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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： G. Sujatha Manimuthu Ayyannan S. Gayathri Priya V. Arun A. N. Arularasan M Jogendra Kumar Department of Electronics and Communication Sri Venkateswara College of Engineering Tirupati Andhra Pradesh Senior Software Engineering Manager AI/ML and Data Expert San Francisco Bay Area California USA Department of Electronics and Communication R. M. D. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science &Engineering Madanapalle Institute of Technology Madanapalle Andhra Pradesh Department of Artificial Intelligence &Data Science Panimalar Engineering College Chennai Tamil Nadu Department of Computer Science Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh

To improve the detection of phishing sites, this proposal introduces feature weights for intelligent phishing site detection based on hybrid bio-inspired algorithms. The proposed approach uses Gray Wolf Optimization (GWO) and Firefly Algorithm (FF), which examines a wide range of website attributes, to more precisely identify phishing sites. Then employs Artificial Neural Network (ANN) to classify different website elements according to the importance of each component in differentiating between legitimate and phishing websites using bioinspired-based recommended site feature weights. According to experimental findings, the suggested hybrid bioinspired-based feature weighting greatly improved classification accuracy, true positive (TPR), and negative rates (TNR), as well as precision and F1 score. Phishing is an online crime that entails the gathering of private information like passwords, account numbers, and credit card numbers. Attackers use alluring URLs to entice phony website visitors. Recently, Artificial Intelligence-based phishing detection has seen some success, and in this study, ANN was used to detect phishing. This ANN classifier may make phishing websites simpler to spot. However, it has been shown that the effectiveness of detection can be increased by applying a genetic algorithm to improve feature selection.

关键词： Uniform resource locators Smart cities Phishing Biological system modeling Artificial neural networks Feature extraction Classification algorithms

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Statistical Analysis and Design of a New Chaotic Entropy Source for Cryptography and Application to Image Encryption

SSRN

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SSRN 2023年

作者： Nwachioma, Christian Ngwongwo, Ignatius P. Medaiyese, Olusiji O. Özkaynak, Fatih Department of Mechanical Engineering University of Colorado Denver DenverCO80204 United States Department of Mathematics Federal University of Technology Owerri Imo Owerri460114 Nigeria Department of Software Engineering Firat University Anatolia Elaziǧ23119 Turkey Department of Computer Science and Engineering University of Louisville KY40292 United States

This work proposes a new deterministic entropy source that is adequately pseudorandom for the design of substitution boxes (s-box) for image encryption applications. The source is a novel multistable chaotic system with a far-reaching ergodicity space that shows high sensitivity to both states and parametric deviations. The series of numbers generated from the source sufficiently pass the various tests for randomness and pseudorandomness as recommended by the National Institute of Standards and Technology. S-boxes are generated based on data from the system and are subjected to further cryptographic analyses, including nonlinearity, strict avalanche criterion, bit independence criterion, equiprobable xor-logic, and security. Specifically, the security analysis tests the robustness of the generated s-boxes on image encryption and decryption based on the cipher block chaining mode. Although each mode implements the so-called data encryption standard, using the proposed entropy source for the generation of s-boxes, initialization vectors, and permutation and resizing tables, the method holds the potential to heighten confusion and diffusion in the ciphertexts as indicated by various Pearson's correlations coefficients. Relevant algorithms are elaborated, and tables of data and graphs of simulations are presented to validate the results. © 2023, The Authors. All rights reserved.

关键词： Cryptography

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TSFEDL: A PYTHON LIBRARY FOR TIME SERIES SPATIO-TEMPORAL FEATURE EXTRACTION AND PREDICTION USING DEEP LEARNING (WITH APPENDICES ON DETAILED NETWORK ARCHITECTURES AND EXPERIMENTAL CASES OF STUDY)

arXiv

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arXiv 2022年

作者： Aguilera-Martos, Ignacio García-Vico, Ángel M. Luengo, Julián Damas, Sergio Melero, Francisco J. Valle-Alonso, José Javier Herrera, Francisco Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Software Engineering University of Granada Granada Spain Repsol Technology Lab Spain

The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others. In this paper, the TSFEDL library is introduced. It compiles 20 state-of-the-art methods for both time series feature extraction and prediction, employing convolutional and recurrent deep neural networks for its use in several data mining tasks. The library is built upon a set of Tensorflow+Keras and PyTorch modules under the AGPLv3 license. The performance validation of the architectures included in this proposal confirms the usefulness of this Python package. Copyright © 2022, The Authors. All rights reserved.

关键词： Python

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Research of Sampling Method For Cybercrime Black Industry Chain

Research of Sampling Method For Cybercrime Black Industry Ch...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Ying’An Cui Xue Li Yande Wu Department of Computer Science and Engineering Xi’an University of Technology Xi’an China School of International Business Shaanxi Normal University Xi’an China Software R&D Department 2 China Unicom Western Innovation Research Institute Co. Ltd Xi’an China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In modern organization theories, the social network established by cyber criminals is termed the crime cyber black industry chain. Under the current technical level, treating a crime cyber black industry chain as a whole dataset has to face severe challenges in many aspects, so the research of the sampling method specifically for it has been one of the current hot topics. Based on the structural feature of a cybercrime industry chain, the overall framework and the detailed algorithm of a multi-stage sampling method are proposed. Tested with real cybercrime datasets, our proposed multi-stage sampling method outperforms existing mainstream single-stage sampling methods at macro, meso, and micro scales, indicating it can solve practical problems with high efficiency and low cost.

