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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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Enhancing Quantum Machine Learning algorithms for Optimized Financial Portfolio Management

Enhancing Quantum Machine Learning Algorithms for Optimized ...

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IEEE International conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Narinder Kumar Bhasin Sunil Kadyan Kathari Santosh Ramya HP Ravindra Changala B Kiran Bala Amity University Zonal Head Indian Institute of Banking and Finance Delhi India School of Management and Commerce Manav Rachna University Faridabad Haryana India Department of MBA CMR Institute of Technology Bengaluru India Department of Management Studies (MBA) Dayananda Sagar College of Engineering Bangalore India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBM), and Quantum K

关键词： Support vector machines Machine learning algorithms Quantum algorithm Heuristic algorithms signal processing algorithms Machine learning Quantum circuit

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Intelligent Water Body Delineation for Flood Monitoring Using Remote Sensing Data and Deep Learning algorithms

Intelligent Water Body Delineation for Flood Monitoring Usin...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Senthil Kumari Mathana. K Chitra. P Priya Thiagarajan Department of Computer Applications Thiagarajar College of Engineering Madurai India Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Floods pose significant threats to lives, infrastructure, and the environment. Flood monitoring is crucial for effective disaster management and mitigation. Traditional methods of flood monitoring have several limitations including manual data collection, limited coverage, and delayed response times. In recent years, advanced remote sensing technology, coupled with the power of deep learning algorithms have greatly enhanced flood monitoring capabilities. This paper presents a comprehensive investigation into the integration of remote sensing data and deep learning algorithms for water body delineation in flood monitoring. A dataset containing 700 Sentinel-2 satellite images was used. It was divided into training, testing, and validation data, with corresponding ground truth masks. Deep learning models, including UNET, SEGNET, DeepLabv3, and RefineNet, were employed for water body delineation tasks. After thorough assessment, RefineN et emerged as the best performing model. It has exceptional accuracy and precision in outlining water bodies. Transfer learning techniques were explored to further improve the RefineN et model performance. Various backbone architectures, including ResNet50, ResNet101, MobileNetV2, and EfficientN etBO, were investigated for their compatibility with RefineN et. Experimental results revealed that EfficientN etBO served as the optimal backbone architecture, significantly enhancing the model's performance. The RefineNet model with EfficientN etBO backbone achieved remarkable results, with an accuracy of 0.9478, F1-score of 0.9162, IOU of 0.8455, and MAP of 0.8708. These findings underscore the potential of integrating remote sensing data and deep learning algorithms for flood monitoring applications. The findings from this research carry substantial importance for disaster management and emergency response efforts using neural networks.

关键词： Deep learning Training Accuracy Transfer learning signal processing algorithms Floods Time factors Monitoring Remote sensing Testing

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Software Diversification Protection Methods for Binary Programs

Software Diversification Protection Methods for Binary Progr...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Yingchao Chen Junchao Wang Xin Zhou Jianmin Pang Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Software diversification is an effective software protection method against reverse engineering and code reuse attacks, which can provide heterogeneous redundant execution bodies for mimetic defense mechanisms. Most existing software diversification methods require access to the source code, which can provide defenders with more valuable information for devising effective defense strategies. However, due to commercial copyright and the purpose of preventing software piracy, developers often keep their software closed-source, making it difficult to access the source code. Therefore, a method called R2BF (ReCooking and Randomizing Binary File) is proposed, which combines decompilation and diversification compilation techniques to address the difficulty of obtaining the source code. This method involves diversifying the source code through compilation to achieve software diversification for binary executable files. Security and performance testing of diversified C programs has shown that diversified binary executable files can mitigate vulnerabilities resulting from code reuse, validating that this method can enhance the security of binary programs and is feasible for software protection. The diversified binary executable files are nearly identical to the original, non-diversified files in terms of file size and execution time, and may even exhibit acceleration and optimization effects in certain scenarios.

