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2023 Innovations in Intelligent systems and Applications Conference, ASYU 2023

作者： Temiz, Mustafa Yousef, Malik Bakir-Gungor, Burcu Abdullah Gul University Department of Electrical and Computer Engineering Kayseri Turkey Zefat Academic College Department of Information System Zefat Israel Abdullah Gul University Department of Computer Engineering Kayseri Turkey

ISBN: (纸本)9798350306590

Advances in next-generation sequencing and '-omics' technologies makes it possible to characterize the human gut microbiome. While some of these microorganisms are important regulators of our immune system, modulation of the microbiota leads to a variety of diseases. Colorectal cancer (CRC), the third most common cancer worldwide, is caused by genetic mutations, environmental conditions, and abnormalities in the gut microbiota. Using various machine learning methods and meta-analysis techniques, this study aims to build a classification model that can help in CRC diagnosis by analyzing metagenomic datasets of different populations obtained at the species level. Using 8 different countries and 9 different metagenomic datasets, 3 different meta-analyzes are performed: within-population, cross-population, and one population is selected for testing and the rest is used as a training dataset (LODO). For CRC classification, 4 different classification algorithms (Random Forest (RF), Logitboost, Adaboost, and Decision Tree (DT)) are used. The best performance among these methods was obtained with the Random Forest algorithm with an AUC of 0.98 by using JP for the training data set and JPN populations for the test data set in the cross-population performance evaluation. © 2023 IEEE.

关键词： Classification (of information)

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Comparison of Unsupervised Learning Algorithms for K-Means Clustering Efficiency in IoE Sensor Detection systems for Smart Agriculture 13

Comparison of Unsupervised Learning Algorithms for K-Means C...

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13th International Electrical engineering Congress, iEECON 2025

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University of Technology Krungthep Department of Electrical and Telecommunication Engineering Bangkok Thailand Thammasat University Faculty of Engineering Thammasat School of Engineering Department of Electrical and Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department of Robotic Engineering and Automation System Bangkok Thailand

ISBN: (纸本)9798331543952

This study investigates the application of clustering algorithms to data collected from color sensors providing frequency signals during the cultivation process. Continuous sampling allowed for real-time data collection, and variations in temperature and humidity were observed to influence signal reception. The dataset's large volume and varying sample sizes necessitated the evaluation of several clustering models for efficient data processing. The experiment tested K-Means, K-Means++, Mini-Batch K-Means, and Bisecting K-Means algorithms. The results reveal that the computation time for each model increased with the number of groups. Specifically, the K-Means, K-Means++, and Mini-Batch K-Means models exhibited shorter computation times, ranging from 0.02 to 0.03 seconds for two-group scenarios, while the Bisecting K-Means algorithm took significantly longer, approximately 0.10 seconds. Despite the variation in processing times, all models performed similarly in terms of clustering quality, with an average Silhouette Score of 0.82. This score indicates that the algorithms effectively separated data points into well-defined groups while maintaining high accuracy in clustering. Additionally, the analysis highlighted the importance of balancing computation time and clustering quality, particularly as the number of clusters increases. These findings suggest that while computational efficiency may vary across models, the clustering quality remains robust, providing valuable insights for data analysis in agricultural sensor networks. The study's results demonstrate that each clustering model is capable of handling large datasets with high accuracy in grouping, making them applicable to real-world agricultural data processing scenarios. In conclusion, the experiment confirms that all tested models, despite differing in computation time, successfully partitioned the data with high-quality results, offering promising applications for data analysis and optimization in agricult

关键词： K-means clustering

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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic systems

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IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Receding-Horizon Control of Constrained Switched systems with Neural Networks as Parametric Function Approximators

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SN computer Science 2023年第1期4卷 62页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students 6

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Yusa, Mochammad Coastera, Funny Farady Rahmadani, Deni Sari, Julia Purnama Kusrini, Kusrini Hartanto, Anggit Dwi University of Bengkulu Faculty of Engineering Department of Informatics Bengkulu Indonesia University of Bengkulu Faculty of Engineering Department of Information System Bengkulu Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Information System Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals;2) Filtering and selecting criteria for student academic activities;3) Transforming and normalizing the data;4) Applying automatic weighting using CRITIC;5) Calculating criteria rankings using the PROMETHEE II technique;and 6) Validating the ranking outcomes. © 2023 IEEE.

