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Energy-Efficient Autism Spectrum Disorder data Collection Mechanism using Wireless Sensor Networks and Internet-of-Things 5

Energy-Efficient Autism Spectrum Disorder Data Collection Me...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Vijaya Shetty, S. Srujana, S.N. Naidu, P. Ramesh Neeli, Jyoti Sarojadevi, H. Prakash, Ramya Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350356236

A patient's cognitive health may be better assessed using smart monitoring and assisted living technologies. Social skills, repetitive habits, verbal and nonverbal communication, and adjusting to new environments are among the challenges children on the autism spectrum face. Because of their reduced cognitive capacities, children with autism spectrum disorder (ASD) present a significant public health challenge because it is difficult to comprehend their feelings. The main purpose of this research is to assist autistic newborns in adapting to their environment via Internet-of-things (IoT) technologies and wireless sensor networks. The study indicates an Internet of Things (IoT)-WSNs system for data collection, using an energy-efficient cluster-based routing optimization (EECRO) method that sends data to an IoT gateway server as quickly as possible, allowing the guardian to access the data in real-time. The simulation outcome indicates the EECRO can significantly reduce energy consumption to deliver messages from autistic children to their guardians. © 2025 IEEE.

关键词： Assisted living

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Design and Implementation of a data Encryption Framework Simulink Model for Protecting Sensitive Organizational data 1

Design and Implementation of a Data Encryption Framework Sim...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Patchigolla, Veera Lakshmayya Clarksburg MD United States

ISBN: (纸本)9798331518578

Internet-of-Medical-Things (IoMT) based sensitive data protection has become a critical problem lately. IoMT-sensitive data are used to connect patients with adults. Since every record, data, and healthcare signal is transmitted over these systems, they need strong and safe access methods for individuals to transmit their essential statistics or documents. Because of this, breaching these systems could have negative impacts on patients. An emerging pattern in biometric verification is to prevent using authentic biometrics for security purposes. Cancelable digital fingerprints can be created using a data encryption framework or non-invertible transformations. This study presents an integrated structure for cancelable ECG signal detection that is suitable for use in IoMT networking access steps. The suggested system uses fuzzy reasoning to perform a non-invertible transition on ECG data, altering its shifting spectrum. Cancellable biometric platforms aim to retrieve original ECG data from processing variants, but the procedure is non-invertible and hinders this goal. Next, customized trends are used to build compact encryption using XOR. This eliminates the complication of comprehensive encryption systems requiring significant computing resources. By combining encryption techniques and non-invertible transformations, the encryption phase improves the safety of cancelable biometrics features and permits the mixed nature of the suggested cancelable biometrics architecture. For the actual adoption of the suggested ECG-based cancelable biometrics identification structure, hardware adaptation is also shown. According to empirical findings, the suggested structure performs well, with an Equivalent Error Rate (EER) of 0.059%. © 2025 IEEE.

关键词： Electrocardiography

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Multi AI Agents based Black Box Audio Transcriber and Aviation Incident Report Generator 5

Multi AI Agents based Black Box Audio Transcriber and Aviati...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Vagale, Ghanashyam Khan, Mohammed Naif Goutam, Rajat Nikhil, Devarakonda Kumar, Matur Rohith Narula, Tarun Khoury College of Computer Sciences Northeastern University Boston United States Ramaiah University Department of Computer Science Bangalore India Centre for Artificial Intelligence National Institute of Technology Jalandhar India National Institute of Technology Department of Computer Science Jalandhar India

ISBN: (纸本)9798350356236

This paper introduces a framework that automates the transcription of black box audio data and the generation of aviation incident reports using AI agents. Traditionally, manual transcription of cockpit recordings is time-consuming and errorprone. The proposed system employs Groq-Whisper for audio transcription and various AI agents for report generation, streamlining the analysis of incident data. Each agent focuses on specific tasks, such as refining transcriptions and structuring conversations, leading to the identification of critical events and causal factors in the reports. This approach enhances accuracy and efficiency in aviation safety investigations, providing a reliable tool for real-time decision-making. The paper highlights the significance of AI integration to improve efficiency while ensuring explainability and trust in aviation contexts, ultimately contributing to enhanced air travel safety. © 2025 IEEE.

