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Proceedings of the 41st International Conference on Machine Learning

作者： David Stein Bjoern Andres Faculty of Computer Science TU Dresden Germany Faculty of Computer Science TU Dresden Germany and Center for Scalable Data Analytics and AI Dresden/Leipzig

The linear ordering problem consists in finding a linear order < on a finite set A so as to minimize the sum of costs associated with pairs of elements a, b for which a < b. The problem is NP-hard and APX-hard. We introduce algorithms for solving the problem partially by deciding efficiently for some pairs (a, b) whether a < b is in an optimal solution. To do so, we construct maps from the feasible set of orders to itself and establish efficiently testable conditions on the cost function of the problem for which these maps are improving. We examine the effectiveness and efficiency of these conditions and algorithms empirically, on two data sets.

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Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis 24

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Alazaidah, Raed Hassan, Mohammad Al-Rbabah, Lara Samara, Ghassan Yusof, Marina Al-Sherideh, Ala'a Saeb Zarqa University Ai Faculty of Information Technology Department of Data Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan The Hashemite University Faculty of Engineering Department of Computer Engineering Zarqa Jordan Selanger Malaysia Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350384307

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection. © 2023 IEEE.

关键词： Classification Feature Selection Machine Learning Medical Diagnosis Prediction

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Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

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Journal of Bionic Engineering 2023年第3期20卷 1263-1295页

作者： Jeffrey O.Agushaka Absalom E.Ezugwu Oyelade N.Olaide Olatunji Akinola Raed Abu Zitar Laith Abualigah School of Mathematics Statisticsand Computer ScienceUniversity of KwaZulu-NatalKing Edward AvenuePietermaritzburg CampusPietermaritzburg3201KwaZulu-NatalSouth Africa Department of Computer Science Federal University of LafiaLafia950101Nigeria Unit for Data Science and Computing North-West University11 Hoffman StreetPotchefstroom2520South Africa Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044Abu DhabiUnited Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

关键词： Improved dwarf mongoose Nature-inspired algorithms Constrained optimization Unconstrained optimization Engineering design problems

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Customer Segmentation Based on Recency, Frequency, Monetary, Variety and Duration (RFMVD)

Customer Segmentation Based on Recency, Frequency, Monetary,...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Agus Sundari Maya Silvi Lydia Muhammad Anggia Muchtar Department of Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

In banking, maintaining customer retention and customer satisfaction are important. Effective customer segmentation can be a strategic tool to improve customer loyalty and business performance. This research can assist banks in developing marketing strategies to retain customers and improve services based on bill payment transactions. The data that will be used in this study is bill payment transaction data at regional banks in 2023, as for the types of bill payments made by customers, namely electricity, water, telephone payments, and internet bills. The amount of data used is 702,174 rows. This research uses K-Means clustering to group customers based on recency (the last time the customer made a transaction), frequency (the number of customers making transactions), monetary (the nominal amount of money spent by the customer), variety (the number of types of bills paid) and duration (the average duration of time the customer makes the previous transaction until the next transaction) (RFMVD). The results of customer segmentation based on recency, frequency, monetary, variety and duration using K-Means clustering produce 3 customer groups, namely passive customers, loyal customers, and VIP customers. The silhouette score value on customer segmentation using K-Means clustering is 0.7303, which indicates that the grouping is quite good and the number of clusters is correct.

关键词： Time-frequency analysis Technological innovation Electricity Customer satisfaction Banking Telephone sets History Business

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Quantitative Scalability of Nodes and Geographical Coverage in LEACH Protocol 6

Quantitative Scalability of Nodes and Geographical Coverage ...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Mir, Foudil Meziane, Farid Bounceur, Ahcene Laouid, Abdelkader Faculty of Exact Sciences Bejaia06000 Algeria University of Derby Data Science Research Centre Derby United Kingdom Kfupm Information And Computer Science Department Dhahran Saudi Arabia University of El Oued Liap Laboratory El Oued39000 Algeria

ISBN: (纸本)9798350350265

Energy optimisation using clustering remains a constant challenge in wireless sensor networks (WSNs). The high volume of data generated within these networks leads to excessive energy consumption when transferring it to the processing center. Therefore, clustering protocols, in particularly hierarchical routing protocols dedicated to WSNs, must imperatively integrate energy efficiency mechanisms in order to extend the functional lifetime of the nodes and the network. In this paper, we focus on an in-depth analysis of scalability in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The LEACH protocol, renowned for its random and distributed approach to WSNs energy optimisation, forms the focus of our analysis and exploration. Our study focuses in particular on the quantitative scalability of nodes and the scalability of geographical coverage. The aim is to improve our understanding of the behaviour of the LEACH protocol in large-scale environments, particularly with regard to node and network lifetime and energy management parameters. © 2024 IEEE.