关键词： Software testing Codes Source coding Software protection Software algorithms Reverse engineering signal processing algorithms Software Security Optimization

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Beamspace AIC & MDL Algorithm for Based on Concentric Circular Array Antenna

Beamspace AIC & MDL Algorithm for Based on Concentric Circul...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Heui-Seon Park Suk-seung Hwang Department of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju Korea Interdisciplinary Program in IT-Bio Convergence System School of Electronic Engineering Chosun University Gwangju Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In modern wireless communication systems using multiple antenna elements, accurately identifying the signals contained in the received signal is very important. Conventional signal number estimation algorithms operate for the entire set of antenna elements, resulting in high computational complexity. In order to enhance this high computational complexity problem, this paper proposes beamspace based Akaike Information Criterion (AIC) and Minimum Description Length (MDL) algorithms based on a concentric circular array (CCA) antenna for efficient signal number estimation. In addition, computer simulation results are presented to evaluate the signal-number estimation performance of the proposed algorithm.

关键词： Wireless communication Array signal processing Computer simulation Estimation Computational complexity MIMO communication Antenna arrays

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Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems

Denoising Diffusion Model-Based Channel Estimation in IRS-As...

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International conference on Wireless Communications and signal processing (WCSP)

作者： Lan Yi Jiansong Miao Tao Zhang Zhenhui Yuan Jiawen Kang Dusit Niyato Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China School of Software Engineering Beijing Jiaotong University Beijing China School of Engineering University of Warwick UK School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.

关键词： Wireless communication System performance Noise reduction Channel estimation signal processing algorithms Estimation Artificial neural networks Integrated sensing and communication Prediction algorithms Diffusion models

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Envisioning Educational Success Through advanced Analytics and Intelligent Performance Prediction

Envisioning Educational Success Through Advanced Analytics a...

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International conference on Communications and signal processing

作者： T. Sureshkumar J Charanya T. Kumaresan G Rajeshkumar P Kavin Kumar B Anuj Department of Information technology Nehru institute of technology Coimbatore Department of Computer Science and Engineering Kongu Engineering College (Autonomous) Erode Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Dept of CSE Bannari Amman Institute of Technology Sathyamangalam Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final exam, taking into account a range of socio-economic and academic factors. Every student's journey is different, and it is influenced by various factors such as their socioeconomic background, academic abilities, and personal experiences. To evaluate their performance, we use various machine learning algorithms such as KNN, Naive Bayes, SVM, AdaBoost, and Naive Bayes— and assess their performance using key metrics such as ROC curve, confusion matrix, precision, recall, and F1 score. The F1 score offers a balanced assessment of precision and recall, offering insights into overall model accuracy. Precision and recall focus specifically on the model's ability to correctly identify positive instances and capture all actual positive instances, respectively. ROC curves demonstrate the balance between the rates of correctly identifying true positives and incorrectly identifying false positives, allowing for a nuanced assessment of classifier performance. The confusion matrix further breaks down true positives, true negatives, false positives, and false negatives, enhancing our understanding of algorithmic effectiveness. Through a comprehensive comparison of these classification methods, our goal is to identify the most accurate approach for predicting student outcomes, thus contributing valuable insights to educational institutions and policymakers aiming to implement effective strategies for student success and academic improvement.

关键词： Support vector machines Measurement Machine learning algorithms Navigation Education signal processing algorithms signal processing

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advanced Routing Strategies for LEO and VLEO Constellations: Ensuring Polar Coverage

Advanced Routing Strategies for LEO and VLEO Constellations:...

