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Decentralized Query Solving Educational Website 2

Decentralized Query Solving Educational Website

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2nd International Conference on informatics, ICI 2023

作者： Jadhav, Swati Bhavik, Manasi Barde, Rohan Bhosale, Vaibhavi Bura, Rishita Amune, Amruta Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350343830

With the blockchain technology's quick advancement and the rising need for the Internet's partial decentralization, the use of underlying technology built on the blockchain has drawn a lot of attention. This research paper presents the design and implementation of a decentralized query-solving educational website, leveraging blockchain technology and a stack of complementary technologies including Solidity, ThirdWeb, React, ***, MetaMask, and Hardhat. The proposed platform aims to revolutionize the traditional online learning experience by addressing challenges related to data integrity, trustworthiness of content, and query-solving efficiency. This study delve into the technical aspects of implementing a blockchain-based query-solving educational website, including the design considerations, data structure, consensus mechanisms, and privacy enhancements. The research paper highlights the technical considerations and challenges encountered during the implementation process, including the design of smart contracts, integration of ThirdWeb technologies, development of the user interface, and integration with MetaMask for secure authentication and transaction handling. © 2023 IEEE.

关键词： Smart contract

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Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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TriMod Fusion for Multi-Modal Lung Tumor Diagnosis

TriMod Fusion for Multi-Modal Lung Tumor Diagnosis

引用

2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kumar, Vinod Lakhina, Upasana Bharti Singh, Prabhneet Sharma, Ankita Chandigarh University Dept. Of Computer Sc. And Engineering Uie Punjab Mohali India Panipat Institute Of Engg. And Technology Dept. Of Computer Sc. And Engineering Panipat Samalkha India

ISBN: (纸本)9798350306446

Lung cancer (LC) currently has the highest mortality rate of any medical diagnosis, underscoring the urgent need for improved detection techniques. This study highlights the critical role of automated image processing methods in improving the efficiency and accuracy of LC detection and identifies future directions for the field. The authors propose a unique method called TriMod Fusion to assist in uncovering the importance of correct tumor classification for effective patient care and management. This technique enables a more reliable and accurate classification and diagnosis of LC. A thorough examination of LIDC image data shows that the new method surpasses traditional single-modality approaches, improving the classification and diagnosis of lung tumors with higher accuracy, sensitivity, and specificity. The study presents a structural framework consisting of five sequential processes to automate the LC image recognition and classification process. Bilateral unsharp masking methods, including Gaussian, median, and adaptive, are used to improve image quality after initial image acquisition. The final stage of classification involves combining multiple neural classifiers with a hierarchical random walker and a Bayesian model. It also uses powerful segmentation algorithms and extraction methods. Findings from the third technique, which utilizes a deep recurrent neural network (DRNN) for categorization, reveal that the diagnostic precision for LC ranges from 87.5% to 97.4%, demonstrating a noteworthy enhancement in diagnostic efficiency. © 2024 IEEE.

关键词： Support vector machines

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Post Quantum Code-Based Cryptosystems with Dual Inverse Matrix 13

Post Quantum Code-Based Cryptosystems with Dual Inverse Matr...

引用

13th International Conference on Information technology in Asia, CITA 2023

作者： Makoui, Farshid Haidary Gulliver, T. Aaron Dakhilalian, Mohammad University of Victoria Dept. of Electrical & Computer Engineering VictoriaBC Canada Isfahan University of Technology Dept. of Electrical & Computer Engineering Isfahan Iran

ISBN: (纸本)9798350326208

The Shor algorithm demonstrates the significant risk that quantum attacks pose to the security of widely used cryptographic primitives. However, code-based cryptography has been shown to be resistant to these attacks. To date, no polynomial-time attack exists that can break code-based cryptosystems such as the McEliece cryptosystem. Despite this, these cryptosystems are not employed in practical applications in domains such as online banking, blockchains, and e-commerce platforms. The primary reason is the large sizes of the public and private keys associated with code-based cryptosystems. In this paper, a new code-based cryptosystem is introduced which employs a dual matrix based on the transpose and inverse of the parity check matrix to reduce the key size compared to the McEliece cryptosystem. © 2023 IEEE.

关键词： Public key cryptography

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Minimization of data centers in survivable dynamic SDM-EONs 14

Minimization of data centers in survivable dynamic SDM-EONs

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Sourabh Mondal, Khokan Calcutta Institute of Technology Dept. of Computer Science & Engineering Howrah India Techno India University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350335095

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms. © 2023 IEEE.

