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Design and Optimization of an IR-Based automation System with Telegram Bot Integration for Remote Device Control 4

Design and Optimization of an IR-Based Automation System wit...

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4th international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Adnan Sharar Oyon, Md. Shihab Himel, Abid Hasan Mredul, Khaliduzzaman Ahmed, Abir Ali, M Tanseer Hossainl, Chowdhury Akram Rahman, Md. Abdur Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Dept. of Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

automation is advancing rapidly, with infrared (IR) communication being a key method for controlling electronic devices. Despite this, challenges remain in optimizing IR controllers for consistent performance across varying angles and task loads. A 3D design and a robust algorithm have been developed to enhance the system's accuracy, while security is ensured by matching a unique TOKEN with the Telegram bot for each session. Error rates were analyzed across different angles, with specific zones determined based on performance. Minimal error rates defined the working zone, while higher rates indicated critical and invalid zones. Error rates increased as the transmitter moved further from the optimal angles, such as a 0-6 % error rate when operating within a 0° - 5° range. Reaction time was calculated across multiple tasks, showing an average of 5 seconds per command. Mismatch rates, observed during higher task loads, rose sharply when handling many tasks. For instance, the mismatch rate was 0 % for 12 tasks but 20 % when executing 20 tasks. Performance evaluations demonstrated optimal IR signal strength for reliable control within a distance of 10 to 15 inches, with diminishing effectiveness beyond 20 inches. These findings provide a foundation for developing more reliable and efficient IR control systems for practical applications. © 2025 IEEE.

关键词： Bot (Internet)

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Robotic automation Dynamic Hybrid Neuro-Fuzzy and Deep Learning Framework with GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic and CNN-TCN for Accurate IoMT-Based Chronic Kidney Disease Detection

Robotic Automation Dynamic Hybrid Neuro-Fuzzy and Deep Learn...

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Allur, Naga Sushma Dondapati, Koteswararao Chetlapalli, Himabindu Kodadi, Sharadha Deevi, Durga Praveen Purandhar, N. Astute Solutions Llc CA United States Everest Technologies OH United States 9455868 Canada Inc ON Canada Infosys TX United States O2 Technologies Inc CA United States code - 69 Andhra Pradesh 517325 India

ISBN: (纸本)9798331513894

Background Information: Chronic kidney disease (CKD) is a world health concern that requires an early detection and careful observation over time. The IoMT is offering continuously streaming data for prognostication;however, the traditional approaches fail in capturing temporal interdependencies, spatial representation of attributes, and uncertainty in data. Objectives: The objective is the engineering of a Dynamic Hybrid Neuro-Fuzzy and Deep Learning Architecture by combining GRU-BiLSTM, Capsule Networks, Type-2 Fuzzy Logic, and CNN-TCN for accurate identification of CKD, ensuring scalability, precision, and minimal latency. Methods: The proposed framework uses GRU-BiLSTM for temporal pattern analysis, Capsule Networks for spatial representation, Type-2 Fuzzy Logic for managing uncertainty, and CNN-TCN for extraction of spatial-temporal features. Data preprocessing and robust feature extraction enhance the prediction pipeline using IoMT. Results: The architecture outperforms the state-of-the-art methods, with 96.5% accuracy, 95.7% precision, and 94.9% recall, while being scalable (50 GB) and having minimal latency of 98.5 ms, making it suitable for real-time IoMT applications. Conclusion: This framework provides a strong, extensible, and adaptive solution for CKD prediction, overcoming the challenges of IoMT and improving patient care. Future work will extend this framework to other chronic diseases and secure deployments through distributed learning. © 2025 IEEE.

关键词： Fuzzy inference

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VTAF-Ensemble: A Hybrid Vision Transformer and Attention-based Learning Approach for Enhanced Brain Tumor Detection and Classification 1

VTAF-Ensemble: A Hybrid Vision Transformer and Attention-bas...

