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Open and Closed Source Models for LLM-Generated Metaheuristics Solving engineering Optimization Problem 28th

Open and Closed Source Models for LLM-Generated Metaheuris...

28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Senkerik, Roman Viktorin, Adam Kadavy, Tomas Kovac, Jozef Janku, Peter Pekar, Libor Guzowski, Hubert Smolka, Maciej Byrski, Aleksander Pluhacek, Michal A.I.Lab Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University Zlin Czech Republic Faculty of Computer Science AGH University of Krakow Krakow Poland Center of Excellence in Artificial Intelligence AGH University of Krakow Krakow Poland

ISBN: (纸本)9783031900648

This paper explores the applicability of generative AI (genAI), specifically Large Language Models (LLMs), for the automatic generation and configuration of metaheuristic algorithms to address a real-world engineering problem: the optimal parameter estimation of time-delay systems in interconnected heating-cooling loops. The study introduces a pioneering workflow and iterative architecture with feedback within the emerging field of genAI-driven optimization for real optimization problems, eliminating the need for manually crafted or modified algorithms. This automated system empowers domain experts in engineering to solve complex optimization problems with minimal knowledge of optimization algorithms, lowering the barrier to entry for sophisticated algorithm use. We demonstrate how LLMs can generate effective optimizers under conditions like connstrained optimization problems where the solution lies near the boundaries of the search space. Four state-of-the-art LLMs (closed and open-sourced) have been selected for experiments. These are GPT-4o, GPT-4o mini, Claude Sonnet 3.5 and Llama 3.1. All studied LLMs generated metaheuristics that outperformed the initialization baseline optimization method (Random Search and CMA-ES). Notably, the Claude Sonnet 3.5 model generated a metaheuristic with the best mean results, almost matching the performance of the tuned state-of-the-art DISH algorithm, as an example of adaptive Differential Evolution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Time of arrival

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Banjarese Chatbot Using Seq2Seq Model 4

Banjarese Chatbot Using Seq2Seq Model

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Setiawan, Bambang Abdi Utami, Ema Hartanto, Anggit Dwi Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9781665433945

The significant development of chatbot technology began with the implementation of the Seq2Seq (Sequence-to-Sequence) model which is based on two RNN (Recurrent Neural Network) architecture. Long-term training with vanilla RNN has the problem of vanishing or exploding gradient, so it is necessary to do research that focuses more on optimization steps for this problem. The optimization carried out on the Seq2Seq model is to apply a LSTM (Long-Short Term Memory) gate mechanism with a stable ability to understand long-term information. A GRU (Gated Recurrent Unit) is faster in training and an attention mechanism that can handle long sequence data. Also, gradient descent can minimize the error rate. A combination of gate types, gradient descent, and attention mechanisms needs to be done to produce a better conversational model. The test results of the conversational model performance were obtained by taking into account the metric scores in accuracy, precision, recall, F1, and loss by using 80% train data and 20% test data from 5000 lines of Banjarese conversation. In the 50th epoch, the best score was from testing the LSTM gate model with metric values of accuracy 64,67%, precision 85,20%, recall 62,49%, F1 72,09%, and loss 3,6073. © 2021 IEEE

关键词： Long short-term memory

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Predicting Estimated Time of Arrival Using Boosting Models 6

Predicting Estimated Time of Arrival Using Boosting Models

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Kam, Say-Hong Chong, Yung-Wey Ibrahim, Noor Farizah Keoh, Sye-Loong Phon-Amnuaisuk, Somnuk Abdul Rahim, Sharul Kamal School of Computer Sciences Universiti Sains Malaysia Penang Malaysia National Advanced IPv6 Centre Universiti Sains Malaysia Penang Malaysia School of Computing Science University of Glasgow Glasgow United Kingdom School of Computing and Informatics Universiti Teknologi Brunei Brunei Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350344349

Estimating the time of arrival (ETA) in public transportation can be challenging due to incomplete data and the complex nature of the urban environment. This study aims to address persistent criticism of the poor punctuality problem in Malaysian buses through the modeling of bus arrival time predictions. The study uses geographical and time data to predict bus arrival times through several boosting models. The data cleaning method enhanced data quality by eliminating invariable entries, segmenting the bus route for a more granular analysis, and encoding the data for improved structure and reliability. Through the implementation of Boruta for feature selection, relevant variables crucial for prediction were identified, contributing to the model's precision. The results highlighted LightGBM's superiority over AdaBoost and XGBoost, exhibiting the highest accuracy and a balanced level of complexity. This integrated methodology not only presents a robust prediction model but also showcases a potential practical implementation. © 2024 IEEE.

