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Advances in Fuzzy Relation Inequalities and Optimization Techniques

Advances in Fuzzy Relation Inequalities and Optimization Tec...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Udit Deo Jay Kumar Jain Mms Beg Department of Mathematics and Computer Applications MANIT Bhopal Bhopal India Department of Computer Engineering Zakir Husain College Of Engineering and Technology AMU Aligarh India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Fuzzy relation inequalities (FRIs) play a crucial role in various optimization and decision-making problems that involve uncertainty and imprecision. The solution of such inequalities has been an active area of research, leading to the development of a wide array of advanced algorithms designed to handle fuzzy constraints in optimization problems. This paper reviews the most prominent algorithms developed for solving fuzzy relation inequalities, focusing on the evolution of methods from traditional linear programming to modern metaheuristic algorithms. We explore linear programming approaches that integrate fuzzy relational constraints using different composition operations, such as max-min, max-Łukasiewicz, and addition-min compositions. The advancements in these methods, particularly in handling large-scale and complex fuzzy systems, are highlighted. Additionally, the paper examines the application of metaheuristics, including Particle Swarm Optimization (PSO), in solving fuzzy optimization problems under relational inequalities. The use of bipolar fuzzy relations in optimization models is also reviewed, emphasizing their ability to model conflicting objectives in decision-making. Moreover, geometric programming, hybrid algorithms combining fuzzy relations with other optimization paradigms, and future trends in integrating machine learning techniques into fuzzy optimization are discussed. The review provides valuable insights into the state-of-the-art algorithms for fuzzy relation inequalities, offering potential directions for future research and applications in dynamic, real-world systems. The proposed methods are crucial for improving decision-making in fields such as healthcare, finance, and intelligent systems, where uncertainty and imprecision are inherent.

关键词： Uncertainty Machine learning algorithms Reviews Heuristic algorithms Decision making Metaheuristics programming Linear programming Particle swarm optimization Fuzzy systems

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd international conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Comparative Analysis of Large Language Models in Solidity Smart Contract Vulnerability Detection: Review

Comparative Analysis of Large Language Models in Solidity Sm...

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Advanced Innovations in Smart Cities (ICAISC), international conference on

作者： Amjad Almaghthawi Wael M.S. Yafooz Computer Science Department College of computer Science and Engineering Taibah University Madinah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Blockchain (BC) technology and artificial Intelligence (AI) are promising areas with potential for powerful, secure, and decentralized applications in various sectors. Also, LLMs, are essential advanced machine learning frameworks, which are now used in various applications, including customer service, chatbots, code generation, and language translation. In terms of software development, LLMs are being investigated as a potential way to create smart contracts, focusing on their ability to emulate human programming techniques. For that purpose, this study reviews the capabilities and limitations of Large Language Models (LLMs) like GPT-3.5, PaLM2, and Code Llama in detecting vulnerabilities in Solidity smart contract code.

关键词： Technological innovation Codes Translation Reviews Large language models Smart contracts Focusing Static analysis Blockchains Software development management

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Deep Learning for Early Skin Cancer Detection: A Comparative Study on Hybrid CNN Models 2

Deep Learning for Early Skin Cancer Detection: A Comparative...

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2nd international conference on Data, Information and Computing science, CDICS 2024

作者： Matiray, Sakshi Singh, Law Kumar GLA University Mathura India Amity School of Engineering and Technology Amity University Punjab Mohali India

ISBN: (纸本)9781643685762

Globally, skin cancer—especially melanoma—is a major health concern, and better patient outcomes depend on early and precise diagnosis. Manual examination is a common component of traditional diagnostic techniques, but it is labor-intensive and subject to error. Promising approaches to automate skin lesion analysis have been made possible by recent developments in deep learning. In order to overcome the shortcomings of current approaches, this work investigates the creation of a hybrid deep learning model that blends VGG16 and ResNet50 architectures. The suggested approach incorporates sophisticated preprocessing methods, such as picture normalization and data augmentation, to improve feature extraction and classification accuracy using datasets like ISIC 2018 and HAM10000. Important results show that the hybrid model outperforms state-of-the-art benchmarks in performance metrics, achieving 98.75% training accuracy and 97.50% validation accuracy. The model's enhanced precision (97.60%), recall (97.55%), and F1 score (97.58%) highlight how reliable it is at differentiating between benign and malignant tumors. The study also emphasizes how crucial it is to strike a balance between model complexity and computing performance in order to support practical clinical deployment. This study advances computer-aided diagnostic (CAD) systems for the identification of skin cancer by tackling issues including class imbalance and dataset unpredictability. The suggested method has a great deal of promise to improve early diagnosis, lessen the need for invasive treatments, and assist dermatologists in making clinical decisions. This paper thoroughly reviews current methodologies used in skin cancer detection, offering a timely resource for researchers developing automated and precise melanoma detection models. © 2025 The Authors.