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

作者： Fathima, Mohammed Nazneen Singh, Prabhjot Singh, Simrandeep Chandigarh University Department of Electronic and Communication Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798331529246

This work introduces a new methodology, Vision Transformer with Attention-based Feature Mapping and Optimized Ensemble Learning (VTAF-Ensemble), which includes three advanced techniques: Vision Transformer-Based Segmentation (VT-Seg), Attention-Based Feature Mapping (AF-Map) and Optimized Ensemble Learning (OE-Ensemble), to boost the brain tumor detection and classification from Magnetic Resonance Imaging (MRI) images. Traditional practice of identifying tumor is error prone, time consuming, hence it requires automation. This thesis proposes an integrated method, which includes VT Seg for accurate segmentation, AF Map for feature enhancement, and OE Ensemble for Ensemble classification using SVM and CNN classifiers. We show that VTAF-Ensemble achieves 92.1% Intersection over Union (IoU), 94.5% Mean Average Precision (mAP), 93.2% Dice Coefficient (DC) and 95% sensitivity in segmentation. Moreover, the model also yields better results on classification metrics compared to others: Area Under the Curve (AUC) of 95.2% and F1 Score of 92.9% and recall of 93%. Overall, this hybrid approach provides substantial accuracy improvements, scalability and utility for medical image applications as they relate to medical imaging applications, in particular, brain tumor specific diagnostic applications. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Fabrication of Sensorsless Automated Guided Vehicle for Small Warehouse Rack Management

Fabrication of Sensorsless Automated Guided Vehicle for Smal...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sahu, Neelesh Kumar Chaturvedi, Piyush Gupta, Vaishali Patel, Ruchi Verma, Ashok Shrivastava, Megha Gyan Ganga Institute of Technology and Sciences Artificial Intelligence and Robotics MP Jabalpur India IPS Academy Institute of Engineering & Science Department of Computer Sc. & Engg. MP Indore India Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Medicaps University Department of Mathematics MP Indore India

ISBN: (纸本)9798331512965

Automated guided vehicles (AGVs) are an essential part of today's logistics networks because they save time, reduce wear and maintenance expenses, and maximize efficiency in route *** creation of AGVthat is capable of effectively managing warehouse racks without the utilization of sensors is the primary emphasis of this project. In order to travel and carry out duties, the AGVruns on predetermined pathways and makes use of a button-based interface. For the purpose of offering an efficient and costeffective solution for the automation of small warehouses, it is outfitted with a lever mechanism that is operated by a servo motor and features the ability to connect and move racks. © 2025 IEEE.

关键词： Automatic guided vehicles

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A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques 3

A Comprehensive Analysis of Hate Speech Detection and Recogn...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Mannepalli, Praveen Kumar Sharma, Saurabh Rajput, Neha Kalamdhad, Sanjay Chandigarh University Department of Computer Science & Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Mohali India Chandigarh University Department of Computer Science and Engineering Punjab India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science & Business System Nagpur India

ISBN: (纸本)9798331542375

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone. © 2025 IEEE.

关键词： Tweets

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SEMSCS: An SE Attention Multi-Scale Convolution Channel Selection Network for Limited-Channel SSVEP Signal Classification 24

SEMSCS: An SE Attention Multi-Scale Convolution Channel Sele...

引用

2nd international conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Ma, Yunxiao Huang, Jinming Liu, Chuan Lu, Shuangshuang College of Engineering Qufu Normal University Shandong China

ISBN: (纸本)9798400710681

Brain-computer interfaces (BCIs) have witnessed remarkable advancements in medical and rehabilitative fields in recent years. Among various BCI paradigms, steady-state visually evoked potentials (SSVEP) stand out for their robustness and ease of detection within BCI systems. However, the practical deployment of SSVEP-based BCIs faces challenges due to the constraints imposed by wearable EEG devices, particularly in terms of cost-effectiveness and portability, which often preclude the inclusion of the complete set of international standard EEG channels. This study introduces SEMSCS (SE Attention Multi-Scale Convolution Channel Selection), an innovative neural network architecture designed to address these challenges. Built upon the foundational EEGnet framework, SEMSCS incorporates SE channel attention and multi-scale convolution techniques to adapt flexibly to varying electrode distributions and quantities across diverse EEG devices, ensuring comprehensive utilization of available electrode data without discarding non-selected channels. Experimental validation on a standardized dataset involving data from ten channels across forty SSVEP targets demonstrates SEMSCS’s effective performance in SSVEP classification under restricted channel conditions. These findings underscore SEMSCS’s potential as a practical solution for enhancing the applicability of SSVEP-based BCIs in real-world scenarios where wearable EEG devices with limited channel layouts are employed. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Integrative automation of Cybersecurity Systems through Advanced Data Science Methodologies: Enhancing Predictive Analytics and Real-Time Threat Mitigation 4

Integrative Automation of Cybersecurity Systems through Adva...

