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Topological Quantum computing for Engineers 4th

Topological Quantum Computing for Engineers

引用

4th international conference on Artificial Intelligence and Knowledge Processing

作者： Ganguly, Srinjoy Devendrababu, Shalini Mustafa, Hasan Jain, Prateek Ayala Bertel, Luis Gerardo Fractal Analyt QuantumAI Lab Gurugram India Univ Cartegena Cartegena Colombia

ISBN: (纸本)9783031734762;9783031734779

This didactic paper explores topological superconductivity, an essential idea for enhancing topological quantum computing by utilizing Majorana Fermions (Majorana Zero Modes). Topological quantum computing can enable fault-tolerant qubits, which are essential for implementing quantum AI algorithms and related techniques. This paper discusses the basic principles of topological superconductors, their experimental implementations, and the crucial importance of Majorana Fermions in facilitating error-resistant quantum computing. The paper also examines the integration of quantum AI, emphasizing how the stability of topological qubits enhances the performance of quantum machine learning algorithms. It discusses the obstacles and future possibilities of using topological states for quantum technologies, focusing on engineering applications and recent technical advancements. The study highlights the potential of topological superconductivity to transform quantum computing by providing insight into the creation of reliable quantum systems and scalable quantum circuits.

关键词： Topological Superconductivity Quantum computing Majorana Fermions Kitaev Chain

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Optimizing Resource Costs With Machine learning Techniques 2

Optimizing Resource Costs With Machine Learning Techniques

引用

2nd international conference on Machine learning and Autonomous Systems, ICMLAS 2025

作者： Balamanigandan, R. Mahaveerakannan, R. Saraswathi, S. Jenifer, A. Mary Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

The number of SMEs opting for cloud computing rises, efficient utilisation of the cloud has become a crucial issue because of the rental nature of the resources. Current approaches tend to be more traditional, targeting only individual components such as the amount of computing capacity, which may not adequately capture or mitigate the factors that define most actual cloud workloads and their unpredictable behaviour. In light of this, this article presents a new strategy that applies anomaly detection learning algorithms and particle swarm optimization to achieve the right cloud resource configuration at the least cost possible. The suggested idea works independent of a specific context in a loop fashion and distinguishes exceptional cases through an anomaly detection strategy in the loop process and at the same time learns the usage patterns. More importantly, it adapts to changes in system utilisation, as well as the pricing models offered by various cloud providers. Realistic costs as well as system data using the Microsoft Azure proved significant with highlighting 85% costs shaving within 10 months. This research supports the effectiveness of our integrated solution for solving multifaceted issues related to resource management in cloud environments for SMEs. © 2025 IEEE.

关键词： Resource allocation

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Vibration-Based Failure Prediction in Machinery: Novel Application of Time Series Decomposition and Neural Networks 5

Vibration-Based Failure Prediction in Machinery: Novel Appli...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Nirmal, W.C. Lakmal, H.K.I.S. Fernando, K.J.P. Nsbm Green University Faculty of Engineering Department of Mechatronic & Industrial Engineering Homagama Sri Lanka Nsbm Green University Faculty of Engineering Department of Electrical Electronic & Systems Engineering Homagama Sri Lanka

ISBN: (纸本)9798331530983

Predictive maintenance (PdM) is critical in reducing downtime and minimizing operational costs in industrial settings. This study introduces a novel framework for vibration-based failure prediction by combining Seasonal and Trend Decomposition using LOESS (STL) and deep learning models, specifically Long Short-Term Memory (LSTM) networks. The framework processes multivariate vibration data collected from two-roller mills in a tire manufacturing facility. STL is employed to decompose the data into trend, seasonal, and residual components, enabling noise reduction and enhanced feature extraction. These components serve as inputs to LSTM models for predicting machine condition scores. A comprehensive analysis compared five deep learning architectures: LSTM, Bidirectional LSTM, Gated Recurrent Unit (GRU), Deeper LSTM, and Conv1D LSTM. The GRU and Conv1D LSTM demonstrated superior performance across multiple vibration axes, achieving low errors and high R2 values. Results highlight the effectiveness of STL in preparing vibration data for sequential learning and the robust predictive capabilities of deep learning models. This study provides a scalable approach to PdM, enhancing reliability and efficiency in industrial machinery maintenance. Future work includes extending the framework to other machinery types and real-time monitoring systems. © 2025 IEEE.

关键词： Rollers (machine components)

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Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems

Decentralized Online Ensembles of Gaussian Processes for Mul...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Llorente, Fernando Waxman, Daniel Djurić, Petar M. Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY United States

ISBN: (纸本)9798350368741

Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed setting, Bayesian solutions are much more limited. We introduce a fully decentralized, asymptotically exact solution to computing the random feature approximation of Gaussian processes. We further address the choice of hyperparameters by introducing an ensembling scheme for Bayesian multiple kernel learning based on online Bayesian model averaging. The resulting algorithm is tested against Bayesian and frequentist methods on simulated and real-world datasets. © 2025 IEEE.