关键词： Contrastive Learning

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Simplifying code translation: Custom syntax language to C language transpiler

Simplifying code translation: Custom syntax language to C la...

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Computational Intelligence, Communication Technology and Networking (CICTN), international conference on

作者： Pecheti Charan Lalith Sujal Goel Misha Kakkar Seema Sharma Department of Computer Science and Engineering Amity University Noida Uttar Pradesh India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In today’s world, programming has become a very essential skill as it is used everywhere, from creating software’s for operating systems to analysis of patients’ health care data in biomedical engineering. Programmers usually face a lot of difficulties to understand beginner level coding languages having low-level syntax. The proposed study "Simplifying Code Translation: Custom Syntax Language to C Language Transpiler", targets the specified problem and provides the solution in a form of transpiler that converts the newly developed custom language (made up of high-level syntax) to C language. The developed tool minimizes the overall cost incurred during the code designing process. This transpiler consists of 4 main parts: preprocessor, lexer, parser and emitter. It first starts by creating preprocessor which handles preprocessor directives such as 'define' and 'include'. This preprocessed code is then passed onto lexer which tokenizes the code. The parser is responsible for creating Abstract Syntax Tree (AST) using the tokens provided. The final AST is then transformed to C program by the emitter. Then output C code maintains proper syntactic and semantic structure of the original custom syntax language code.

关键词： Program processors Codes Translation Semantics C languages Syntactics programming Libraries Software tools Standards

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Long-Term Reversible Hydropower System Operation Optimization Based on Fast Unit Commitment

Long-Term Reversible Hydropower System Operation Optimizatio...

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Smart Grid and artificial Intelligence (SGAI), international conference on

作者： Chengyuan Li Luyu Ma Xiaowei Yu School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan China Centrual China Branch State Grid Corporation of China Wuhan China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

To address the urgent demand for large-scale energy storage in renewable energy transition of China, this study proposes a schematic of reversible hydropower system and its long-term operation optimization model. By employing Fast Unit Commitment (FUC) algorithm, the model effectively reduces computational complexity while maintaining accuracy. Case studies demonstrate that the proposed method takes 1,000 times less time compared to nonlinear programming methods. With cascade reservoirs retrofitted into reversible hydropower stations, wind and solar curtailment rates decrease by 93.83% and 88.72 % , respectively, leading an annual profit increase of 0.47 billion CNY. These results show the algorithm's efficiency and the retrofit's economic benefit, providing critical insights for enhancing renewable integration in high-penetration grids.

关键词： Vibrations Systems operation Optimization models Hydroelectric power generation programming Reservoirs Smart grids Power systems Resource management Surges

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Optimization Study of Energy Management of Intelligent Unloading Device Based on Supercharged Power System

Optimization Study of Energy Management of Intelligent Unloa...

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Smart Grid and artificial Intelligence (SGAI), international conference on

作者： Xingjian Zhang Chao Lu Xiaodong Lv Guangming Zhang Shuohuang Railway Development Co. Ltd of CHN ENERGY Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

To cope with the dual demands of loading and unloading efficiency and environmental protection in the modern logistics industry, this paper investigates an intelligent unloading device energy management method based on a supercharged power system. By combining a diesel generator with a power battery to form a dual-source energy supply architecture, it can provide sufficient power when the unloading vehicle is under high load and shave peaks and fill valleys during the low load or idle phase, realizing a comprehensive balance between fuel economy and emission control. The paper first establishes a system simulation platform including a diesel fuel consumption model and a battery SOC model, and then factors such as fuel consumption, emission, and battery life are incorporated into a multi-objective optimization framework to explore the performance differences among strategies based on rule-based control, offline dynamic programming (DP), and model predictive control (MPC). Simulation results show that the optimization approach can reduce fuel consumption by 5% to 9%, and significantly smooth battery SOC and reduce emissions compared with the traditional empirical strategy. The hardware-in-the-loop test preliminarily verifies the effectiveness of the proposed method, which supports the subsequent popularization and application under more complex or high-power working conditions.

