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2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Sarathkumar, D. Anil, A.R. Sivadasan, J. Jayakumar, T. Manivel, Murugesan Anandhakumar, C. Engineering Kongu Engineering College Department of Electrical and Electronics Tamil Nadu 638 060 India Sree Buddha College of Engineering Pattoor Department of Computer Science &engineering Kerala Alappuzha India Psr Engineering College Department of Electronics and Communication Engineering Tamil Nadu Sivakasi626140 India Nandha Engineering College Department of Electrical and Electronics Engineering Tamil Nadu 638052 India Karpagam Institute of Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Sri Ramakrishna Institute of Technology Department of Electrical and Electronic Engineering Tamil Nadu Coimbatore641010 India

ISBN: (纸本)9798331529833

Industrial automation has become a cornerstone of modern manufacturing, enhancing efficiency, reliability, and scalability. The integration of intelligent control algorithms, such as fuzzy logic, neural networks, genetic algorithms, and model predictive control, has revolutionized traditional automation systems, enabling more adaptive, efficient, and robust operations. This paper explores the application and impact of these intelligent control techniques in industrial settings. It highlights how machine learning algorithms can predict equipment failures, optimize processes, and enhance decision-making in real-time. Case studies are presented to illustrate the benefits and challenges of deploying these algorithms across various automated systems, such as robotic assembly lines, process control in chemical plants, and smart warehousing. Furthermore, the paper discusses the integration of these algorithms with industrial IoT (IIoT) platforms, facilitating seamless communication and data exchange between machines and control systems. The research also addresses the challenges of implementing intelligent control, such as computational complexity, security concerns, and the need for real-time processing. Finally, this study evaluates future trends, emphasizing the potential of combining artificial intelligence and deep learning techniques with industrial automation for a more adaptive and autonomous manufacturing ecosystem. © 2025 ieee.

关键词： Genetic algorithms

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Enhancing Emoji Identification in Real-World Emotion Recognition Systems using Facial Masking Techniques 1

Enhancing Emoji Identification in Real-World Emotion Recogni...

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1st international conference on intelligent Systems and Computational Networks, ICISCN 2025

作者： Puthran, Raksha Mugeraya, Supriya Shetty, Anusha Prashanth Department of Master of Computer Applications Bangalore India

ISBN: (纸本)9798331529246

Emojiify is a cutting-edge real-time image processing system powered by Convolutional Neural Networks (CNNs) for face emotion recognition. This unique technology fosters human-computer interaction by appropriately recognizing emotions in live facial images and instantly overlaying corresponding emojis, satisfying the rising need for engaging and intuitive emoji-based digital communication. The system classifies seven emotions - happiness, winking, pouting, sad, angry, shock, and neutrality - ensuring a smooth and interactive user experience. Trained on a selected dataset of 2,100 photographs, emojiify achieves an incredible 97% accuracy, exhibiting its capacity to capture nuanced emotional expressions. Its good performance across many demographic categories makes it vital for applications in interactive media, targeted content delivery, and user engagement. By integrating state-of-the-art machine learning with real-time picture processing, this research increases digital emotion expression, unlocking potential applications in entertainment, security, and smart systems. This work redefines how technology bridges human emotions and digital interactions. © 2025 ieee.

关键词： Human computer interaction

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Design and Simulation of an intelligent Traffic Management System Using Vehicular Networks 3

Design and Simulation of an Intelligent Traffic Management S...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Tapan Datta, P. Lalith Kumar, D. Tanoj, S. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In this study, the reasearch is focused on the simulation of vehicular communication networks, especially intelligent traffic systems, to enhance dependability, safety, and optimality in communication. The simulation is performed using the OMNeT++ simulator and is integrated with the Veins framework that uses the ieee 802.11p protocol for V2V and V2I communications. Importing real-world traffic scenarios into the framework is obtained via the use of SUMO through TraCIScenarioManager. Other features of the framework include the definition of mobility models, dynamic obstacles, and detailed radio propagation modeling. TraCIDemo11p is used for mobility and communication by both RSUs and vehicles with applications in beaconing and channel switching. The whole framework analyzes smart traffic management solutions, providing as much insight into their performance based upon the communication reliability and overall efficiency in vehicular networks. © 2025 ieee.