关键词： distributed learning Gaussian processes multi-agent systems

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CropSmart-A Crop Prediction Application Using Machine learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

引用

12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Detection of Malicious Interpreted Code using ML to Optimize Parameters for Minimizing Prediction Errors

Detection of Malicious Interpreted Code using ML to Optimize...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Dhawan, Manoj Kuril, Devendra Panadiwal, Himanshu Rawat, Hitesh Rawat, Anjali Rawat, Romil School of Engineering Avantika University Ujjain India Shri Vaishnav Vidyapeeth Vishwavidyalaya Department of Information Technology Indore India University of Extremadura Department of Business Management and Economics Badajoz Spain University of Extremadura Department of Computer and Communication Technology Badajoz Spain LabGeoInf-Research LABoratory in GEOmatics and INFormation systems Rome Italy

ISBN: (纸本)9798331538538

Mobile devices face growing cybersecurity threats, including malware, data breaches, malicious applications, and social engineering attacks. Cybercriminals exploit these vulnerabilities to gain unauthorized access, disrupt communication, and compromise sensitive information. Conventional malware detection methods, such as signature-based and heuristic techniques, often struggle to identify evolving threats due to their reliance on predefined patterns. This study presents an advanced malware detection framework that integrates a Reservoir Network with a Deep learning Approach (DLA) to enhance detection accuracy and minimize prediction errors. The proposed method utilizes two static features-API call permissions and alert signals- to improve detection efficiency. Experimental evaluation, conducted using the Drebin malware dataset, demonstrates that the proposed model achieves an accuracy of 99.7 %, reduces the false positive rate to below 1 %, and ensures optimal processing speed with a batch size of 64 and a learning rate of 0.001. Compared to traditional approaches, this framework exhibits superior adaptability to obfuscated malware while enabling real-time threat detection. The findings emphasize the effectiveness of machine learning (ML)-driven security mechanisms in addressing emerging cybersecurity threats. © 2025 IEEE.

关键词： Computer viruses

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Implementing Resource-Aware Scheduling Algorithm for Improving Cost Optimization in Cloud computing 4

Implementing Resource-Aware Scheduling Algorithm for Improvi...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Reddy, Kiran Jagannadha Udayaraju, Pamula Pamaiahgari, Venkata Pradeep Reddy Kumar, V. Dilip Walmart BentonvilleAR United States Srm University Department of Cse AP India Wendys International Llc OH United States SRKR Engineering College Department of CSE India

ISBN: (纸本)9798331523923

An uncountable number of computational resources are shared for various applications using a transformative technology called Cloud computing. It is an emerging technology that can offer scalable and on-demand resources. However, cost optimization and resource allocation are critical issues due to the increasing number of cloud user and their request. It can be solved by optimising resource allocation by reducing the task/user queue. It is possible only if the requested resource is free;otherwise, it must schedule the same or relevant resource. This paper demonstrates a Resource-Aware Scheduling (RAS) algorithm to increase the speed of resource allocation by mapping the user request with the resource availability. The proposed algorithm examines the log information of the resources, workload behaviour, resource availability, price, task efficiency, and reduced wastage and allocates the resources dynamically. To do that, it maps user information with resource information and schedules based on availability and priority. The simulation-based experiment is carried out in a private cloud space and demonstrates the resource allocation, utilization, response time, and cost. It is also compared with the traditional scheduling algorithm to evaluate the performance of the RAS. The evaluation found that the RAS model is more suitable for cloud resource allocation. © 2025 IEEE.

关键词： Cloud computing Cost Optimization Resource Allocation Resource-Aware Scheduling Scheduling

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Approaches to Research-Based teaching and learning 27th

Approaches to Research-Based Teaching and Learning

引用

27th international conference on Interactive Collaborative learning, ICL 2024

作者： Restivo, Maria Teresa Zvacek, Susan M. LAETA University of Porto Porto3ES Portugal *** London United States

ISBN: (纸本)9783031835223

This article introduces the idea of research-based learning approaches and provides examples of such initiatives developed and implemented at the University of Porto (UPorto) and its Faculty of engineering (FEUP). This pedagogical approach, in which students are directly engaged in research, is described and the benefits and challenges of its implementation are discussed. Specific projects that have been undertaken at UPorto and FEUP using this model are also described, including multidisciplinary initiatives and young researcher events. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Emotion Recognition Using EEG Signals in Human Brain Waves and Deep learning Approaches 10

Emotion Recognition Using EEG Signals in Human Brain Waves a...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems. © 2025 IEEE.

关键词： Recurrent neural networks

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