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Dual-Population-Iterating and Preys-Gathering MPA for XGBoost Optimization 16th

Dual-Population-Iterating and Preys-Gathering MPA for XGBoos...

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16th international conference on Genetic and Evolutionary computing, ICGEC 2024

作者： Li, Jiacheng Lv, Li Kang, Ping Tian, Xiumei School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789819615308

The marine predator algorithm (MPA) often struggles with issues such as low population diversity, susceptibility to local optima, and population aggregation when optimizing XGBoost, which is sensitive to numerous parameters. To address these challenges, we propose the dual-population-iterating and prey-gathering marine predator algorithm (DPGMPA) and apply it to XGBoost parameter optimization. Initially, we utilize a dual-population-iterating strategy to create the original population and an opposition-based learning population. Brownian motion and Laplace distribution respectively control the step size to guide population updates toward the global optimum, enhancing population diversity. Subsequently, the preys-gathering strategy is employed, selecting prey with the highest accuracy in each iteration and having neighboring preys converge towards them to mitigate local optima. Furthermore, we optimize the Fish Aggregating Devices (FADs) by introducing variable weight control, addressing the population aggregation issue. To validate the parameter optimization capability of this algorithm for XGBoost, we conducted experiments on six datasets, using accuracy and F1 score as evaluation metrics, and compared the results with five other algorithms. The experimental results demonstrate that the DPGMPA algorithm more effectively escapes local optima when optimizing the XGBoost classifier, significantly improving XGBoost's classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Laplace distribution Marine Predator Algorithm normal distribution parameter optimization population diversity XGBoost

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An Integrated IoT and Wireless System for Energy Optimization and Real-Time Monitoring Towards Sustainable and Efficient Building Management in Smart Buildings

An Integrated IoT and Wireless System for Energy Optimizatio...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Singh, Someet Malik, Praveen Kumar Chauhan, Ashish Singh Kumar, Manoj Yamsani, Nagendar Singh, Parulpreet School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Uttaranchal University Uttarakhand Dehradun India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527495

At a time when urbanization is increasing, and the world's energy need is growing, energy efficiency in building elements has become a key factor in the environment impact mitigation and operational cost reduction strategies. Building management is governed by traditional operational features that are often inadequate to contemporary occupation patterns, infrastructural demands, and environmental changes. Consequently, there is a growing tendency towards the implementation of integrated systems such as autonomous management systems which can oversee energy use and other resources without sacrificing comfort and safety. Cloud storage and wireless technologies have now made it possible to improve energy management like never before. By making use of such technologies, smart building systems are able to receive, transmit and monitor data from many networks in real time. This capability further enhances the control of energy-demanding devices and processes such as heating, ventilation, and lighting, which are controlled and manipulated by preset schedules and manual settings. In addition to this, predictive maintenance and detection of inefficiencies is possible due to real time data analysis trends, usage history and anticipation of external consumption forces. © 2025 IEEE.

关键词： Smart homes

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Deep Neural Network Solver for HJB Equations 16th

Deep Neural Network Solver for HJB Equations

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16th APCA international conference on automatic control and Soft computing, controlO 2024

作者： Davey, Ashley Zheng, Harry Department of Mathematics Imperial College LondonSW7 2BZ United Kingdom

ISBN: (纸本)9783031817236

This paper proposes a deep learning algorithm for solving stochastic control problems, with a focus on the utility maximisation problem. The algorithm solves Markovian problems via the Hamilton Jacobi Bellman (HJB) equation. We solve this highly nonlinear partial differential equation (PDE) with a second order backward stochastic differential equation (2BSDE) formulation. The convex structure of the problem allows us to describe a dual problem that can either verify the original primal approach or bypass some of the complexity. We apply the algorithm to problems with power, log and non-HARA utilities in the Black-Scholes model. Numerical experiments show highly accurate results with low computational cost, supporting our proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic control systems

