadigotla Narendra Prasannan, Prasad Manjunath, K.V. Herbert, Mervin A. Rao, Shrikantha S. Department of Computer Science and Engineering Global Academy of Technology Bengaluru560098 India Smart Manufacturing Precision Machine Tools Central Manufacturing Technology Institute Bengaluru22 India Department of Industrial Engineering and Management Siddaganga Institute of Technology Tumkur572103 India Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India

Digitalisation of the manufacturing industries due to the implementation of the ‘industrial internet of things (IIOT)’ is a key enabler for improved productivity and reliability at a reduced labour cost. The industrial IOT connects all the industrial machines such as PLCs, CNCs, and robots through a robust network. The generated data by these end devices plays a vital role in industrial automation, however acquiring the data from machines specifically legacy machines using various communication protocols is the biggest challenge and costly process, especially for MSMEs. Hence this paper discusses the usage of the open-source framework for real-time data acquisition from industrial machines and its implication in Industry 4.0. The paper implements and validates the possibility of the usage of an open-source framework for data acquisition instead of vendor-specific licensed software using several test cases. The paper also validates and proves ‘Wireshark’ can be a universal open-source solution for data acquisition using any standard communication protocols from various vendor-specific machines. Hence this work provides a novel solution for the digitalisation of the MSME manufacturing industries efficiently at the reduced maintenance cost and improve their productivity. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Cost reduction

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ICE-VDOP: an integrated clustering and ensemble machine learning methods for an enhanced vector-borne disease outbreak prediction using climatic variables

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International Journal of Information technology (Singapore) 2024年第4期16卷 2077-2088页

作者： Jayabalan, Divya Elango, Sivasankar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli 620015 India

Dengue is prone to cause frequent disease outbreaks. An early outbreak prediction based on the climatic variables is essential to control the disease’s spread and avoid possible outbreaks. Existing non-seasonal outbreak classification uses a partition-based clustering method for outlier removal;however, a standard outlier detection with improved prediction performance is needed to detect outbreaks. We develop ICE-VDOP model, an Integrated Clustering and Ensemble machine-learning model for Vector-borne Disease Outbreak Prediction to classify outbreaks based on non-seasonal climatic characteristics for Bangkok and Bangladesh datasets. The work begins with identifying the clustering approach that generates a refined dataset ensuring the data points’ inherent structure is preserved and does not alter the association of climatic variables and Dengue Incidence. The partitioning-based clustering methods modify the direction of association between climatic variables and Dengue Incidence after outlier removal as it removes the essential data points. The comparative results shows that BIRCH (balanced iterative reducing and clustering hierarchy), a hierarchical clustering technique for outlier removal captures finer non-seasonal climate variables (temperature, rainfall and relative humidity) and gradient boosting (GB) achieves a prediction accuracy of 97% for Bangkok and 98% for the Bangladesh datasets. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Binary classification BIRCH Dengue Gradient boosting Outlier detection Vector-borne disease outbreak prediction

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A cross entropy and whale optimization algorithm based image segmentation for aerial images

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International Journal of Information technology (Singapore) 2024年 1-15页

作者： Ahmed, Saifuddin Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Cachar Silchar 788010 India

There are several image segmentation techniques for image processing such as, thresholding, region based, edge based, watershed etc. However, the multilevel thresholding based image segmentation is the most widely used approach. This approach mostly suffers high computational complexity with the increasing number of threshold levels. The researchers have been using optimization techniques to resolve this issue. This paper presented a multilevel thresholding technique for segmenting aerial images using Whale Optimization Algorithm (WOA). It uses Minimum Cross Entropy (MCE) as an objective function for selecting a set of optimum threshold values. The presented method is evaluated by using a set of standard aerial images. The evaluated performance of the proposed technique is compared with Bacterial Foraging Optimization (BFO), Moth-flame Flame optimization (MFO) and Particle Swarm Optimization (PSO). The overall performance and the quality of segmented images are evaluated based on the following parameters, Average Mean Structural Similarity Index (MSSIM), Average Feature Similarity Index (FSIM), Average Peak Signal to Noise Ratio (PSNR), average mean objective functions values and average CPU rum time values. In terms of quality of the segmented images based on MSSIM, FSIM and PSNR values the presented method performs better than other methods and we found the average CPU run time also to a considerable range. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Cross entropy Image segmentation Multilevel thresholding Optimization technique Whale optimization algorithm

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DL Based Speech to Text Converter for Audio Visual Applications 3

DL Based Speech to Text Converter for Audio Visual Applicati...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kulkarni, Rekha Takalikar, Mukta Awatade, Nikhil Bhalgat, Ameya Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350343274

In the digital era, Speech recognition and Text conversion has become a prominent research field with numerous applications. The accurate recognition of speech and quick conversion possess number of challenges like accent, mixed words, variation in pronunciation etc. The proposed system leverages the power of machine learning algorithms to accurately recognize speech and convert it into text in real time. The system is trained using the ML modules like speech recognition, moviepy etc. Overall, our work demonstrates the potential of machine learning-based approaches for speech recognition and text conversion and highlights the need for further research in this area to address the challenges and limitations of existing systems. © 2023 IEEE.

关键词： Speech recognition

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI 4

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swarna Sudha, M. Manjula, S. Bharathi, Indira Krishnasamy, Valarmathi Vijayalakshmi, K. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India School of Computer Science and Engineering Vellore Institute of Technology Chennai India PSR Engineering College Department of Electronics and Communication Engineering Sivakasi India

ISBN: (纸本)9798331523923

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions. © 2025 IEEE.

关键词： Contrastive Learning

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An Experimental Analysis of Machine Learning Models for Diabetes Classification 7th

An Experimental Analysis of Machine Learning Models for Di...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Ghosh, Subhayu Acharya, Riyan Jana, Nanda Dulal Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819764648

Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection and effective management of diabetes are crucial to prevent severe complications and improve the quality of life for affected individuals. Machine Learning (ML) techniques have shown great promise in aiding the early detection of various diseases on patient data, including diabetes. ML algorithms can analyze vast datasets, identify patterns, and make accurate predictions, helping medical professionals to diagnose diabetes at its early stages. In our work, we employed several ML models for diabetes classification using different datasets. These models include K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient-Boosted Model (GBM), eXtreme Gradient Boosting (XG-Boost), Adaptive Boosting (Ada-Boost), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). We performed a comparative analysis of their performance on three distinct datasets using evaluation metrics like accuracy, precision, F1-score, sensitivity, specificity and Cohen’s Kappa Value. Our findings revealed that the RF algorithm is optimal for symptoms-based and primary lab report-based diabetes detection, while XG-Boost excels in classifying different types of diabetes from a multi-class dataset. Moreover, we investigated diverse symptoms and their impact on diabetes outcomes, offering insights into preventive measures and early stage monitoring for this disease classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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