关键词： Industries Costs Social networking (online) Organizations Sampling methods Information and communication technology Reliability

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An Intelligent Approach to Cost-effective Management of Typhoid Fever in Children

An Intelligent Approach to Cost-effective Management of Typh...

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International Conference on Signal Processing and Communication (ICSC)

作者： Kingsley T. Igulu Thipendra P Singh Tosin C. Olayinka Ojei H. Onyijen Edwin Onatuyeh Wilson Nwankwo Department of Computer Science Kenule Beeson Saro-Wiwa Polytechnic Bori Bori Nigeria Bennett University Greater Noida India Department of Software Engineering Wellspring University Benin City Edo State Nigeria Department of Physics Edo State University Uzairue Edo State Nigeria Department of Accounting Delta State University of Science & Technology Ozoro Nigeria Department of Cyber Security Delta State University of Science & Technology Ozoro Nigeria

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Typhoid fever poses a significant health concern among children, due to its potential for severe complications and high treatment costs. This paper proposes an intelligent approach to modelling the prognosis and management of paediatric typhoid infections leveraging machine learning and data analytics to optimize healthcare resources and improve patient outcomes. The dataset was sourced from hospitals in Edo State, Nigeria. The dataset comprises laboratory tests of blood samples of children, from age zero (0) to five (5) years. Six (6) attributes that exhibit the highest information gain: widal test, malaria count, monocyte, platelets, HB, eosinophils, cost of treatment respectively, were selected and ranked thus: 0.6759, 0.4936, 0.3147, 0.2843, 0.2416, 0.2127, and 0.20. Five different classification algorithms (J48_Consolidated, LMT, RepTree, MultiBoost Decision Stump) were employed. and Random Forest) and their results were compared. Performance analysis on the five (5) classifiers shows that Multiboost Decision Stump exhibited the best accuracy of 94% and least MAE of 0.0604. The finding shows that the developed model when integrated into automated disease diagnostic workflows will provide an enhanced and flexible solution for quick prognosis of typhoid fever in children in the tropics with significant cost savings with respect to hospitalization. The finding presupposes that we can enhance the efficiency of typhoid fever management while ensuring the delivery of quality care to children in resource-limited settings.

关键词： Pediatrics Machine learning algorithms Costs Signal processing algorithms Predictive models Prediction algorithms Classification algorithms Performance analysis Prognostics and health management Random forests

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COMPUTATION OF THE LEAST PRIMITIVE ROOT

arXiv

引用

arXiv 2022年

作者： McGown, Kevin J. Sorenson, Jonathan P. Department of Mathematics and Statistics California State University Chico United States Computer Science and Software Engineering Department Butler University IndianapolisIN46208 United States

Let g(p) denote the least primitive root modulo p, and h(p) the least primitive root modulo p2. We computed g(p) and h(p) for all primes p ≤ 1016. As a consequence we are able to prove that g(p) 5/8 for all primes p > 3 and that h(p) 2/3 for all primes p. We also present some additional *** Codes 11A07, 11Y16 © 2022, CC BY-NC-ND.

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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IEEE International Conference on Big Data

作者： Mst Shapna Akter Md Jobair Hossain Faruk Nafisa Anjum Mohammad Masum Hossain Shahriar Nazmus Sakib Akond Rahman Fan Wu Alfredo Cuzzocrea Department of Computer Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department of Applied Data Science San Jose State University USA Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA iDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480468

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research.

关键词： Machine learning algorithms Computational modeling Supply chains Qubit Artificial neural networks Machine learning Big Data

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Research on Multimodal Modal Fusion Application in Pulmonary Embolism Diagnoses 5

Research on Multimodal Modal Fusion Application in Pulmonary...

引用

5th International Conference on Signal Processing and Machine Learning, CONF SPML 2025

作者： Deng, Haonan Zhang, Xinyu Lu, Yiyi Ye, Zhongxu Institute of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Computing Science University of Glasgow GlasgowG11 6QT United Kingdom Duke Kunshan University Suzhou215316 China Department of Computer and Information Technology Purdue University West Lafayette47907 United States

ISBN: (纸本)9781837243235

Pulmonary embolism is a life-threatening illness caused by blockages in the pulmonary arteries, usually due to blood clots. This condition requires accurate diagnosis on time in order to prevent critical complications like cardiac arrest or chronic pulmonary hypertension. As a result, this study explores the capability of data fusion in a multimodal scenario to improve diagnostic accuracy in PE through the integration of Electronic Medical Records with imaging data from CT Pulmonary Angiography. Deep learning models such as PENet, ResNet3D_50, ResNext3D_101, and DenseNet were used for the analysis of imaging data. The EMR data were analyzed using logistic regression and other machine learning methods. A Late Fusion approach that combined the outputs from the EMR and imaging models, using averages, resulted in a diagnostic accuracy with an AUC of 0.94. This is a perfect example where combined clinical and imaging features addressed the shortcomings of one-modality approaches. Advanced fusion techniques, such as MISA, were also tried, but simpler strategies often performed better than them when the amount of data was limited. Overall, the results point out the promise of multimodal fusion in advancing PE diagnostics and also take note of the selection of an appropriate strategy for clinical application. © The Institution of engineering & Technology 2025.

关键词： Pulmonary diseases

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Correction to: Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

引用

Neural Processing Letters 2022年第1期55卷 33-34页

作者： Baskar, M. Renuka Devi, R. Ramkumar, J. Kalyanasundaram, P. Suchithra, M. Amutha, B. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram India Department of Software Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Kanchipuram India Department of Wireless Sensor Networks Institute of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

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Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka 12

Smart Agriculture Prediction System for Vegetables Grown in ...

引用

12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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