关键词： Highway administration

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review 3

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkatesan, Iyyappan Dharanish, M.B. Muthumari Lakshmi, S. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported. © 2025 ieee.

关键词： Weather forecasting

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Causal fMRI-Mamba: Causal State Space Model for Neural Decoding and Brain Task States Recognition

Causal fMRI-Mamba: Causal State Space Model for Neural Decod...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Deng, Weihao Han, Fei Ling, Qinghua Liu, Qing Han, Henry School of Computer Science and Communication Engineering JiangSu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China School of Computer Jiangsu University of Science and Technology China School of Electronic and Information Engineering West Anhui University China School of Engineering and Computer Science Baylor University United States

ISBN: (纸本)9798350368741

Deep learning advances neural decoding in functional magnetic resonance imaging (fMRI) tasks with convolution and attention-based methods. However, these methods struggle with capturing global spatiotemporal information due to high dimensionality, noise and inter-individual difference of fMRI, which also increase computational complexity and prior bias. To this end, a novel causal state space model, Causal fMRI-Mamba, is proposed for neural decoding and task state mapping. It effectively captures global spatiotemporal information via eliminating local redundancies and capturing long-distance dependencies. Meanwhile, a causal representation framework is designed to extract invariant high-order features and disentangle related causal features, enhancing model performance. Furthermore, a dense connection module is expanded to prevent significant causal information loss in hidden states of inter layers. On the HCP brain task state classification task, Causal fMRI-Mamba achieves better performance and generalization than comparison methods. © 2025 ieee.

关键词： causal representation learning Neuro-decoding state space model task-based fMRI

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intelligent Power Consumption Measurement and Control System Based on C/S and Euclidean Distance 5

Intelligent Power Consumption Measurement and Control System...

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5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Xiao, Yuanzheng Hong, Huawei Chen, Feifei Qian, Xiaorui Xu, Ming Feng, Huang Fuzhou Fujian350013 China State Grid Info-telecom Great Power Science and Technology Co. Ltd. Fuzhou350003 China

ISBN: (纸本)9798331533694

With the rapid development of smart grids, intelligent power measurement and control systems have become a key technology for achieving efficient energy management and energy conservation and emission reduction. Traditional measurement and control systems often have problems such as low data processing efficiency and poor real-time performance, which makes it difficult to meet the needs of modern power grids for precise control and intelligent management. In response to the above problems, this paper aims to explore an intelligent electricity measurement and control system based on C/S (Client/Server) architecture and Euclidean distance algorithm to improve data processing efficiency and system real-time performance. This paper elaborates on the application of C/S algorithm and Euclidean distance in intelligent power measurement and control system, including system architecture design, data processing flow and algorithm implementation. Then, through system performance testing and experimental results analysis, the effectiveness and reliability of the proposed system are verified. Finally, the research content, experimental results and shortcomings of this paper are summarized, and directions for future improvement are proposed. The experimental results show that the intelligent electricity measurement and control system based on C/S and Euclidean distance can significantly improve data processing efficiency and system real-time performance. The highest speed of data processing in the system is 200, reducing energy consumption and operating costs, providing strong support for the intelligent management of smart grids. © 2025 ieee.

关键词： C/S Algorithm Euclidean Distance intelligent Power Consumption Measurement and Control System

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Active Sensing for Multiuser Beam Tracking With Reconfigurable intelligent Surface

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ieee TRANSACTIONS ON WIRELESS communicationS 2025年第1期24卷 540-554页

作者： Han, Han Jiang, Tao Yu, Wei Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Marvell Semicond Canada Inc Toronto ON M5H 1J9 Canada Qualcomm Technol Inc Santa Clara CA 95051 USA