关键词： Forecasting

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A secured GA-WPC framework for scheduling the independent tasks in cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 13003-13015页

作者： Dubey, Kalka Sharma, S.C. Kumar, Mohit Kumar, Prashant Nasr, Aida A. Cloud Computing and Wireless Sensor Lab Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais Uttar Pradesh Amethi229304 India Department of Information Technology Dr BR Ambedkar National Institute of Technology Punjab Jalandhar144011 India Information Technology Department Faculty of Computers and Informatics Tanta University Tanta Egypt

Cloud computing proved itself a powerful and popular platform for providing computing services. It offers on-demand services to users with highly efficient resources over the internet. With the exponential increase in the number of internet users and due to the high acceptance rate to use the cloud services in everything, the demand for cloud platforms has also increased significantly. This leads to heavy workloads on the servers, which is a difficult task to manage due to the heterogeneity and uncertainty of the resources. However, several traditional, heuristic, and evolutionary-based approaches have been proposed to solve the problem of scheduling. Existing state-of-art techniques and frameworks have solved the problem up to a limited level. Still, these solutions have not given any effective and concrete solution for the overall resource scheduling problem. Therefore, this paper tries to overcome the bottleneck of existing solutions by presenting a new framework named Genetic Algorithm Water Pressure Change (GAWPC) to solve task scheduling problems efficiently. The GAWPC framework exploited the feature of the Genetic Algorithm (GA) and Water Pressure Change optimization (WPCO) techniques to form a new hybrid mechanism. In the proposed framework, GA has been deployed to find the optimal solution for a particular population. WPCO has been used to optimize fitness function for a fast convergence rate. The computer-based simulation has demonstrated promising benefits like improved computation cost, make-span time, and load balancing when compared with the most recent existing baseline techniques using CloudSim. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Genetic algorithms

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Tackling heavy-tailed noise in distributed estimation: Asymptotic performance and tradeoffs

Tackling heavy-tailed noise in distributed estimation: Asymp...

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Telecommunications Forum (TELFOR)

作者： Dragana Bajovic Dusan Jakovetic Soummya Kar Manojlo Vukovic Department of Power Electronic and Communication Engineering Faculty of Technical Sciences University of Novi Sad Department of Mathematics and Informatics Faculty of Sciences University of Novi Sad Department of Electrical and Computer Engineering Carnegie Mellon University Department of Fundamental Sciences Faculty of Technical Sciences University of Novi Sad

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

We present an algorithm for distributed estimation of an unknown vector parameter θ ∗ ∈ ℝ M in the presence of heavy-tailed observation and communication noises. Heavy-tailed noises frequently appear, e.g., in densely deployed Internet of Things (IoT) or wireless sensor network systems. The presented algorithm falls within the class of consensus+innovation estimators and combats the effect of the heavy-tailed noises by adding general nonlinearities in the consensus and innovations update parts. We present results on almost sure convergence and asymptotic normality of the estimator. In addition, we provide novel analytical studies that reveal interesting tradeoffs between the system noises and the underlying network topology.

关键词： Technological innovation Wireless sensor networks Heavily-tailed distribution Network topology Noise Estimation Vectors Telecommunications Internet of Things Convergence

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An Artificial Intelligence (AI) Based X-AODV Routing Protocol in Wireless Networks 4

An Artificial Intelligence (AI) Based X-AODV Routing Protoco...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Hussain, Khalid Ullah Sheikh, Md Amin Jazri, Husin Bin Muzzaffar, Saira Chowdhury, Sohini Kavita School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