关键词： Scalability

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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AI-Driven Solutions for Social Engineering Attacks: Detection, Prevention, and Response 2

AI-Driven Solutions for Social Engineering Attacks: Detectio...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Fakhouri, Hussam N. Alhadidi, Basim Omar, Khalil Makhadmeh, Sharif Naser Hamad, Faten Halalsheh, Niveen Z. University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman Jordan Al-Balqa Applied University Department Of Computer Information Systems Salt Jordan University Of Petra Computer Science Department Amman Jordan Sultan Qaboos University Oman The University Of Jordan Amman Amman Jordan

ISBN: (纸本)9798350394962

With the rapid evolution of cyber threats, social engineering attacks have become increasingly sophisticated, leveraging human vulnerabilities to bypass traditional security measures. While many conventional defense mechanisms have been overwhelmed, Artificial Intelligence (AI) offers a promising avenue to detect, prevent, and respond to these emerging threats. This research analyzes the intricacies of contemporary social engineering attacks, from their methods of deployment to their recent adaptations, such as leveraging social media and mobile apps. By contrasting prior solutions with the potential of AI-based defenses, we highlight the key role of machine learning in behavioral pattern recognition, Natural Language Processing's (NLP) efficacy in identifying phishing attempts, and predictive analytics' power to anticipate future attack vectors. Through detailed case studies, we showcase real-world scenarios where AI mechanisms have successfully countered social engineering ploys. The findings reveal that AI-enhanced mechanisms significantly improve the identification and mitigation of social engineering threats. Specifically, AI-driven behavioral analytics effectively detect subtle, manipulative cues indicative of phishing and other deceitful tactics, considerably reducing the incidence of successful attacks. Furthermore, predictive analytics has shown great promise in forecasting and preemptively countering potential cyber threats, In addition, while effective, AI tools must evolve with the changing tactics of cyber threats, Continuous learning and updating are necessary to maintain and improve accuracy and effectiveness. © 2024 IEEE.

关键词： Machine learning

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Using GPT and authentic contextual recognition to generate math word problems with difficulty levels

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Education and Information Technologies 2024年第13期29卷 1-29页

作者： Hwang, Wu-Yuin Utami, Ika Qutsiati Graduate Institute of Network Learning Technology National Central University Taoyuan 320317 Taiwan Department of Computer Science and Information Engineering National Dong Hwa University Hualien 974301 Taiwan Data Science Technology Engineering Department Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia

Automatic generation of math word problems (MWPs) is a challenging task in Natural Language Processing (NLP), particularly connecting it to real-life problems because it can benefit students in developing a higher level of mathematical thinking. However, most of the MWPs are presented within a scholastic setting in a decontextualized way. This paper describes a prototype system that generates authentic math word problems (i.e., real-life problems) using authentic contextual recognition with controlled difficulty levels. Our innovative approach includes the acquisition of authentic contextual information through recognition technology, an instructional-based prompt generator for three different difficulty levels, and question generation through the Generative Pre-trained Transformer (GPT) model. We evaluated the performance of the system in terms of the quality of generated questions using automatic evaluation and human evaluation. Further, we assessed the usability of the system using heuristic evaluation. The automatic evaluation showed our generated MWPs were relevant for geometry topics and varied in sentence generation. The human evaluation found our system generated more realistic problems with satisfactory language quality and mathematical validity. Our system also produced more questions with higher difficulty levels based on human evaluation. Heuristic evaluation captured the usability of the system and highlighted the potential to be applied in a pedagogical context for mathematics learning. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentic context Difficulty level Math word problems Natural language processing Primary school Question generation Technology education

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AI-Driven Sentiment Analysis for Music Composition 18th

AI-Driven Sentiment Analysis for Music Composition

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18th EAI International Conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TRIDENTCOM 2023

作者： Wang, Qinyuan Qu, Youyang Cheng, Haibo Yu, Yonghao Wang, Xiaodong Gu, Bruce Sydney Conservatorium of Music University of Sydney Sydney Australia Jinan China Faculty of Data Science City University of Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Engineering Design and Construction Melbourne Polytechnic Melbourne Australia

ISBN: (纸本)9783031513985

In the realm of music composition, sentiment plays a pivotal role in connecting compositions with their audience, evoking emotions and memories. With the rapid evolution of artificial intelligence (AI), there exists a burgeoning interest in utilizing AI for sentiment analysis in various domains, including textual data, social media, and film. This paper delves into the novel application of AI-driven sentiment analysis specifically tailored for music composition. Leveraging diverse music datasets across multiple genres and eras, we introduce an innovative methodology that breaks down music into foundational features such as melody, rhythm, timbre, and harmony. Through the application of advanced AI techniques, including neural networks and Long Short-Term Memory (LSTM) models, we aim to accurately map these features to a wide spectrum of sentiments. Our results showcase not only the potential accuracy and precision of our chosen models but also the richness of music compositions they can produce, underscoring the viability of AI in enhancing the emotional depth of musical works. The implications of this research stretch from aiding composers in creating more resonant pieces to the potential therapeutic applications of AI-composed music, tailored to specific emotional needs. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Sentiment analysis

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