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International Conference on Nonlinear applied Analysis and Optimization, ICNAAO-2021

作者： Ganguly, Sanchari Das, Pritha Maiti, Manoranjan Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur WB Howrah711103 India Department of Applied Mathematics Oceanology and Computer Programming Vidyasagar University WB Midnapore721102 India

ISBN: (纸本)9789819905966

Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manufacturing to maintain sustainable development. The adoption of online shopping by customers around the globe has altered the dynamics of competitiveness in the retail supply chain (SC). Considering the above facts, the effect of fairness concern on optimal prices and channel members’ profits in a two-level dual channel SC is investigated. Here, we consider an SC comprising a green manufacturer and a fair-minded retailer where the manufacturer produces and sells the green product through both offline and online channels to consumers. Due to uncertainty in the real world, the fuzziness is associated with market demand, price elasticity, and the coefficient of greenness. The fuzzy objectives and constraints are reduced to crisp ones using expectation and possibility measures, respectively. Both centralized and decentralized models (with and without cognitive bias) are formulated and solved by the Stackelberg game for the optimal prices and product greenness level. The models’ optimal solutions are analyzed and compared with the deterministic models numerically. The effects of the fairness concern coefficient on the optimal prices, product greenness level, and channel members’ profits are investigated. The sensitivity analyses are presented to study the effects of the fuzzy degree of customers’ sensitivity toward greenness on channel members’ profits. Retailer’s fairness is harmful to the manufacturer but beneficial for her own profit. Finally, some conclusions and managerial insights are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification

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Expert Systems 2025年第7期42卷

作者： Wang, D.Y.C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

Customer churn, particularly in the telecommunications sector, influences both costs and profits. As the explainability of models becomes increasingly important, this study emphasises not only the explainability of customer churn through machine learning models, but also the importance of identifying multivariate patterns and setting soft bounds for intuitive interpretation. The main objective is to use a machine learning model and fuzzy-set theory with top-k HUIM to identify highly associated patterns of customer churn with intuitive identification, referred to as Highly Associated Fuzzy Churn Patterns (HAFCP). Moreover, this method aids in uncovering association rules among multiple features across low, medium, and high distributions. Such discoveries are instrumental in enhancing the explainability of findings. Experiments show that when the top-5 HAFCPs are included in five datasets, a mixture of performance results is observed, with some showing notable improvements. It becomes clear that high importance features enhance explanatory power through their distribution and patterns associated with other features. As a result, the study introduces an innovative approach that improves the explainability and effectiveness of customer churn prediction models. © 2025 John Wiley & Sons Ltd.

关键词： Fuzzy rules

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Enhanced Support Vectors based Oversampling Method 25

Enhanced Support Vectors based Oversampling Method

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Sabbah, Thabit Ashraf, Mahmoud Abrar, Mohammad Dhofar University Collage of Arts and Applied Sciences P.O.Box:2509 Salalah211 Oman University of Jeddah College of Computer Science and Engineering Department of Computer Science and Artificial Intelligence Jeddah Saudi Arabia Arab Open University Faculty of Computer Studies Muscat122 Oman

ISBN: (纸本)9798331540012

Class distribution imbalance in the dataset is a critical factor in machine learning based classification performance. Many oversampling techniques were used to overcome the effect of this problem and retrieve the balance state for effective modelling. This paper presents an enhanced technique based on the Support Vectors for oversampling the minority class by generating synthetic data for better performance. The proposed method is tested on machine-generated datasets for binary classification problems. Experiments show an average improvement of approximately 15% in the classification recall and AUC performance measures on both datasets using the proposed method over the leading of the studied oversampling techniques. The proposed method can benefit the real-life machine learning classification problems where the positive class forms the minority of the training dataset like fraud-detection, medical diagnosis, and other problems, by expanding the positive class samples with more discriminative and evenly distributed related synthetic samples. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Sequential and Parallel Computing for Object Detection Using Deep Learning Model 24

Comparative Analysis of Sequential and Parallel Computing fo...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Amsaad, Fathi Al-Eidi, Shorouq Darwish, Omar Computer Science and Engineering Wright State University Ohio United States Tafila Technical University Computer Science Department Tafila Jordan Information Security and Applied Computing Eastern Michigan University Michigan United States

ISBN: (纸本)9798350384307

Recently, parallel and distributed computing has become indispensable in various application domains, such as cloud computing and extensive data analysis, to meet the growing demands for efficiency and reliability. In this study, we compared sequential and parallel computing techniques applied to a deep learning model for object detection. By leveraging multiple evaluation metrics, such as F1 score, execution time, and accuracy, we meticulously evaluated the model's performance. Our analysis revealed that parallel execution considerably decreased the training time of model, resulting in an average time savings of 40 % while maintaining the training process's accuracy and quality. These finding have significant implications for real world applications, as they demonstrate tremendously the potential for expedited deep learning model training without compromising accuracy. This can tremendously benefit medical imaging and realtime video analysis, where accuracy and efficiency are paramount. © 2023 IEEE.

