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arXiv 2024年

作者： Chakraborty, Tanujit Chattopadhyay, Swarup Das, Suchismita Naik, Shraddha M. Hens, Chittaranjan SAFIR Sorbonne University Abu Dhabi United Arab Emirates Sorbonne Center for Artificial Intelligence Sorbonne University Paris France Department of Computer Science & Engineering XIM University Bhubaneswar India Department of Data Science SP Jain School of Global Management Mumbai India Department of Computer Science Khalifa University United Arab Emirates Center for Computational Natural Science and Bioinformatics International Institute of Information Technology Hyderabad India

Complex biological networks, encompassing metabolic pathways, gene regulatory systems, and protein–protein interaction networks, often exhibit scale-free structures characterized by heavy-tailed degree distributions. However, empirical studies reveal significant deviations from ideal power-law behavior, underscoring the need for more flexible and accurate probabilistic models. In this work, we propose the Compounded Burr (CBurr) distribution, a novel four-parameter family derived by compounding the Burr distribution with a discrete mixing process. This model is specifically designed to capture both the body and tail behavior of real-world network degree distributions with applications to biological networks. We rigorously derive its statistical properties, including moments, hazard and risk functions, and tail behavior, and develop an efficient maximum likelihood estimation framework. The CBurr model demonstrates broad applicability to networks with complex connectivity patterns, particularly in biological, social, and technological domains. Extensive experiments on large-scale biological network datasets show that CBurr consistently outperforms classical power-law, log-normal, and other heavy-tailed models across the full degree spectrum. By providing a statistically grounded and interpretable framework, the CBurr model enhances our ability to characterize the structural heterogeneity of biological networks. © 2024, CC BY.

关键词： Maximum likelihood estimation

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Second-Order Statistics and Burst Error Analysis of UAV-to-UAV Rician Fading Channels

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IEEE Transactions on Vehicular technology 2025年

作者： Zeng, Linzhou Liao, Xuewen Zhu, Yunzhe Ma, Zhangfeng Yang, Jingya Peng, Xin Jiangl, Hao Chen, Zhen Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Xi'an Jiaotong University School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi'an710049 China Shaoyang University School of Information Science and Engineering Shaoyang422000 China Henan High-Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou451460 China Beijing Jiaotong University Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing100044 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing210044 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Macau Institute of Microelectronics 999078 China

An arbitrary-elevation two-cylinder geometry-based stochastic model is described for non-isotropic scattering UAV-to-UAV (U2U) Rician fading channels, which takes into account the line-of-sight (LoS), the single-bounced, and the double-bounced rays. The second-order channel statistics are derived from the proposed reference model, including the envelope level crossing rate (LCR) and the average fade duration (AFD). Notably, the key parameters in the LCR are reduced to closed-form, which are shown to be the generalizations of the previously reported ones from the two-ring model. The good agreement between the theoretical and the simulated LCRs/AFDs demonstrates not only the usefulness of the simulation models but also the correctness of the theoretical derivations. Based on those analytical expressions, the impacts of some unique model parameters are studied on the LCR, and the burst error rate of the U2U Rician fading channels is analyzed via the AFD. © 1967-2012 IEEE.

关键词： Stochastic models

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An Algorithm for Extracting Image Features Using a Random Deep Neural Network

An Algorithm for Extracting Image Features Using a Random De...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sonali Prasad Dharmadhikari Rabi. J Girija Rani Karetla Kapil Joshi K. Shivakumar Mohit Tiwari Institute of Management and Entrepreneurship Development Pune Department of Mechanical Engineering Mangalam College of Engineering Kottayam Kerala School of Computer Data and Mathematical Sciences We Stern Sydney University Australia Uttaranchal Institute of Technology Uttaranchal University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

In this paper, a random deep convolutional neural network-based technique for extracting image features is devised and put into practice. The image features are also richer following the image preprocessing procedure described in this article. Additionally, more consistent feature points are recognized, duplicate local features are deleted, and this prevents the feature matching from encountering too many unmatched pairs. Since the self-encoder can successfully rebuild the training data set, data that is close to the training set, and the data itself, this work offers some original ideas for constructing the model. The study's findings have demonstrated the successful outcomes.

