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A retailer’s inventory model for deteriorating items under power pattern demand with shortages partially backlogged in both crisp and fuzzy environments

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International Journal of Industrial and Systems engineering 2025年第1期49卷 96-118页

作者： Patra, Sourav Kumar Paikray, Susanta Kumar Tripathy, Rudra Mohan Department of Mathematics Veer Surendra Sai University of Technology Odisha Burla768018 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar752050 India

An inventory predicament can be resolved with numerous techniques, starting from the trial-and-error manner of mathematical and simulation methods. Mathematical methods always serve as powerful tools for minimising total inventory costs. In this paper, we have considered a retailer’s inventory problem in order to determine an optimal strategy that minimises the total inventory cost under various constraints. Here, the constraints include constant deterioration, power-pattern demand, permissible shortages, partial backlog, different inventory costs, and inherent imprecision of various expenses concerning the current scenario. Subsequently, we develop the mathematical model of the problem together with its solving policy in a crisp as well as fuzzy environments. Moreover, we provide several numerical illustrations to validate our findings. Finally, we present several managerial insights for inventory managers based on the sensitivity analysis of associated parameters. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Sensitivity analysis

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An Advanced Clustering with Toeplitz and Circulant Matrices and a Comparison Against Spectral Clustering, and K-Means 3

An Advanced Clustering with Toeplitz and Circulant Matrices ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Saragadam, Sneha Rohan, Gamidi Manideep, Thotapalli Sri Surya Jayan, Sarada Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331518592

In an effort to increase computing efficiency, this work presents an improved spectral clustering technique that approximates the affinity matrix using Toeplitz and Circulant matrices. To preserve the quality of the clustering, we take advantage of the spectral characteristics of the similarity graph's Laplacian matrix. The Iris flower, Wine Quality, and Mushroom datasets are used to assess the approach. The following six clustering methods are contrasted: PCA combined with either spectral or K-means clustering, K-means clustering (with and without matrix approximation), and spectral clustering (with and without matrix approximation). The Silhouette Score, Fl score, precision, recall, and Adj usted Rand Index (ARI) are used to evaluate performance. The suggested method produces similar results to traditional methods with a significant reduction in computational cost, according to the results. In order to fully investigate the advantages of these matrices, architectures in spectral clustering are explored. © 2024 IEEE.

关键词： K-means clustering

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Reinforcement Learning Approach for Improving Platform Performance in Two-Sided Mobility Markets 27

Reinforcement Learning Approach for Improving Platform Perfo...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ghasemi, Farnoud Tabatabaei, Seyed Hassan Ghanadbashi, Saeedeh Kucharski, Rafal Golpayegani, Fatemeh Jagiellonian University Faculty of Mathematics and Computer Science Poland School of Computer Engineering Iran University of Science and Technology Tehran Iran School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9798331505929

Two-sided mobility markets, with platforms like Uber and Lyft, are complex systems by nature due to intricate, non-linear interactions between the platform and the involved parties including travelers and drivers. These interactions give rise to phenomena underlying market evolution, mainly cross-side network effects. Currently, such platforms rely on rule-based (RB) strategies with a constant commission rate to grow and achieve sustainability in terms of market share and profitability. However, the constant commission rate significantly constrains the platform's ability to leverage network effects, leading to inefficient growth. In this study, a Reinforcement Learning-based (RLB) strategy is proposed to improve the platform performance through strategic levers. We employ a Deep Q-Network (DQN) within an agent-based framework, enabling the platform to adjust the commission rate on a day-to-day basis while learning the complex, non-linear interactions in the market. The results show that the RL-based strategies successfully generate and control the essential cross-side network effects in the market enhancing the platform performance via dynamic commission rate. Our results indicate 12% improvement in the platform revenue with the RL-based strategy in comparison to the rule-based strategy without significantly compromising the platform market share which can essentially impact the platform's viability in the long term. © 2024 IEEE.