关键词： Speech analysis

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A Novel Approach To Brain Tumor Detection using AI ML, DL, with Synthetic data Augmentation 5

A Novel Approach To Brain Tumor Detection using AI ML, DL, w...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Appavu, Narenthirakumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350356236

Effective treatment and better patient outcomes depend on early brain tumor identification. Even if they are trustworthy, traditional diagnostic techniques frequently need manual analysis, which can be laborious and arbitrary. In order to improve brain tumor diagnosis from MRI scans, this work introduces a unique method that combines Support Vector Machine (SVM) clustering, Statistical Gray Level Co-occurrence Matrix (LBP) features, ResNet50, and synthetic data augmentation. SVM clustering overcomes the drawbacks of linear clustering techniques by accurately segmenting tumor areas. Texture-based characteristics like homogeneity and contrast are extracted using LBP, offering vital information for categorization. By utilizing its deep learning architecture, the ResNet50 model attains great accuracy after being adjusted based on the retrieved characteristics. By mitigating data scarcity, synthetic data augmentation ensures the resilience of the model. Improved accuracy, sensitivity, and specificity are demonstrated by experimental findings on publicly available datasets, indicating the efficacy of this strategy in providing scalable, dependable solutions for automated brain tumor diagnosis. © 2025 IEEE.

关键词： Deep learning

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Quantum frontiers in high energy physics

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 53-75页

作者： Yaquan Fang Christina Gao Ying-Ying Li Jing Shu Yusheng Wu Hongxi Xing Bin Xu Lailin Xu Chen Zhou Institute of High Energy Physics Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Physics Southern University of Science and Technology Interdisciplinary Center for Theoretical Study University of Science and Technology of China Peng Huanwu Center for Fundamental Theory State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Center for High Energy Physics Peking University Beijing Laser Acceleration Innovation Center State Key Laboratory of Particle Detection and Electronics Department of Modern Physics University of Science and Technology of China Key Laboratory of Atomic and Subatomic Structure and Quantum Control (MOE) Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum MatterSouth China Normal University Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear ScienceSouthern Nuclear Science Computing Center South China Normal University Southern Center for Nuclear-Science Theory Institute of Modern Physics Chinese Academy of Sciences

Numerous challenges persist in high energy physics(HEP), the addressing of which requires advancements in detection technology, computational methods, data analysis frameworks, and phenomenological designs. We provide a concise yet comprehensive overview of recent progress across these areas, in line with advances in quantum technology. We will discuss the potential of quantum devices in detecting subtle effects indicative of new physics BSM, the transformative role of quantum algorithms and large-scale quantum computers in studying real-time non-perturbative dynamics in the early universe and at colliders, as well as in analyzing complex HEP data. Additionally, we emphasize the importance of integrating quantum properties into HEP experiments to test quantum mechanics at unprecedented high-energy scales and search for hints of new physics. Looking ahead, the continued integration of resources to fully harness these evolving technologies will enhance our efforts to deepen our understanding of the fundamental laws of nature.

关键词： high energy physics quantum sensing quantum simulations quantum machine learning quantum information

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters 1

Cloud Based Automated Control System Workshops and Rooms for...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Kumar, Dinesh Kuntal, Ravinder Singh Deep, Pragya Chamoli, Anup Shekhar Singh, Pinki Mandal, Rishabh SRM Institute of Science & Technology Department of Computer Science and Engineering UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Mathematics Karnataka Bangalore India IIMT College of Engineering Department of AI & DS Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun248001 India Accurate College of Engineering & Management Department of Computer Science and Engineering Greator Noida India Tula's Institute Department of Computer Science and Engineering UK Dehradun India

ISBN: (纸本)9798331518578

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing. © 2025 IEEE.