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Hardware Security in Evolving FinTech Landscape 5th

Hardware Security in Evolving FinTech Landscape

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5th international conference on computing and Network Communications, CoCoNet 2023

作者： Bhowmik, Biswajit Dongala, Joshitha Reddy Sudhama, Kruthika K. Antony, Reshma Tresa Girish, K.K. Maharshi Sushrut CAS Lab BRICS Laboratory Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Surathkal575025 India

ISBN: (纸本)9789819745395

The assimilation of technology into the financial sector, often referred to as FinTech, has brought about a significant transformation. This shift has not only widened the scope of financial inclusivity but has also fundamentally reshaped the contours of financial solutions delivered. As FinTech solutions continue to empower individuals with greater control over their finances through mobile banking, digital wallets, and advanced data analytics, the security of these innovations becomes paramount. While software security has traditionally received more attention, this paper underscores the significance of hardware security, which serves as the foundational infrastructure for software security measures. It delves into the factors used to evaluate hardware security and outlines various categories of hardware attacks. A case study, focusing on point-of-sale (PoS) systems, exemplifies the importance of hardware security in FinTech. Ultimately, this research contributes to a comprehensive understanding of the evolving FinTech landscape and its implications for both financial inclusion and cybersecurity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Real-Time Anomaly Detection in Industrial Automation Using Generative Adversarial Networks 5

Real-Time Anomaly Detection in Industrial Automation Using G...

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5th IEEE international conference on Advances in electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Changala, Ravindra Khan, Shamim Ahmad Kumar, Vijay Rani, V. Kanpur Dhilli, K. Raj, I. Infant Department of Cse Guru Nanak Institutions Technical Campus Hyderabad India Glocal School of Science & Technology Glocal University Department of Ece Uttar Pradesh India Department of Physics Graphic Era Hill University Dehardun India Graphic Era Deemed to be University Dehradun India Hindustan Institute of Technology and Science Department of Eee Chennai India Vignan's Institute of Engineering for Women Dept of Ece Visakhapatnam India K. Ramakrishnan College of Engineering Department of Cse Tamil Nadu Trichy India

ISBN: (纸本)9798350356236

The study presents the use of GANs in anomaly detection for real-time industrial automation. A new model is proposed and implemented in Python, taking into consideration the dual nature of the algorithm known as GANs, which work on a dual framework including a generator and a discriminator. The normal behavior of industrial automation must be generated by a generator;meanwhile, the discriminator distinguishes between normal and anomalous data. It would facilitate the effective identification of anomalies, through adversarial training, based on learning normal operational patterns in real time, with the advantage of being adaptable in very dynamic and complex industrial environments. The ability to be timely about intervention, given its real-time capability, significantly enhances such attributes as predictive maintenance, improved quality control, and safe and efficient operations. This has been found in the study that the right identification of anomalies will prevent expensive downtime and will be a sign of smooth industrial process. In the proposed GAN model, 99.12% accuracy is achieved compared with other approaches, such as DBN, AnoGAN, and BiGAN, with an impressive gap of 4.05%. Thus, superior performance indicates that GANs are robust and reliable for dealing with real-time industrial automation challenges. A critical characteristic of this Python implementation model is its adaptability, flexibility, and amenability to a practitioner in the industry as much as a researcher. Overall, this study further provides significant additions to the overall understanding of anomaly detection in an industrial setting with its demonstration capabilities toward the resiliency enhancement and efficiency potential in modern systems of industry through GAN applications. © 2025 IEEE.