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： G. Hariharan P. Prasanth P. Arthi Devarani T. Sajana Indhumathi C Ashok Kumar Deportment of Artificial Intelligence and Machine Learning Malla Reddy University Hyderabad Telangana India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R. M K College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Business Systems R.M. K. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science BanasthaliVidyapith Rajasthan India

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly.

关键词： Chemotherapy Image edge detection Lung cancer Medical services Clinical trials Knowledge discovery Concurrent engineering

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Neural Networks for Fault diagnosis in Electrical Machine

Neural Networks for Fault diagnosis in Electrical Machine

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Intelligent Technologies (ACOIT), Asian Conference on

作者： R. Vinston Raja J. Raja Sreenivasulu Gogula Atul Katiyar T. Thilagam V. S. Bhagavan Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) Vardhaman College of Engineering Hyderabad Telangana India Department of Electrical Engineering Mahatma Jyotiba Phule Rohilkhand University Bareilly Uttar Pradesh India Department of Science and Engineering S.A Engineering College Chennai Tamil Nadu India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Finding electrical equipment problems quickly is essential for minimising downtime and ensuring ongoing functioning. When it comes to early defect identification, traditional methods like human inspection or sensor-based surveillance might not cut it. Another alternative to employing machine learning algorithms to analyse complex patterns in sensor data is neural networks. With an emphasis on motor drives, this article lays forth a method for identifying electrical equipment issues using neural networks. The network has been trained to identify various issues, such as imbalanced rotors, broken stator windings, and failing bearings, by examining historical data. The trial findings prove that the suggested method may effectively find issues, make things more reliable, and cut down on maintenance expenses.

关键词： Fault diagnosis Motor drives Machine learning algorithms Surveillance Neural networks Stator windings Rotors Machine learning Maintenance Predictive maintenance

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Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation

arXiv

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arXiv 2024年

作者： Li, Yicong Sun, Xiangguo Chen, Hongxu Zhang, Sixiao Yang, Yu Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong999077 Hong Kong School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD4072 Australia Department of Systems Engineering and Engineering Management Nanyang Technological University Singapore999077 Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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Hybrid ACO and PSO to minimize makespan in job shop scheduling problems

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AIP Conference Proceedings 2024年第1期3222卷

作者： Priljen Habeahan Erna Budhiarti Nababan Mahyuddin K. M. Nasution Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The lowest time search in the dataset that E. Taillard utilized employs a heuristic approach based on tabu search techniques to get the predicted solution. Glover’s study gives a broad description of tabu search, which is commonly encountered in Taillard’s job shop scheduling difficulties and Widmer et al.’s flow shop sorting challenges. Although tabu search is relatively simple to use and typically yields excellent results, it takes a long time to complete. In this research a hybrid ACO and PSO was carried out to minimize makespan in the Job Shop Scheduling Problem which was used as sourced from benchmark data which is secondary data obtained from E. Taillard “Benchmarks for basic scheduling problems” which consists of job shop matrix data (job × machine) measuring 4 × 4, 5 × 5, 7 × 7, 10 × 10, 15 × 15, 20 × 20, 30 × 15, 30 × 20, 50 × 15 and 50 × 20. Hybrid is carried out by calculating the Pbest value, namely the process position of each job on the machine to get the best solution using the PSO algorithm. Next, calculate the Gbest (Global best) value for the position of each job on the best machine on the entire machine using the PSO algorithm and initialize the ACO parameters using the PBest and Gbest values. The results of research on datasets with sizes 10×10, 15×15, 20×20, 30×15, 30×20, 50×15 and 50×20 produce smaller makespan compared to the lower bound on the dataset with an average minimum makespan improvement value of 1.184.