关键词： Decision support systems

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GCD-Sampling: A General Cross-scale Decoupled Sampling for Point Cloud 39

GCD-Sampling: A General Cross-scale Decoupled Sampling for P...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Wang, Yanzi Xiong, Jianyu Zha, Yaohua Xia, Shu-Tao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China

ISBN: (纸本)157735897X

Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Multiobjective Optimization for Traveling Salesman Problem: A Deep Reinforcement Learning Algorithm via Transfer Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 896-908页

作者： Gao, Le-Yang Wang, Rui Jia, Zhao-Hong Liu, Chuang Anhui University School of Computer Science and Technology Hefei230039 China National University of Defense Technology College of System Engineering Changsha410073 China Xiangjiang Laboratory Changsha410205 China Anhui University School of Internet Hefei230039 China

A wide range of real applications can be modelled as the multiobjective traveling salesman problem (MOTSP), one of typical combinatorial optimization problems. Meta-heuristics can be used to address MOTSP. However, due to involving iteratively searching large solution space, they often entail significant computation time. Recently, deep reinforcement learning (DRL) algorithms have been employed in generating approximate optimal solutions to the single objective traveling salesman problems, as well as MOTSPs. This study proposes a multiobjective optimization algorithm based on DRL, called multiobjective pointer network (MOPN), where the input structure of the pointer network is redesigned to be applied to MOTSP. Furthermore, a training strategy utilizing a representative model and transfer learning is introduced to enhance the performance of MOPN. The proposed MOPN is insensitive to problem scale, meaning that a trained MOPN can address MOTSPs with different scales. Compared to meta-heuristics, MOPN takes much less time on forward propagation to obtain the pareto front. To verify the performance of our model, extensive experiments are conducted on three different MOTSPs to compare the MOPN with two state-of-the-art DRL models and two multiobjective meta-heuristics. Experimental results demonstrate that the proposed MOPN obtains the best solution with the least training time among all the compared DRL methods. © 2024 IEEE.

关键词： Traveling salesman problem

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A Non-Sequential Steganography Method Using Perfect Square Quotient Differencing (NSPSQD)

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SN computer Science 2025年第5期6卷 1-26页

作者： Basak, Ratan Kumar Chatterjee, Ritam Dasgupta, Kousik Dutta, Paramartha Computer Science and Engineering Institute of Engineering and Management (IEM) University of Engineering and Management Kolkata Kolkata India Computer Science and Engineering Kalyani Government Engineering College Kalyani India Computer and System Science Visva-Bharati University Bolpur India

In today’s digital world, keeping data safe is a top priority. Two common methods used to protect data are steganography and cryptography. Steganography hides secret data within everyday files (like images, GIFs or videos), while cryptography scrambles the data into an unreadable format. This paper introduces a new way to hide data using a technique called Perfect Square Quotient Differencing. Instead of embedding data in a straight sequence, the method hides information in two steps within the components of an image pixel (called the quotient and remainder). In the first step, a perfect square quantization technique is applied to the quotient part. In the second step, the Two Least Significant Bit (2LSB) method is used on the remainder part. A new range-table is also introduced to help determine how much data can be hidden in the first step. This two-step approach allows a large amount of data to be hidden (about 3 bits per pixel on average). The method was tested on many animated color images, and its performance was measured using tools like Peak-Signal-to-Noise-Ratio (PSNR), Mean Square Error (MSE), Universal Image Quality Index (UIQI), and Payload Curve. The results show that this method works better than several modern steganography techniques. Additionally, tests were conducted to ensure the method is secure against potential attacks. This new algorithm could be particularly useful for protecting digital documents stored in cloud-based platforms, offering a robust and efficient way to keep data safe.

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Enhanced Object Detection and Classification via Multi-Method Fusion

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computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Effect of individual atmospheric parameters on beam spreading of higher-order Gaussian beam

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Optical and Quantum Electronics 2024年第12期56卷 1-18页

作者： kumar, Mukesh Khandelwal, Arpit Azeemuddin, Syed Center for VLSI and Embedded System Technologies International Institute of Information Technology Hyderabad India Department of Electrical Engineering Indian Institute of Technology Jodhpur India Department of Electrical and Computer Engineering Behrend College Penn State University EriePA United States

The beam spreading is essential for evaluating the higher-order Gaussian beam when it propagates through atmospheric turbulence. In this paper, we investigate the impact of individual atmospheric parameters such as jitter, turbulence, wind speed, and thermal blooming on beam spreading of Hermite Gaussian (HG), and Laguerre Gaussian (LG) beams. We also examine spot size variations due to beam quality on various HG and LG modes. It is seen that the impact of spreading caused by beam quality and turbulence is higher than that of jitter, wind speed, and thermal blooming. As the mode order increases, the effect of turbulence strength increases, leading to more spread for HG and LG beams. Spreading due to diffraction and beam quality on higher mode LG beam is higher than the HG beam in different modes. Beam spreading due to thermal blooming for the lower mode is higher compared to the higher mode for LG and HG beams. The sensitivity of higher mode LG beams to turbulence distortions is greater than HG beams as they experience more significant fractional increases in their spot size due to turbulence. The combined beam spread for the Laguerre Gaussian beam is larger than the Hermite Gaussian beam. This paper aims to understand better individual beam spreading in the atmosphere and its impact on the overall performance of higher-order laser propagation to develop optimized laser systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Gaussian beams

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