关键词： Deep neural networks Parallel computing Sequential computing

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Assessing WLAN 802.11ac vs. 802.11n Using OPNET: An Evaluation of Comparative Performance

Assessing WLAN 802.11ac vs. 802.11n Using OPNET: An Evaluati...

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Future of Information and Communication Conference, FICC 2025

作者： Ali, Ali Mohd Al-Qerem, Ahmad Owida, Hamza Abu Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13132 Jordan Medical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan

ISBN: (纸本)9783031853623

Wireless LAN 802.11 has gained significant popularity due to its cost-effectiveness and ease of installation. However, a key challenge lies in its limited Quality of Service (QoS) capabilities, especially for real-time applications like video streaming and VoIP, which require low latency and prioritized data handling. This study focuses on improving the QoS features of Wireless LAN by evaluating the performance of the newer 802.11ac standard compared to the earlier 802.11n standard. Using the Riverbed (OPNET) simulator, we model various real-world scenarios to assess how enhanced channel access mechanisms can address QoS limitations. Our findings indicate that 802.11ac, with its advanced channel access allocation, significantly outperforms 802.11n, particularly in reducing latency and ensuring better prioritization of critical data traffic. These improvements make 802.11ac a more suitable option for supporting modern, high-demand applications compared to its predecessor. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quality of service

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer Systems science & engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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Reinforcement Learning Approach to Solve: PBL Markov Model 5th

Reinforcement Learning Approach to Solve: PBL Markov Model

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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Parijatha, Kimidi Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819997060

PBL is an educational approach that centers on learners actively engaging with open-ended problems rather than just absorbing predetermined content. This method encourages in-depth understanding and the development of problem-solving skills. Notably, the Markov PBL approach doesn’t focus on providing answers but instead emphasizes the cultivation of valuable skills and attributes. These include attention to detail, enhanced coordination, and the strengthening of group dynamics and communication. The field of pedagogy has experienced a significant impact worldwide due to the pandemic. This global crisis led to the temporary closure of schools, colleges, and universities. In response, many educational institutions swiftly adopted online learning as the primary mode of instruction. Online learning encompasses various courses delivered entirely via digital platforms, with the exception of Massive Open Online Courses (MOOCs). Compared to traditional brick-and-mortar classroom settings, online learning via virtual classes conducted over the internet offers a different set of advantages. It provides learners with opportunities to enhance their skills through practical application and critical assessment. Additionally, it facilitates the retrieval of consolidated knowledge and encourages continuous learning within a collaborative team environment. This study aims to explore both the advantages and disadvantages of applying the Markov PBL method within the context of online learning. While the benefits are apparent, there are also challenges and limitations associated with this approach, which we will delve into further. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Findings of the Second Shared Task on Multilingual Coreference Resolution 6

Findings of the Second Shared Task on Multilingual Coreferen...

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CRAC Shared Task on Multilingual Coreference Resolution at 6th Workshop on Computational Models of Reference, Anaphora and Coreference, CRAC 2023

作者： Žabokrtský, Zdeněk Konopík, Miloslav Nedoluzhko, Anna Novák, Michal Ogrodniczuk, Maciej Popel, Martin Pražák, Ondřej Sido, Jakub Zeman, Daniel Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Prague Czech Republic University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic Institute of Computer Science Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9781955917025

This paper summarizes the second edition of the shared task on multilingual coreference resolution, held with the CRAC 2023 workshop. Just like last year, participants of the shared task were to create trainable systems that detect mentions and group them based on identity coreference;however, this year’s edition uses a slightly different primary evaluation score, and is also broader in terms of covered languages: version 1.1 of the multilingual collection of harmonized coreference resources CorefUD was used as the source of training and evaluation data this time, with 17 datasets for 12 languages. 7 systems competed in this shared task. © CRAC 2023. All Rights Reserved.

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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

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Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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Advancements in Wireless Networks: Transition from 5G to Beyond 2

Advancements in Wireless Networks: Transition from 5G to Bey...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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