The multi-hopping mechanism features in the wireless network environment, in which all nodes contain the information of link and connectivity in any infrastructure less manner. When mobile devices either jump from their original network or change their location without describing any fashion, it is named mobile ad hoc network MANET. To ensure the up to date information related to the location and to provide the routing traffic of the node, numerous protocols have been used to remain efficient in achieving this for instance, AODV, DSR, DSDV etc. While we, the peer-to-peer networking, have challenges in many places, security is something that warrants attention more than any other things. The signal corruption hampers data transmission in bedwire networks when the attacks take place. This kind of activity can touch anyone including the layers lying above the physical and below the network layer. In the current discussion we developed, proposition of diversified, efficient and secure AI-based X-AODV routing protocol. We have investigated our proposed protocol against the recent modified two protocols which are SAODV and ARAN. The outcome of simulation reveals that our proposed routing protocol takes place in successfully performing of attack detection and is also the best when it comes to providing security and performance. The Simulation outcomes exhibit the Networks' performance improvement. © 2024 IEEE.

关键词： Network layers

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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Real-Time Safety Helmet Detection Using Enhanced YOLOv5 Object Detection 9

Real-Time Safety Helmet Detection Using Enhanced YOLOv5 Obje...

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Zamri, Fatin Najihah Muhamad Kartiwi, Mira Zulkurnain, Nurul Fariza Yusoff, Nelidya Md. Gunawan, Teddy Surya Nur, Levy Olivia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Utm Kuala Lumpur Razak Faculty of Technology and Informatics Kuala Lumpur54100 Malaysia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350343380

Personal Protective Equipment (PPE) regulations require construction workers to wear safety helmets to ensure site safety. However, monitoring PPE compliance consistently in fast-paced, dynamic construction environments poses a significant challenge. In response, we developed a sophisticated object detection system that automates the real-time verification of helmet use, thereby improving safety standards and reducing the likelihood of accidents. Extensive research was conducted to analyze all feasible algorithms that can be implemented in the safety helmet detection system and compare the proposed model with an existing one to ensure the proposed system can give high accuracy and high inference speed. Therefore, YOLOv5 was identified as the ideal choice in terms of accuracy and speed, and it was then enhanced with optimized transfer learning. We began our methodology by pre-training a comprehensive Kaggle dataset before refining the model using Roboflow on a specialized dataset. Using PyTorch and YOLOv5, we conducted exhaustive model training, testing, and evaluation. Our system achieved a lightning-fast inference speed of 39.8 milliseconds and a remarkable 91.4 percent accuracy in identifying helmet compliance. The implementation of such object detection technologies has the potential to significantly increase safety helmet compliance, thereby creating a safer environment for construction workers. © 2023 IEEE.

关键词： Object detection

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Investigation of the acceleration influence on friction in bearings in dual-spin missile

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Materials Today: Proceedings 2023年 93卷 817-823页

作者： Ivana Todić Vladimir Kuzmanović University of Belgrade Faculty of Mechanical Engineering Weapon Systems Department Kraljice Marije 16 11120 Belgrade Serbia University of Belgrade Faculty of Mathematics Department for Computer Science and Informatics Studentski Trg 16 11000 Belgrade Serbia

Rockets and missiles fired from a tube usually have aerodynamic surfaces that are packed when the rocket is in the tube. One of the common fins folding solutions is the wrap-around fins. The wrap-around fins are usually separated from the rocket’s body by a bearing; thus the characteristics and the type of bearings used will have an effect on roll stabilization and the performance of roll stabilization autopilot. Also, during the flight, many forces act upon the missile thus affecting its exposed components. In turn, this may also affect the performance of the bearings that allow the wrap-around fins to rotate independently around the missile’s body. This dual-spin concept increases roll stabilization efficiency and reduces induced roll from lateral control. For this design to be effective and achieve the desired performance, it is critical to analyze how the movements of the missile affect the bearing separating the body from the wrap-around fins. Since the sections are separated by the bearing, various imperfections of the joint such as friction, misalignment, etc. combined with the acceleration of the missile may have a transitional influence on the performance of the wrap-around fins and thus roll stabilization. In this paper, we will first identify and explain the problem of acceleration influence on friction in bearings. Next, the laboratory equipment and experimental procedures for examining friction in bearings will be described in detail. We will then present and analyze the results obtained from the experiments. Finally, we will draw conclusions and present the design modification this investigation has led to as well as the improved roll autopilot performance achieved by using the appropriate bearing.

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