关键词： Energy efficiency

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SMOTE and Hyperparameter Optimization: A Dual Machine Learning Strategy for Enhancing Coupon Recommendation in Vehicular Contexts 3

SMOTE and Hyperparameter Optimization: A Dual Machine Learni...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350319125

This study centers on enhancing the accurateness of coupon recommendation algorithms within the framework of in-vehicle settings. The In-Vehicle Coupon Recommendation dataset from the UCI Repository was utilized in this study. A range of machine learning algorithms, such as Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), and CatBoost, were employed for analysis. Among the several algorithms considered, CatBoost exhibited notable performance, with a baseline accuracy of 74%. In order to improve the efficacy of the model, we utilized the 'Synthetic Minority Over-sampling Technique' (SMOTE) to mitigate the issue of class imbalance. This was subsequently followed by a thorough hyperparameter adjustment of the CatBoost algorithm. The improved model demonstrated a significant upgrade, with an accuracy rate of 77%. Additionally, there were noteworthy improvements in precision, recall, and F1 score. The AUC, which represents the Area Under the Receiver Operating Characteristic Curve, saw a notable improvement to 0.82. This signifies a noteworthy enhancement in the capacity to differentiate between positive and negative cases. The results emphasize the efficacy of CatBoost in the context of recommending coupons within vehicles, while also drawing attention to the influence of preprocessing methods and hyperparameter adjustment in enhancing the model's overall performance. This study is a valuable contribution to the domain of personalized advertising in the context of vehicles, providing valuable insights for the advancement of coupon recommendation systems that are more precise and dependable. © 2023 IEEE.

关键词： Coupon Recommendation Hyperparameter Tuning Machine Learning Recommender System SMOTE

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Deep Learning Driven Multilingual System for Recognition and Categorization of Medicinal Plants with Extensive Elucidation 9

Deep Learning Driven Multilingual System for Recognition and...

引用

9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Azhaguramyaa, V.R. Janet, J. Hariharan, A. Kamaleshwaran, A. Albert Joseph, A. Computer Science And Engineering Sri Krishna College of Engineering And Technology Tamil Nadu Coimbatore India Sri Krishna College of Engineering And Technology Dept. of Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

Medicinal plant use is widespread throughout many cultures and geographical areas, frequently entwined with customs and knowledge. The use of technology, especially deep learning, to improve the identification and classification of these plants has gained popularity in recent years. This research introduces a new multilingual system with deep learning capabilities that can identify and classify medicinal plants with a great deal of explanation. The term 'deep learning-driven multilingual system for recognition and categorization of medicinal plants with extensive elucidation' describes an advanced technology that carries out several tasks by utilizing deep learning, one of the most advanced machine learning techniques. Based on pictures, the system can recognize and classify a variety of therapeutic plants. Because users may communicate with the system, ask questions, and get information in their native tongue, it is usable by a wide variety of people across the globe. Apart from identification and classification, the method offers comprehensive clarifications or in-dept. accounts of the recognized therapeutic herbs. © 2024 IEEE.

关键词： Deep learning

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Quantitative Analysis and Forecasting in Finance: Leveraging Wavelet Packet Transform for Enhanced Market Insights

Quantitative Analysis and Forecasting in Finance: Leveraging...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Libina, M. Kavitha, C. Kabilan, A. Ifet College Of Engineering Dept. Of Information Technology Villupuram India Manakula Vinayagar Institute Of Technology Dept. Of Computer Science Puducherry India

ISBN: (纸本)9798350352689

Finance forecasting is always known for its ever-changing and complex nature in geopolitical markets, requiring advanced analytical tools to provide valuable deep insights for financial advisors, investors, and vital decision-makers. In this study, we delve into the realm of both quantitative analysis and forecasting in finance-related markets, utilizing sophisticated techniques to enhance our understanding of the markets. Our study focuses on the innovative utilization of the Optimized Wavelet Packet Transform (WPT), an advanced tool for processing signals, to uncover complex patterns within financial time series data through quantitative analysis using Plotly's interactive line charts. It bridges the gap between quantitative analysis and forecasting. It also reaches an accuracy rate of 99.5%, which is considered the best among the other prediction models. These insights help stakeholders and advisors navigate the ever-evolving landscape of financial markets and make decisions wisely. © 2024 IEEE.

关键词： Forecasting

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins 27

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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27th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2023

作者： Diamantopoulos, Georgios Tziritas, Nikos Bahsoon, Rami Theodoropoulos, Georgios School of Computer Science University of Birmingham Birmingham United Kingdom University of Thessaly Dept. of Informatics and Telecommunications Lamia Greece Southern University of Science and Technology Dept. of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350337846

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system. © 2023 IEEE.

关键词： Blockchain

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

引用

11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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