This paper studies a beam tracking problem in which an access point (AP), in collaboration with a reconfigurable intelligent surface (RIS), dynamically adjusts its downlink beamformers and the reflection pattern at the RIS in order to maintain reliable communications with multiple mobile user equipments (UEs). Specifically, the mobile UEs send uplink pilots to the AP periodically during the channel sensing intervals, the AP then adaptively configures the beamformers and the RIS reflection coefficients for subsequent data transmission based on the received pilots. This is an active sensing problem, because channel sensing involves configuring the RIS coefficients during the pilot stage and the optimal sensing strategy should exploit the trajectory of channel state information (CSI) from previously received pilots. Analytical solution to such an active sensing problem is very challenging. In this paper, we propose a deep learning framework utilizing a recurrent neural network (RNN) to automatically summarize the time-varying CSI obtained from the periodically received pilots into state vectors. These state vectors are then mapped to the AP beamformers and RIS reflection coefficients for subsequent downlink data transmissions, as well as the RIS reflection coefficients for the next round of uplink channel sensing. The mappings from the state vectors to the downlink beamformers and the RIS reflection coefficients for both channel sensing and downlink data transmission are performed using graph neural networks (GNNs) to account for the interference among the UEs. Simulations demonstrate significant and interpretable performance improvement of the proposed approach over the existing data-driven methods with nonadaptive channel sensing schemes.

关键词： Sensors Reconfigurable intelligent surfaces Reflection coefficient Vectors Downlink Correlation Data communication Adaptation models Reflection Long short term memory Active sensing beam tracking deep learning reconfigurable intelligent surface graph neural network recurrent neural network long short-term memory (LSTM)

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 ieee.

关键词： Natural language processing systems

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Deep Learning-Driven Computational Intelligence for Leaf Disease Identification for Smart Agriculture 3

Deep Learning-Driven Computational Intelligence for Leaf Dis...

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3rd international conference on intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Phani Prasad, J. Jadala, Vijaya Chandra Department of CS&AI School of Computer Science and Artificial Intelligence SR University Warangal506371 India

ISBN: (纸本)9798331527549

In the context of smart farming, Artificial Intelligence (AI) plays a crucial role in enhancing agricultural practices, from soil management to crop yield optimization. By leveraging deep learning and image processing techniques, AI can address various challenges across different agricultural sectors, particularly in detecting and mitigating plant diseases. This paper focuses on the use of these technologies for leaf disease detection to minimize crop loss. Deep learning and image processing are key components in identifying, classifying, and monitoring plant diseases. The study reviews various types of leaf diseases and examines the effectiveness of existing algorithms for detection. To improve performance, an algorithm based on ensemble learning methods is proposed. Experimental investigation is conducted on tomato leaf diseases, followed by a comparative analysis of the results. The proposed AI-driven system enables early disease detection, facilitating timely monitoring and intervention, ultimately reducing crop loss and improving overall crop health through proactive management and precautionary measures. © 2025 ieee.

关键词： Contrastive Learning

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intelligent Hiring Software- Job Recommendation System and Profile Builder 1

Intelligent Hiring Software- Job Recommendation System and P...

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1st international conference on Advances in computer Science, Electrical, Electronics, and communication Technologies, CE2CT 2025

作者： Kumar, Shubham Rajput, Nikhil Mishra, Varsha Aggarwal, Gunjan Sharda University Greater Noida India

ISBN: (纸本)9798331518578

In today's world, where there are multiple career options, still there is a difficulty in finding the securing and a good job that perfectly matches an individual's skill due to the absence of a very personalized recommendation systems. Additionally, the lack of proper guidance in making resumes, it is very much harder to find the best fit opportunity and get shortlisted for a company's hiring process. To alleviate those times consuming task, we develop and implement a job recommendation system for online job searching. This paper proposes a new method for overcoming these challenges by the development of intelligent Hiring Software- job recommendation system and profile builder. By using the Naive Bayes our platform categorizes users according to their stated skills and profiles in their resumes and Hybrid approach of graph- based model enables job the recommendations. Natural Language processing used for betterment of the resumes or profile buildup. Our Platform uses the NLP for profile improvement through the suggestions based on the previous profile and job description. Your career profile can be greatly improved using NLP. © 2025 ieee.

关键词： Apprentices

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