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Generalized Net Model of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm and Its Application on Diabetes Dataset 2nd

Generalized Net Model of Density-Based Spatial Clustering of...

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2nd international Symposium on Bioinformatics and Biomedicine, BioInfoMed 2022

作者： Petrov, Petar Bureva, Veselina Kacprzyk, Janusz Laboratory of Intelligent Systems "Prof. Dr. Assen Zlatarov" University "Prof. Yakimov" Blvd. Burgas8010 Bulgaria Vocational School of Electrical Engineering and Electronics "Konstantin Fotinov" Burgas Bulgaria Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw01-447 Poland Warsaw School of Information Technology Ul. Newelska 6 Warsaw01-447 Poland

ISBN: (纸本)9783031310683

Density-based spatial clustering of applications with noise (DBSCAN) is a data science algorithm for density-based clustering. A Generalized net (GN) model of the DBSCAN algorithm is constructed. Intuitionistic fuzzy evaluations are defined to estimate the clustering procedure. The GN model of DBSCAN optimizes and estimates the procedure of the standard clustering algorithm. The clustering procedure is implemented using Python programming language. The method is then applied to a diabetes dataset to form clusters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Three-Dimensional Path Planning for Unmanned Aerial Vehicles Based on Multi-Strategy Improved Spider Wasp Optimization 2

Three-Dimensional Path Planning for Unmanned Aerial Vehicles...

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2nd international conference on the Frontiers of Intelligent Control, Measurement, and Signal Systems, FICMSS 2025

作者： Hu, Yanan Peng, Zhenni Yang, Xintong Ling, Zhicheng College of Electronic Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Fundamental Experimental Teaching Department Nanjing University of Aeronautics and Astronautics Nanjing210016 China

Aiming at the issues of slow search speed, limited local search capability and easy to become trapped in local optimum of Spider Wasp Optimization (SWO) in the trajectory planning algorithm of Unmanned Aerial Vehicles (UAVs), this paper proposes an Improved Spider Wasp Optimization (ISWO).The initialization of the population in the ISWO algorithm using chaotic mapping techniques enhances uniformity and diversity of initial solutions, preventing premature convergence and facilitating thorough exploration of the search space. This unpredictability helps avoid local optima traps. Additionally, the adaptive weight factor adjusts the equilibrium between exploration and exploitation founded on the optimization state, allowing efficient transitions between broad and focused searches. The Cauchy variant optimization enables larger jumps in the search space, aiding in escaping local optima and exploring distant areas for better solutions. Together, these improvements lead to faster convergence, reduced planning time, and better solution quality in complex optimization problems. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Mathematical programming

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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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Rural Electrification Using Computer Vision and Wireless Sensor Networks 2

Rural Electrification Using Computer Vision and Wireless Sen...

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2nd IEEE international conference on Intelligent Technologies, CONIT 2022

作者： Shukla, Rishabh Kaur, Harkeerat Iit Jammu Computer Science and Engineering Department Jammu India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

The procedure of supplying electrical electricity to rural and isolated locations is known as rural electrification. Rural towns are experiencing massive market imperfections as national systems fail to meet their electrical needs. when it comes to development regulations, the concept of a 'smart city' has gained a lot of traction. The purpose of a smart city is to make better use of public resources and deliver better services to inhabitants while lowering operating expenses. In this research, smart street light applications are employed to create wireless sensor networks for smart cities, and they are also used to construct the networks. The street lights feature wireless sensor kits that gather information about temperature, humidity, and carbon dioxide levels to keep a watch on the environment. They also keep track of regional changes. The following is how it works: In addition, each sensor package includes a camera for monitoring environmental activity. A Raspberry Pi programming card was used to create the sensor kit. A sensor kit has been created specifically for this card. We stacked the XBee wireless modules on top of the sensor kit to make it wireless. The information is saved in a database. An interface is used to provide the data to the user. © 2022 IEEE.

关键词： Rural areas

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Development of Two Dimension (2D) Game Engine with Finite State Machine (FSM) Based artificial Intelligence (AI) Subsystem 2

Development of Two Dimension (2D) Game Engine with Finite St...

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2nd international conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Adeniyi, Abidemi Emmanuel Brahma, Biswajit Adebiyi, Marion Olubunmi Awotunde, Joseph Bamidele Jimoh, Rasheed Gbenga Olasinde, Enoch Bandyopadhyay, Anjan Department of Computer Science Bowen University Osun State Iwo Nigeria McKesson Corporation 32559 Lake Bridgeport St FremontCA94555 United States Department of Computer Science Landmark University Omu-Aran Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Nigeria School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

With Al becoming more and more relevant in today's world, this project aims to develop a 2D game engine with an Al subsystem for state-driven agents, which is rarely implemented by a lot of 2D engines out there. In this study, a 2D game engine was designed with an FSM (Finite State Machine) - based AL subsystem using state-driven game agents. The engine was implemented using the Javascript programming language and the WebGL 2.0 graphics library/API. It is targeted at web-based games/simulations. Components and subsystems include physics, audio, math, rendering, and AI (based on finite-state machines). The FSM-based AI subsystem is a solution aimed at reducing the ambiguity and performance hits associated with creating 2D game AI in the naive approach. The AI subsystem creates an interface for 2D games to be created with a common paradigm, and simulated with a great level of realism. The state machine used in this study is used to represent a variety of behaviours, such as wandering, attacking, and fleeing. The following conclusions were drawn as regards the impact of the AI approach used on rendering performance;the naive approach to implementing game AI is also compared with the FSM approach in terms of rendering (frames per second/ FPS, or frame rate). With the naive approach (vector math) being used to implement AI, there was a drop in the rendering frame rate to 50 FPS. The FSM approach didn't affect the frame rate, which is usually at 60FPS. With a well-developed FSM (Finite State Machine), game agents can transition between states easily as a response to user input or stimulus from the game environment. The proposed method was tested by creating a prototype game with the 2D game engine. The prototype game was a straightforward side-scrolling platformer featuring a cast of non-player characters (NPCs). This approach to implementing 2D game AI goes a long way toward improving performance and mitigating ambiguity in the code. © 2024 Elsevier B.V.. All rights rese