关键词： Deep reinforcement learning

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Voice assistance to blind people by detection of surrounding objects, activities, and humans

Voice assistance to blind people by detection of surrounding...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Kambham, Suhas Preetham Varman, Raahul Ramya, Tadepalli Sri Valli Eniyan, M.K. Tangavel, Senthil Kaumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Mathematics and Computer Science Ashland University United States

ISBN: (纸本)9798331510299

Various systems were developed to assist those with vision impairments and enhance their standard of life. Regrettably, the majority of these systems have restricted functionality. We decided to build a model that detects all possible surrounding things and guides them through voice description. Our main objective is to detect the obstacles in the person's pathway and warn them using voice description [1]. The model can assist blind people from the surrounding obstacles which exist in their pathway. The primary goal of the model is to identify the nearby obstacles - vehicles, animals, persons, objects, etc, in the person's pathway and guide or warn them using voice description. Our proposed system integrates advanced real-time obstacle detection and voice-guided navigation using a novel combination of DepthNet module with convolutional LSTM layers, enhancing spatial context awareness compared to traditional models. This approach, along with fine-tuning on diverse datasets such as MS COCO and BDD100K, ensures robust performance in varied environments and conditions. The real-time guidance capability is further improved by the integration of optical flow algorithms, which accurately estimate motion in dynamic scenes. This comprehensive solution significantly enhances the assistive technology for those with visual impairments. © 2024 IEEE.

关键词： Obstacle detectors

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Improved Bounds on the Size of Permutation Codes Under Kendall τ-Metric

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IEEE Transactions on Information Theory 2025年第6期71卷 4156-4166页

作者： Parvaresh, Farzad Sobhani, Reza Abdollahi, Alireza Bagherian, Javad Jafari, Fatemeh Khatami, Maryam University of Isfahan Faculty of Engineering Department of Electrical Engineering Isfahan81746-73441 Iran School of Mathematics Tehran19395-5746 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Applied Mathematics and Computer Science Isfahan81746-73441 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Pure Mathematics Isfahan81746-73441 Iran

In order to overcome the challenges caused by flash memories and also to protect against errors related to reading information stored in DNA molecules in the shotgun sequencing method, the rank modulation method has been proposed. In the rank modulation framework, codewords are permutations. In this paper, we study the largest size P(n, d) of permutation codes of length n, i.e., subsets of the set Sn of all permutations on {1, . . . , n} with the minimum distance at least d ∈ {1, . . . ,(Formula presented)} under the Kendall τ-metric. By presenting an algorithm and two theorems, we improve the known lower and upper bounds for P(n,d) . In particular, we show that P(n,d)=4 for all n ≥ 6 and (Formula presented) (Formula presented) © 1963-2012 IEEE.

关键词： Flash memory

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Generalized Extended Welch Bound for Multi-Cell Non-Orthogonal Sequence Sets

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IEEE Communications Letters 2025年第6期29卷 1176-1180页

作者： Shen, Bingsheng Liu, Kaiqiang Gu, Zhi Yang, Yang Zhou, Zhengchun Southwest Jiaotong University School of Information Science and Technology Chengdu China Southwest Jiaotong University School of Mathematics Chengdu China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Sequence sets with minimum total squared correlation (TSC) have important application in communications. In a multi-cell system, due to the difference in channel strength between the home cell and the neighboring cells, the TSC can be expressed as the sum of squares of weighted correlations among sequences. In this letter, we obtain a closed-form expression for the lower bound of weighted TSC under multi-cell systems which can be regarded as a generalization of the extended Welch bound, namely generalized extended welch bound (GEWB). Moreover, we find a class of sequence sets whose parameters can achieve the theoretical lower bound of the weighted TSC, indicating that the derived GEWB is tight. © 1997-2012 IEEE.