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Automatic data Clustering Based Mean Best artificial Bee Colony Algorithm

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Computers, Materials & Continua 2021年第8期68卷 1575-1593页

作者： Ayat Alrosan Waleed Alomoush Mohammed Alswaitti Khalid Alissa Shahnorbanun Sahran Sharif Naser Makhadmeh Kamal Alieyan Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Computer Department Imam Abdulrahman bin Faisal UniversityDammamSaudi Arabia School of Electrical and Computer Engineering Department of Information and Communication TechnologyXiamen University MalaysiaSepang43900Malaysia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityDammamSaudi Arabia Center for Artificial Intelligence Technology(CAIT) Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiMalaysia School of Computer Sciences Universiti Sains Malaysia11800PenangMalaysia Faculty of Computer Sciences and Informatics Amman Arab UniversityAmmanJordan

Fuzzy C-means(FCM)is a clustering method that falls under unsupervised machine *** main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster *** Bee Colony(ABC)is a type of swarm algorithm that strives to improve the members’solution quality as an iterative process with the utilization of particular kinds of ***,ABC has some weaknesses,such as balancing exploration and *** improve the exploration process within the ABC algorithm,the mean artificial bee colony(MeanABC)by its modified search equation that depends on solutions of mean previous and global best is ***,to solve the main issues of FCM,Automatic clustering algorithm was proposed based on the mean artificial bee colony called(AC-MeanABC).It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value.A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of *** experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.

关键词： artificial bee colony automatic clustering natural images validity index number of clusters

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Incorporating Transfer Learning in Engine Health Assessment for Accurate Remaining Useful Life Prediction

Incorporating Transfer Learning in Engine Health Assessment ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： C. Karpagavalli Nelson A R. Raja Saranya E Manzoore Elahi M. Soudagar Christu Paul Ramaian Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering College Sivagangai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Aircraft engines are critical, therefore any move to make them safer, more reliable, and fuel-efficient is highly encouraged. To solve the challenges of flight safety and maintenance expense during aircraft engine operation, a prognostics and health management system is deployed. This system is primarily concerned with flaw detection, health evaluation, and life prediction. Making decisions about aircraft engine operation and maintenance is strongly reliant on estimating the remaining useful life (RUL). This study provides an ensemble transfer learning model for RUL prediction using the C-MAPSS turbofan engine dataset. Pre-processing procedures such as filtering and normalization are used to improve the dataset. The proposed ensemble model for RUL prediction is then trained on the processed data. Some of the well-known transfer learning models that are compared include Xception, ResNet-50, and VGG-16. The ensemble model outperforms the other models, with the highest R2 value of 0.9901. Furthermore, the ensemble model is very effective; it can predict RUL in just 37 seconds, which suggests that it could be useful for keeping an eye on engine health and making decisions about aircraft.

关键词： Training Accuracy Atmospheric modeling Computational modeling Transfer learning Predictive models Maintenance

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Rapid Detection technology of Sewing Thread Based on Deblurgan-Bsv3 Defuzzification Algorithm and St-Fpn Detection Algorithm

SSRN

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SSRN 2024年

作者： Zheng, Xiaohu Chen, Yufan Liu, Bing Xu, Xiuliang Institute of Artificial Intelligence Donghua University Shanghai201620 China Shanghai Industrial Big Data and Intelligent Systems Engineering Technology Center Shanghai201620 China College of Information Science and Technology Donghua University Shanghai201620 China Hangzhou Zhongfu Technology&Innovation Research Institute Co. Ltd Shanghai201620 China Precise Machinery Scientific & Technology Co. Ltd Shanghai201599 China

Stitch defect detection is an important means to ensure the quality of stitched fabrics. To realize the intellectualization of the sewing process, a rapid defect detection method based on machine vision was proposed. In order to solve the problem of motion blur caused by sewing speed, an image DeblurGAN-BSV3 algorithm is proposed. Firstly, different attention fusion mechanisms are introduced to achieve accurate extraction of image features. Secondly, the depth-separable convolution of the trunk network is replaced by blueprint convolution to ensure the processing speed of the detection algorithm and the clarity of the output image. In order to solve the difficulty of rapid detection of pin defects, a student-teacher feature pyramid matching method was proposed. The abnormal region interpretation technology was used to explicitly visualize the defect area of the identified abnormal instances, and then the abnormal detection score was performed on the detected images to make a quality judgment. © 2024, The Authors. All rights reserved.

关键词： Computer vision

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