关键词： Office automation

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Emotion Decoding of Speech Using NLP Analysis Approach

Emotion Decoding of Speech Using NLP Analysis Approach

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Bobbili, Charitha Dhushetti, Sri Varsha Harshith, Y. Shanmugapriyan, J. Hariharan, Shanmugasundaram Vaishnavi, Bandar Vardhaman College of Engineering Department of Artificial Intelligence and Data Science India Nsn College of Engineering and Technology Dept of Electrical and Electorics Engineering India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India

ISBN: (纸本)9798331527495

Emotion decoding of speech using NLP provides an integrated solution for emotion-driven applications. This project creates a comprehensive system for speech-to-text conversion, sentiment analysis, transcription accuracy evaluation, and senti- ment visualization. The system converts spoken audio into text using the SpeechRecognition library and the Google Web Speech API. To ensure reliability, transcription accuracy is assessed using the Levenshtein distance algorithm, which calculates the smallest number of edits necessary to determine the similarity between the transcribed text and the reference text. VADER (Valence Aware Dictionary and Sentiment Reasoner) from the NLTK library is used by the system for emotion analysis. It is a potent tool for assessing sentiment intensity and assigning sentiment scores to associated emotional states like positive, negative, or neutral. Actionable insights into the speech's emotional content are offered by this analysis. The system creates pie charts using Matplotlib to show sentiment distribution in order to improve interpretability. Users can quickly and naturally understand the emotion landscape thanks to this functionality. This system is well-suited for real-world uses like psychological analysis, where emotional states offer vital diagnostic information, and customer service, where comprehending client emotions is essential. It does this by integrating speech processing, transcription evaluation, sentiment analysis, and visualization into a coherent framework. © 2025 IEEE.

关键词： Emotion Recognition

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The Intersection of Algorithms, Automata Theory, and Formal Languages in Computational Theory

The Intersection of Algorithms, Automata Theory, and Formal ...

引用

2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Kumar, Avneesh Awasthy, Neeta Jose, A. Sujin Ramesh, Gajula Gupta, Manish Srinija, K. Najm, Rusul Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P Mathura India New Horizon College of Engineering Bangalore India Griet Department of Cse Bachupally Telangana Hyderabad India Lovely Professional University Phagwara India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798331527495

Initially, the FLAGS methodology and a concise study schedule are presented. Combining automata and formal grammars, FLAGS has revolutionized computational theory. SAA, GSTA, and GAGD comprise the last three numerals of FLAGS. They exhibit greater adaptability, productivity, and the generation of novel ideas more frequently when they collaborate. GSTA guides the development of the automaton's real-time state through the use of formal language grammars. This results in a comprehensive and precise computational model. Official language signals are appended to the robot's orthography by the SAA. Text can be represented by flags in an extensive variety of arrangements. In order for FLAGS to function in computer and innovative language environments, AGDE modifies formal language standards to accommodate computer requirements. FLAGS is more innovative, adaptable, and practicable than six well-known approaches, according to the study's findings. Values of the Language Harmony Index (LHI) may be utilized to assess the productivity of FLAGs. It evaluates the compatibility and performance of various linguistic forms when combined. The abstract emphasizes the influence of FLAGS on computational theory. Through the integration of structured languages, computer science, and automata theory, this one-of-a-kind method produces results. This approach revolutionizes linked learning due to its computer-friendly nature and utilization of the target language. © 2025 IEEE.

关键词： Turing machines

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Big Data Infrastructures Using Apache Storm for Real-Time Data Processing

Big Data Infrastructures Using Apache Storm for Real-Time Da...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Ranganathan, Chitra Sabapathy Sampathrajan, Rajeshkumar Reddy, P. L. Kishan Kumar Parkavi, S. Meenakshi, B. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Andhra Pradesh Guntur India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Apache Storm-based Big Data infrastructures strive to provide a scalable and fault-tolerant platform that will transform real-time data processing. The goal is to use Apache Storm's features to handle and analyze massive amounts of data in real-time, guaranteeing correct insights that are delivered promptly. The objective is to provide a solid foundation that can manage data moving at high speeds from many sources, allowing for analytics in real-time and continuous computing. The goal is to maximize throughput while reducing delay via architectural optimization and efficient data flow methods. The goal is to improve Apache Storm's capabilities and make sure data processes smoothly across dispersed systems by combining it with other big data technologies. The end goal is a robust system that can provide analytics on data in real-time, which will aid in decision-making across several industries, including banking, telecoms, and social media. Efficiency, scalability, and dependability in managing massive data streams are key considerations. The data flow topology dataset for 5 distinct components and 5 streams shows throughput values ranging from 2500 to 4100 tuples per second. Similarly, the fault tolerance and reliability dataset for 5 distinct nodes and 5 different intervals shows throughput values ranging from 700 to 1050 tuples per second. All these results are derived from real-time sensor data used for traffic monitoring. © 2025 IEEE.

