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arXiv 2024年

作者： Chee, Yeow Meng Dau, Son Hoang Etzion, Tuvi Kiah, Han Mao Zhang, Wenqin Dept. of Industrial Systems Engineering and Management National University of Singapore Singapore School of Computing Technologies RMIT University MelbourneVIC3000 Australia Computer Science Faculty TechnionIsrael Institute of Technology Haifa3200003 Israel School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Electronic Information and Engineering Shanghai Jiao tong University China

Motivated by a repair problem for fractional repetition codes in distributed storage, each block of any Steiner quadruple system (SQS) of order v is partitioned into two pairs. Each pair in such a partition is called a nested design pair and its multiplicity is the number of times it is a pair in this partition. Such a partition of each block is considered as a new block design called a nested Steiner quadruple system. Several related questions on this type of design are considered in this paper: What is the maximum multiplicity of the nested design pair with minimum multiplicity? What is the minimum multiplicity of the nested design pair with maximum multiplicity? Are there nested quadruple systems in which all the nested design pairs have the same multiplicity? Of special interest are nested quadruple systems in which all the (v2) pairs are nested design pairs with the same multiplicity. Several constructions of nested quadruple systems are considered and in particular classic constructions of SQS are examined. © 2024, CC BY.

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An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

An Artificial Neural Network Model to Forecast Grain Crops i...

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Communications, information, Electronic and Energy systems (CIEES), International Conference on

作者： Aigul Mimenbayeva Maylen Omirtay Rozamgul Niyazova Gulmira Bekmagambetova Raya Suleimenova Ainur Tursumbayeva Dept. of Computational and Data Sciences Astana IT University Astana Kazakstan Specialty Smart Technologies Astana IT University Astana Kazakstan Dept. of Artificial Intelligence Technologies L. Gumilyov Eurasian National University Astana Kazakhstan Dept. of Computer Science S. Seifullin Kazakh Agrotechnical Research University Astana Kazakstan School of Engineering and Information Technology Eurasian Technological University Almaty Kazakstan Dept. of Information Systems S. Seifullin Kazakh Agrotechnical Research University Astana Kazakstan

The paper analyzes and predicts the yield of grain crops of LLP “North-Kazakhstan Agricultural Experimental Station” for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development.

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SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection

arXiv

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arXiv 2024年

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Co. Limerick CastletroyV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems Tuam GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © 2024, CC BY.

关键词： Automobile simulators

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Enhancing Predictive Model Performance through Comprehensive Pre-processing and Hybrid Feature Selection: A Study using SVM

Enhancing Predictive Model Performance through Comprehensive...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International Conference on

作者： Bharadwaj Thuraka Vikram Pasupuleti Chandra Shikhi Kodete U. Ganesh Naidu N S Koti Mani Kumar Tirumanadham Vahiduddin Shariff School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA School of Technology Eastern Illinois University Charleston IL USA Department of CSBS B V Raju Institute of Technology Narsapur Medak Telangana India Dept. of CSE Sir C R Reddy College of Engineering Eluru India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This study aims to achieve the improved performance of heart disease prediction based on the increased level of preprocessing and rather effective feature selection approaches. Starting with data preparation in which the data is first cleaned up the work develops a class balance solution using Synthetic Minority Over-sampling Technique (SMOTE) while the Z-score function identifies outliers. As it will be described in CHAN section that applies Chi-square and ANOVA under a hybrid model, the useful characteristics for model training are identified and prioritized. Using a method derived from the medical records database, the study focuses on the heart health indices using Support Vector Machines (SVM) which is a modelling technique widely known for its effectiveness in handling increased numbers of variables and the relationship between the measurements. Measures such as accuracy, precision, recall and F1-score among others are used in the evaluation of the success of the approach. In particular, the results shed the light to Health Care applications where risk assessment and correct diagnosis is extremely critical; the findings also assert the important roles of strong preprocessing and selective feature selection strategies in enhancing the predictive model’s performance. This work contributes to the development of approaches intended for stable and efficient modelling in medical decision support system.

关键词： Support vector machines Training Heart Decision support systems Medical services Predictive models Feature extraction Particle measurements Medical diagnostic imaging Diseases

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A Survey on Explainable Artificial Intelligence for Cybersecurity

arXiv

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

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Method for filtering encrypted traffic using a neural network between an Industrial Internet of things system and Digital Twin 5

Method for filtering encrypted traffic using a neural networ...

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5th International Conference on Future Networks and Distributed systems: The Premier Conference on Smart Next Generation Networking Technologies, ICFNDS 2021

作者： Luksha, Ivan Duy Dinh, Truong Karelin, Evgenii Glushakov, Ruslan Kirichek, Ruslan Dept. of Software Engineering and Computer Science Bonch-Bruevich Saint Petersburg State University of Telecommunications Saint-Petersburg Russia Faculty Information Technology 1 Posts and Telecommunications Institute of Technology Hanoi Viet Nam Saint-Petersburg State Pediatric Medical University Saint-Petersburg Russia

ISBN: (纸本)9781450387347

Several traffic optimization approaches are available nowadays, including compression, packet deduplication, specialized hardware and software solutions, application optimization, and neural network utilization. However, the inclination to approach data to consumers and transfer data to the periphery necessitates the employment of novel (perhaps combining the use of known) ways to handle the problem, while keeping in mind that most traffic is encrypted, and the proposed solution should be "transparent"to end-users. The article presents a technique for isolating encrypted IoT traffic, and a study of its effectiveness using a neural network in encrypted SSL sessions has been carried out. © 2021 ACM.