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4th international conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Das, Subhranil Kumari, Rashmi Singh, Raghwendra Kishore School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of CS & AI SR University Warangal India

ISBN: (纸本)9798331530402

Due to emergence in Corona disease, the digital revolution become more intensified which makes more prevalent in day-to-day activities. As a result, cloud service providers become more frequent. Therefore, system integration is essential throughout a partner ecosystem. Unbelievably many edge devices are sending IoT data to the cloud. The system becomes automated due to the organizational structures which resulted in developing new technologies. This new" Integration ecosystem" is where there is just one platform forall the system. According tothe survey of 4, 500 decisionmakers, data protection-rather than business process optimization or processautomation-was the primary argument for employing AI withinorganizations. Using ML algorithmsto automatically identify cyberattacks is referred to as automating cyber security systems. By leveraging automation, cyber security systems can swiftly identify and respond to attacks, reducing the amount of time needed to avert possible harm. With the considerations in mind, we proposed Automating Cyber Security Systems with Data Science Practices in which we will be talking about how various cyber security systems can effectively utilize current data science practices to automate their workflow. © 2025 IEEE.

关键词： Predictive analytics

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Extended Abstract of Poster: STARS: Tree-Based Classification and Testing of Feature Combinations in the Automated Robotic Domain 18

Extended Abstract of Poster: STARS: Tree-Based Classificatio...

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18th IEEE international conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Schallau, Till Schmid, Dominik Pawlinorz, Nick Naujokat, Stefan Howar, Falk TU Dortmund University Dortmund44227 Germany Fraunhofer Institute for Software and Systems Engineering Dortmund44147 Germany

ISBN: (纸本)9798331534677

Testing complex systems is crucial for ensuring safety, especially in automated driving, where diverse data sources and variable environments pose challenges. Here, robust safety validation is critical but exhaustive n-way combinatorial testing is impractical due to the vast number of test cases. The STARS framework uses tree-based scenario classifiers to limit feature combinations in a given domain. © 2025 IEEE.

关键词： automation

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Comparative Study of Deep and Machine Learning Models in Knee Osteoarthritis Detection 3

Comparative Study of Deep and Machine Learning Models in Kne...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Kapoor, Vaidhi Mahajan, Makul Batra, Salil School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331542375

The knee osteoarthritis (KOA) is a common type of arthritis characterised by recent cartilaginous joint destruction and alteration of bone, ligaments and synovial fluid. This paper presents a brief knowledge about the knee OA, including its cause and effect, assessment, signs, prevalence, and the methods of managing it together with Different algorithms like Decision Tree, Random Forest, Artificial Neural Network (ANN) Naive Bayes and CNN. The dataset was collected from GitHub which was from OAI dataset which consists of csv datasets. The accuracy of the algorithms were 85%, 89%, 88%, 95% and 85%. Out of five algorithms, the best algorithm were Naïve Bayes and Random Forest algorithm. © 2025 IEEE.

关键词： Deep neural networks

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The Research on Angular Momentum Conservation and Resistance in the Balance of an Upright Rod Under computer Simulation

The Research on Angular Momentum Conservation and Resistance...

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2025 international conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Tang, Yanni Huang, Juncheng Zhang, Haoyang Li, Zewang Zhang, Zhenwu Rocket Force University of Engineering Baqiao Shaanxi Xi'an China

ISBN: (纸本)9798331506797

This paper investigates the design and implementation of an intelligent stabilization system for an upright rod based on angular momentum conservation. The system uses an ESP32 controller with FreeRTOS for multi-task scheduling and high-precision sensors, including the MPU6050 and AS5600. An improved LQR controller with Kalman filtering handles the control algorithm. The system achieves wind resistance of up to 10.96 m/s and can restore stability within 3 seconds, with a maximum deflection angle of $1^{\circ}$. Experimental data confirm the system's effectiveness. © 2025 IEEE.

关键词： Cube balancing system Dynamic disturbance rejection Inertia flywheel LQR control Posture stability

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