关键词： Large datasets

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Probabilistic Modeling and Uncertainty Quantification of Harmonic Distortions in Grid-Integrated Inverters Through Bayesian Neural Networks

Probabilistic Modeling and Uncertainty Quantification of Har...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Rao, Srinivasa Awasthy, Neeta Asha, V. Nijhawan, Ginni Singh, Gurmeet Albawi, Ali Annapoorna, E. Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gl Bajaj Group of Institutions Department of Computer Science and Engineering U.P. Mathura India New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Department of Aimle Griet Telangana Hyderabad India

ISBN: (纸本)9798331527495

Harmonic distortion in grid connected inverters Thus, impacts on power quality and system stability are high, and more especially for systems that use renewable energy sources. This research presents a new stochastic predictive modelling approach based on Bayesian Neural Networks for forecasting of harmonic distortions with prediction uncertainty. The proposed methodology uses data from the National Renewable Energy Laboratory (NREL) database that consists of detailed grid voltage, current under various REMs. Harmonics components derived via sophisticated signal processing algorithms then yields the desired data base for training and testing. The uncertainty of grid conditions can be modelled by means of probabilities due to the utilization of BNNs for improving the reliability of predictions. The combination of stochastic modelling and harmonic analysis in this work creates a complex approach to enhancing grid stability and power quality. As applied to renewable energy challenges, the common method enhances the proposed approach as a scalable and adaptive solution for the mitigation of harmonic impacts for future power systems. © 2025 IEEE.

关键词： Stochastic models

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Emotion Driven Generative Systems for Enhancing Creative Expression Through Technology

Emotion Driven Generative Systems for Enhancing Creative Exp...

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2025 international conference on Intelligent control, computing and Communications, IC3 2025

作者： Chandrashekar, Rakesh Nagpal, Amandeep Sengar, R.S. Albawi, Ali Sravanthi, Jajimoggala Sirisha, N. Agrawal, Komal New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara India Gl Bajaj Group of Institutions Department of Electrical & Electronics Engineering Mathura India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Galgotias university Department Of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India

ISBN: (纸本)9798331527495

EmoVisArt drastically changes how artists express themselves and employ new tools. This innovative technique creates a dynamic creative experience using powerful algorithms like Emotion Recognition (ER), Generative Art Synthesis (GAS), and Interactive Art Adaptation (IAA). EmoVisArt outperformed conventional approaches in several key areas in our study. EmoVisArt promoted creativity, which is crucial to artistic expression. This method is unique since it can adapt its art to the audience's mood. Machine learning enabled gorgeous and emotionally compelling art. EmoVisArt, a blend of art and technology, displayed innovative innovations. The ER software understood facial expressions well owing to convolutional neural networks. This let the system detect and respond to user emotions. This approach is more sophisticated than prior ones. The IAA algorithm, which enables viewers update their entries in real time, makes EmoVisArt notable for interactivity. By connecting art and users dynamically, this interactive aspect improved the art experience. Traditional techniques struggle for response and engagement. EmoVisArt affected people's emotions due to emotion recognition. By adapting to audience emotions, EmoVisArt creates a personal connection. EmoVisArt's deep grasp of emotions let it stand out and show its potential to alter creativity. © 2025 IEEE.

关键词： Convolutional neural networks

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