关键词： Machine learning

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Classification System Based on Data Mining Technique for Flat-Colored Textiles

SSRN

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SSRN 2025年

作者： Zhou, Min Lin, Yukai Hu, Wei Dai, Chai Tang, Guangfa He, Fei School of Information and Intelligent Engineering Guangzhou xinhua University Dongguan China School of Intelligent Control Hunan Railway Professional Technology College Zhuzhou China College of Electrical and Electrical Engineering Hunan University Changsha China Dept. of Pingdingshan Branch of China Mobile Communication Group Henan co. LTD Henan Pingdingshan China Dept.of Physics Electrical and Intelligent Manufacturing Huaihua University Changsha China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Manual visual inspection for defect detection in manufacturing can lead to high false positive rates. This paper compares data mining techniques for the binary classification of defects in flat products in a relatively open scenario. The background provides context on research into data mining-based detection. The methodology is divided into five sections: data collection, selection, preprocessing and transformation, classification, and model evaluation. Each section details the techniques applied, tailored to the application context, with specifying examples. Experiments apply real-world data sets self-collected from production lines. Statistics like prediction accuracy are generated by established data mining evaluation algorithms. Results indicate the efficacy of data mining for automated defect detection over manual approaches. Finally, conclusions summarize findings and discuss the limitations of the current research and planned future work to address the drawbacks. Overall, the formalized methodology and supportive experimental results demonstrate both the viability and potential areas of improvement for data mining in manufacturing quality control. © 2025, The Authors. All rights reserved.

关键词： Data accuracy

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Certain Investigation on IoT Based Cotton Dust Pollution Monitoring in Textile Plants

Certain Investigation on IoT Based Cotton Dust Pollution Mon...

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International Conference on Inventive Computation Technologies (ICICT)

作者： P Sivanesan Anand J V Trilok Sai Vaddanam Shaik Mohammad Irfan Department of Information Communication Technology School of Computing SASTRA University Thanjavur Tamilnadu India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamil Nadu India Dept. of Computer science and Business Systems SASTRA University Thanjavur Tamilnadu India Dept.of ECE Siddhartha Academy of Higher Education Vijayawada Andhra Pradesh India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The objective of the work is to track the level of cotton dust in the textile sector. The IoT-enabled cotton dust monitoring system deploys three sensors: Sharp GP2Y1010AU0F; Shinyei PPD42NS; and Honeywell HPMA115S0-XXX measuring the dust levels. The Optical dust sensor GP2Y1010AU0F has a threshold of 1 mg/mS, which is placed inside the mask and when that threshold is crossed, buzzer alerts the worker with the air contaminants. It measures inhaled cotton dust and sends data to IoT, real-time concentration height of dust into the mobile devices. Experimental results show that Sharp GP2Y1010AU0F sensor has achieved award for acquiring the highest mean dust concentration of 95.167 mg/mS with a standard deviation of 3.014 among the three sensors, while Honeywell HPMA115S0-XXX and Shinyei PPD42NS recorded a mean of 87.833 mg/mS and 77.500 mg/mS, respectively. The observed response times of the sensors as follows for GP2Y1010AU0F-8s, HPMA115S0-XXX-6s, and PPD42NS- 5s, recording the fastest response by Honeywell HPMA115S0-XXX. A comparative study in accuracy, precision and sensitivity, with a response time, shows the merits and demerits for each sensor. Results emphasize the effectiveness of the system proposed for monitoring in terms of airborne cotton dust exposure in improving safety in textile workplaces. A meta-analysis is performed with Bayesian framework using JASP software.

关键词： Sensor phenomena and characterization software Real-time systems Sensors Safety Bayes methods Pollution measurement Cotton Time factors Monitoring

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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Post Quantum Cryptography: Techniques, Challenges, Standardization, and Directions for Future Research

arXiv

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arXiv 2022年

作者： Bavdekar, Ritik Chopde, Eashan Jayant Bhatia, Ashutosh Tiwari, Kamlesh Daniel, Sandeep Joshua Atul Dept. of Computer Science and Information Systems Birla Insitute of Technology and Science Pilani Pilani Campus Jhunjhunu Rajasthan 333031 India Department of Electrical and Electronics Engineering Birla Insitute of Technology and Science Pilani Pilani Campus Jhunjhunu Rajasthan 333031 India

The development of large quantum computers will have dire consequences for cryptography. Most of the symmetric and asymmetric cryptographic algorithms are vulnerable to quantum algorithms. Grover’s search algorithm gives a square root time boost for the searching of the key in symmetric schemes like AES and 3DES. The security of asymmetric algorithms like RSA, Diffie Hellman, and ECC is based on the mathematical hardness of prime factorization and discrete logarithm. The best classical algorithms available take exponential time. Shor’s factoring algorithm can solve the problems in polynomial time. Major breakthroughs in quantum computing will render all the present-day widely used asymmetric cryptosystems insecure. This paper analyzes the vulnerability of the classical cryptosystems in the context of quantum computers, discusses various post-quantum cryptosystem families, discusses the status of the NIST post-quantum cryptography standardization process, and finally provides a couple of future research directions in this field. © 2022, CC BY.

关键词： Quantum computers

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