关键词： artificial intelligence

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Identifying and Optimizing the Observation Strategy to Detect NEOs (Near-Earth Objects) Using Python 2nd

Identifying and Optimizing the Observation Strategy to De...

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2nd international conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Thakare, Prajwal Department of Computer Engineering COEP Technological University Pune India

ISBN: (纸本)9789819754113

Near-Earth Objects (NEOs) are any little celestial bodies that have orbits with closer proximity to that with Earth. A celestial body can be called a NEO if its closest distance to the Sun, i.e. Perihelion is at least 1.3 Astronomical Units (AU), where 1 AU is approximately 150 million kilometers (Km). If a NEO crosses Earth’s orbit at a distance of 140 m across, it can be called as a Potentially Hazardous Object (PHO). Consequently, a certain sub-set of NEOs advance our Earth quite nearly, and may cross our Earth’s orbit penetrating the atmosphere to the exterior of the Earth, or even collide with it to create craters if they impact a landmass or tsunamis if they strike on the oceans. Continuous sky observations over the years have already recognized and classified over 30,000 and more NEOs over a period of two centuries. Recent NEO detection models propose that there are still hundred thousand of NEOs yet more to be discovered. The study focuses on tracing these objects, classifying them under categories and visualizing the detected NEOs to build an observation strategy for our telescopes to detect these objects in a day sky using Python programming. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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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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2nd international conference on Intelligent Systems in Production engineering and Maintenance, ISPEM 2018

2nd International Conference on Intelligent Systems in Produ...

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2nd international conference on Intelligent Systems in Production engineering and Maintenance, ISPEM 2018

ISBN: (纸本)9783319974897

The proceedings contain 73 papers. The special focus in this conference is on Intelligent Systems in Production engineering and Maintenance. The topics include: Concept of power grid resiliency to severe space weather;promoting cross-border cooperation between science and small businesses as a source of innovation;definition of characteristic values for the efficient and safe implementation of electronic cam gears;design of performance indicators based on effective time and throughput variability. case study in mining industry;development of an intelligent drainage-humidifying control system based on neo-fuzzy neural networks;evolution of technical systems maintenance approaches – Review and a case study;forecasting the mountability level of a robotized assembly station;identification of challenges to be overcome in the process of enhancing innovativeness based on implementation of central European projects funded from interreg programme;improvement of production process scheduling with the use of heuristic methods;a computer application for drone parametrization: Developing solution for drone manufacturing;intuitive methods of industrial robot programming in advanced manufacturing systems;manufacturing activities modelling for the purpose of machining process plan generation;model of application of cluster analysis in storage area designing;module for prediction of technological operation times in an intelligent job scheduling system;Pricing and ordering decisions in a JELS-model for items with imperfect quality;probabilistic fuzzy approach to assessment of supplier based on delivery process;processing of design and technological data due to requirements of computer aided process planning systems;risk assessment for potential failures during process implementation using production process preparation;conveyor belt 4.0.

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Introduction to the Special Issue on Logic Rules and Reasoning: Selected Papers from the 2nd international Joint conference on Rules and Reasoning (RuleML+RR 2018)

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THEORY And PRACTICE OF LOGIC programming 2021年第1期21卷 1-3页

作者： Benzmueller, Christoph Parent, Xavier Ricca, Francesco Free Univ Berlin Dept Math & Comp Sci Berlin Germany Univ Luxembourg Dept Comp Sci Esch Sur Alzette Luxembourg Univ Calabria Dept Math & Comp Sci Arcavacata Di Rende Italy

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artificial Intelligence (AI), Machine Learning (ML) & Deep Learning (DL): A Comprehensive Overview on Techniques, Applications and Research Directions

Artificial Intelligence (AI), Machine Learning (ML) & Deep L...

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Sustainable Computing and Smart Systems (ICSCSS), international conference on

作者： Syed Mohtashim Mian Mohammad Shuaib Khan Mohd Shawez Amandeep Kaur Department of Computer Science & Engineering SRM University Haryana India Institute of Engineering and Technology Chitkara University Punjab India TMU Moradabad GNA University

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The subfield of artificial intelligence (AI) within computer science aims to create intelligent machines capable of performing tasks typically requiring human intelligence. This foundational concept posits that human intelligence can be sufficiently defined for machines to emulate. Machine learning (ML), a branch of AI, enables software programs to enhance their predictive accuracy without explicit programming, using historical data to forecast new output values. Deep learning, a subset of ML, involves training models to organize sounds, text, or images using neural networks and substantial labeled data. In some cases, deep learning models surpass human performance, achieving state-of-the-art accuracy. This research study explores the principles and advancements in AI, ML, and deep learning, emphasizing their transformative potential and applications.

关键词： Deep learning Training Accuracy Social networking (online) Human intelligence Computational modeling Symbols

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