关键词： Power system stability Predictive models Batteries Dynamic programming Fuels State of charge Energy management Optimization Predictive control Load modeling

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Laying the Foundation with the Essential Quintessence of Chinese Culture: Exploring the Deep Integration and Realization of Specialized Connotations of IPE in Python Course 24

Laying the Foundation with the Essential Quintessence of Chi...

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Proceedings of the 2024 2nd international conference on Information Education and artificial Intelligence

作者： Zhipan Wu Bolian Fang Huaying Du Lunman Deng Yunhe Jiang School of Computer Science and engineering Huizhou University Huizhou Guangdong China School of Information Technology City College of Huizhou Huizhou Guangdong China

ISBN: (纸本)9798400711732

This paper discusses the integration of professional knowledge, traditional Chinese culture, and the integration of IPE in the course of Python programming, in order to enhance our cultural self-confidence, stimulate our academic interest, promote our overall development, and realize the goal of curriculum-based ideological education. We demonstrate the "1+1>2" effect of this integration by planning projects related to traditional culture. This approach enriches higher education and lays a solid foundation for cultivating talents with patriotism, social responsibility and innovative spirit. Chinese cultural elements are effectively integrated into teaching and learning through systematic curriculum design, integration of classroom and extracurricular activities, and well-established assessment mechanisms.

关键词： Excellent traditional Chinese culture

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Research on Teaching Reform of AIGC Empowering C Language programming Course 24

Research on Teaching Reform of AIGC Empowering C Language Pr...

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Proceedings of the 2024 2nd international conference on Information Education and artificial Intelligence

作者： Nan Xie Weimin Chen College of Computer Science and Technology Zhejiang University of Water Resources and Electric Power Hangzhou Zhejiang China College of Mechanical and Electrical Engineering China Jiliang University Hangzhou Zhejiang China

ISBN: (纸本)9798400711732

The new idea development has become more and more broadly in the new generation of information technology education. And the computer related general education courses for non-computer majors in Chinese universities are constantly undergoing deeper teaching research and exploration, which including the courses’ curriculum design, teaching mode, teaching methods, teaching design, teaching system, etc., at the same time, the changes in educational reform policies for computer general education courses in universities are all following. The C Language programming course is a compulsory course of non-computer major general education courses in many undergraduate universities. Through studying, the course can not only cultivate students' computer thinking and innovative thinking, but also continuously improve their logical thinking ability and programming debugging practical skills and application skills. What's more, it can expand students’ multidimensional cognition and knowledge seeking ability in the world and help students fully understand the current new generation of information technology foundation, comprehensively master the key, difficult and programming skills of C language. AIGC empowering C course teaching mode has been proposed in response to several explicit issues in the current C programming course teaching, such as poor personalized learning experience, weak practical ability, and slow problem-solving yield. This teaching model builds studying framework based on the multi-source data fusion aims to promote personalized learning for students, deepen their understanding of relevant knowledge, and provide immediate problem feedback. In addition, the model can align the course teaching objects through much training to stimulate better students' learning motivation, improve their comprehensive quality, and cultivate outstanding talents. This paper would provide a new path and reference for the teaching and reform ideas of general education courses for appli

关键词： AI Empowering teaching reform

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2nd international Workshop on the art, science, and engineering of Quantum programming (QP 2024)

Programming Companion 2024 - Proceedings of the 8th Internat...

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programming Companion 2024 - Proceedings of the 8th international conference on on the art, science, and engineering of programming 2024年 XV-XVI页

作者： Arcaini, Paolo Yue, Tao Zhao, Jianjun National Institute of Informatics Japan Beihang University China Kyushu University Japan

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