关键词： Plant cell culture

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Analysis of environmental impact factors in urban road traffic accidents: logistic regression model

Advances in Transportation Studies

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Advances in Transportation Studies 2024年第Special issue期3卷 39-50页

作者： Zhao, X.J. Li, S.Y. Guan, H.B. College of Mathematics and Information Science Anyang institute of Technology Anyang455000 China School of Computer Science and Information Engineering Anyang Institute of Technology Anyang455000 China College of Mathematics and Information Science Zhengzhou University of Light Industry Zhengzhou450002 China

The traditional methods for analyzing environmental impact factors in urban road traffic accidents suffer from issues such as low recall, poor precision, and time-consuming processes. To address these challenges, a novel analysis method based on the logistic regression model is proposed. This method utilizes the MIFP-Apriori algorithm to collect urban road traffic accident data and incorporates the EM algorithm for data filling and processing. Principal component analysis is then employed to screen the environmental impact factors in urban road traffic accidents. These selected factors are input into the logistic regression model to conduct a comprehensive analysis. The results reveal that environmental impact factors like road conditions and meteorological factors have a significant influence on traffic accidents. Experimental results show that the proposed method achieves an average recall rate of 96.98%, an average precision rate of 97.66%, and the analysis time for environmental factors ranges from 0.12s to 0.52s. © 2024, Aracne Editrice. All rights reserved.

关键词： Environmental impact factors Logistic regression model Mifp-apriori algorithm Principal component analysis method Traffic accident Urban road

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Accurate threat hunting in industrial internet of things edge devices

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Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

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Physical Layer Security for CR-NOMA Network with Cooperative Jamming

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Tsinghua science and Technology 2025年第2期30卷 708-720页

作者： Meiling Li Peng Xue Hu Yuan Yuxing Han School of Electronics Information Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Mathematics Kingston UniversityLondonKT12EEUK Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China

Cooperative jamming can effectively combat eavesdropping in physical layer security communication without affecting the legal receiver and improve the security performance of the *** paper introduces cooperative jamming to cognitive radio(CR)networks with non-orthogonal multiple access(NOMA)*** secure performance of the considered CR and NOMA(CR-NOMA)network is evaluated using two modes:non-cooperative jamming and cooperative *** particular,the secrecy outage probabilities(SOPs)of the primary user(PU)in the two modes are derived under Rician fading channels,based on which,the influences of the transmission signal-to-noise ratio(SNR)of secondary users(SUs),the number of SUs,the secrecy rate,and the power allocation coefficient on the SOPs of PU are analyzed *** analysis and simulation results show that cooperative jamming effectively prevents eavesdropping behaviour,which reduces the SOP of PU compared to non-cooperative *** also show that the transmission SNR,the number of SUs,the secrecy rate,and the power distribution coefficients greatly influence performance improvement.

关键词： cognitive radio non-orthogonal multiple access physical layer security cooperative jamming

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Analyzing Artificial Intelligence Based Intrusion Detection System in Detecting DDoS Attack 4th

Analyzing Artificial Intelligence Based Intrusion Detection ...

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kurra, Sai Suchith Rayana, Likhitha Yoosuf, Mohamed Sirajudeen School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

ISBN: (纸本)9789819751990

With the increasing complexity of the cyber-attacks, traditional intrusion systems have become inadequate in effectively detecting and preventing network attacks. Modern machine learning techniques have the capability of preventing sophisticated and aggressive cyber-attacks and have emerged as a promising approach to enhance the accuracy and efficiency of the intrusion detection. This research paper provides a comprehensive analysis and evaluation of the integration of machine learning and deep learning algorithms into intrusion detection system with respect to the Distributed Denial of Service (DDoS) attack. The study begins by presenting an overview of intrusion detection systems, highlighting their importance and significance in safeguarding network infrastructure. Subsequently, it explores supervised machine learning algorithms and deep learning models, such as Random Forest tree, Support Vector Machines (SVM), ResNet and TabNet. The paper presents the pros and cons of existing IDS models with respect to real-time processing, scalability, false positive and false negative results, and IDS vulnerabilities. The above-mentioned algorithms have been trained and evaluated with CICIDS-2017 dataset. The performance metrics used to evaluate the IDS models are accuracy, precision, F1 score, training time of each model, and adaptability to evolving attack patterns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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