关键词： Smart manufacturing

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6G Mobile Network Latency Mobile Edge computing Cloud Architecture 1

6G Mobile Network Latency Mobile Edge Computing Cloud Archit...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Naval, Preeti Pushparajesh, V. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Ramnagar District Karnataka India

ISBN: (纸本)9798331518578

Communication refers to the process by which one individual conveys information to another. Telecommunication and the instruments are made available by technological gadgets, which is now feasible. 5G mobile network standards are being used at present. It is thought that 6th-generation mobile network standards will address several user needs. 6G will be universally available by 2030. In a cloud computing setup, a specific hardware or geographic location is not required. The main aim of this method is to offer computing resources whenever they are needed. By fusing the two, mobile cloud computing enables more flexible and personalized solutions. Machine learning is finding its way into a wide variety of devices. With the use of AI., mobile network services (MNS) may improve the reliability and personalization of user experiences in areas like fraud detection, mobile transaction security, network operation monitoring, and management. A world without traffic accidents, high-tec, specialized healthcare, and criminal activity would be a reality if 6G mobile network services integrated cloud computing with artificial intelligence. The most important requirements for 6G standards are high data rate connection per user and the ability to manage huge record numbers. Research and development are underway for the next-generation mobile network. There are several intriguing aspects of this age. The most pressing problem that requires solving using suitable forensic methods is security. High-performance computers are needed to improve customer service. Two situations are created by combining technological, organizational, and cloud-hosted application-based research approaches in a high-performance computing setting: monitoring performance via remote desktop connection and real-time stream processing. This article seeks to narrow the designated worldwide spectators with a wide variety of empirical chances to allocate resources for trustworthy and defensible on-demand services. ©

关键词： Mobile edge computing

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Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability 1

Using Recurrent Neural Networks to Forecast Climate Change: ...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Rathore, Nitasha Anubha Singh, Kuljeet Kaur, Gaganpreet Sharma, Chetna Sinha, Anurag Bharatiya Vidyapeeth College of Engineering Department of Computer Science and Engineering New Delhi India KIET Group of Institutions India School of Sciences Christ University Department of Computer Science Delhi NCR India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India IGNOU Department of Computer science New Delhi India

ISBN: (纸本)9798331518578

Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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A Survey of Federated Learning Orchestration Using Kubeflow: Challenges, advances, and Future Directions 1

A Survey of Federated Learning Orchestration Using Kubeflow:...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Josyula, Prashanth Ulaganathan, Sethuraman Arava, Sai Kumar Salesforce San Francisco United States Adobe San Francisco United States San Francisco United States

ISBN: (纸本)9798331518578

In distributed machine learning, Federated Learning (FL) has become a game-changing paradigm that allows for cooperative model training while protecting data privacy and taking regulatory compliance into account and with a focus on edge computing integration and privacy protection techniques. This paper offers a systematic assessment of federated learning orchestration using Kubeflow, looking at architectural approaches and implementation issues in Kubernetes-native contexts and includes thorough technical evaluation and code-driven solutions, identifies a number of crucial implementation challenges for Federated Learning processes, such as edge gateway service design, dynamic device management, and adaptive privacy methods. We explore exciting research avenues like context-aware federation protocols, adaptive privacy mechanisms, and dynamic privacy preservation. We also point out important research gaps in existing implementations, particularly with regard to standardized performance metrics, sophisticated fault tolerance mechanisms, and machine learning-optimized orchestration strategies. For researchers and practitioners working on Kubeflow-based federated learning deployments, this paper offers conceptual examples for managing heterogeneous device capabilities, secure model exchange, and adaptive training configurations in Kubeflow-based FL systems highlighting important areas that need further research for enterprise-grade implementations. © 2025 IEEE.

关键词： Federated learning

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Assessing the Accuracy of Regression and Classification Models in Air Quality Prediction 5

Assessing the Accuracy of Regression and Classification Mode...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Vishwakarma, Harsh Kumar Guru Prasad, M.S. Mittal, Vanshika Mittal, Mudit Naik, PavanKumar Chandrappa, S. Dept. of Computer Science and Engg. Dehradun India School of Computing and Information Technology Reva University Bangalore India Dept. of Computer Science and Engg. Bengaluru India

ISBN: (纸本)9798350356236

This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive approach to data analysis, visualization, and preprocessing. The study aimed to predict the Air Quality Index related to key pollutants such as SO2, NO2, RSPM, and SPM. We utilized linear regression, decision tree regression, and random forest regression for prediction tasks and logistic regression, decision tree classification, random forest classification, and k-nearest neighbors for classification tasks. The proposed method was evaluated using different performance matrixes for regression and classification tasks, respectively. The results demonstrated the usefulness of the proposed models in predicting and classifying air quality levels. The proposed model provides a robust framework for air quality management and can be a valuable tool for environmental monitoring and policymaking. © 2025 IEEE.

关键词： Random forests

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