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advanced Satellite Mobile Systems (ASMS)

作者： Camilla Ottaviani Alessandro Compagnoni Juan A. Fraire Giacomo Verardo Gabriel Maiolini Capez Daniel Gaetano Riviello Gregory Stock Carla Fabiana Chiasserini Roberto Garello Department of Electronics and Telecommunications Politecnico di Torino Turin Italy Inria INSA Lyon CITI Villeurbanne France CONICET Universidad Nacional de Córdoba Córdoba Argentina KTH Royal Institute of Technology Stockholm Sweden CNR-IEIIT Consiglio Nazionale delle Ricerche Turin Italy Saarland University Saarbrücken Germany

ISBN: (数字)9798331522353

ISBN: (纸本)9798331522384

The proliferation of mega-constellations comprising thousands of satellites has amplified the need for efficient routing solutions in space networks to minimize latency and ensure robust global connectivity. This work evaluates the performance of multiple decentralized routing algorithms within Walker-Delta constellations, comparing their effectiveness in Low Earth Orbit (LEO) and Very Low Earth Orbit (VLEO) configurations. Our findings reveal that constellation geometry, such as the number of satellites and orbital planes, shapes routing strategies more than orbital altitude, with VLEO demanding larger fleets to maintain seamless coverage. Furthermore, we propose PolarDisCo, a novel routing algorithm specifically designed to provide coverage of polar regions—a geographical area where, due to unique topology challenges, existing decentralized routing algorithms (e.g., DisCoRoute) exhibit a significant performance drop. Experimental results demonstrate that PolarDisCo reduces end-to-end delay on the polar area by 7.9 ms with respect to DisCoRoute, thus providing a 14.8% improvement. These findings underscore the potential of tailored routing strategies to optimize communication in next-generation satellite networks with LEO and VLEO shells.

关键词： Geometry Space vehicles Satellites Low earth orbit satellites signal processing algorithms Routing Orbits Delays Topology Next generation networking

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Radar range Extended target detection using Prototypical Network

Radar range Extended target detection using Prototypical Net...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Wenhao Ma Mingchen Yuan Xinyang Wang Liang Zhang Yanhua Wang Radar Technology Research Institute Beijing Institute of Technology Beijing China Zhengzhou Research Institute Beijing Institute of Technology Zhengzhou China Xi'an Modern Control Technology Research Institute Xi'an China Electromagnetic Sensing Research Center of CEMEE State Key Laboratory Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded•Real-Time Information: Processing Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In radar ground stationary or slow-moving target detection, the target detection algorithm based on High-Resolution Range Profile (HRRP) is an important technical approach. Traditional target detection algorithms based on HRRP are difficult to apply in target detection tasks under multiple terrains. To this end, this paper proposes a feature detection algorithm based on deep learning for target polarized HRRP. This paper studies an end-to-end target detection algorithm based on prototypical networks. This algorithm utilizes prototypical networks to extract deep features from input HRRP and constructs the feature distribution of clutter. Then, it calculates the distance between the features of the input test sample and the clutter feature center, and compares it with the threshold calculated based on the false alarm probability to complete the detection. Under the backgrounds of bare soil and highway, the algorithm improves by 22% compared to the traditional energy detection algorithm for two types of vehicle targets at a signal-to-clutter ratio (SCR) of 0dB.

关键词： Road transportation Radar detection Object detection Radar Soil Probability Feature extraction Classification algorithms Clutter signal to noise ratio

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Development of A GIS Equipment Partial Discharge Intelligent Detection System Based on Ultra-High Frequency Detection Technology

Development of A GIS Equipment Partial Discharge Intelligent...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Yurui Qin Dongyan Shen Shengkai Tong Tianyu Gu North China Electric Power University Baoding Hebei China

ISBN: (数字)9798350366914

ISBN: (纸本)9798350366921

This article introduces an intelligent detection system for Gas Insulated Switchgear (GIS) based on ultra-high frequency detection technology, which is of great significance for ensuring the stability and safety of power systems. GIS is widely used as a key power equipment in modern power systems. The partial discharge detection results of GIS equipment are important basis for judging its health status. Based on this, the project team has developed an intelligent positioning detection system that integrates ultra-high frequency sensors, digital storage oscilloscopes, test hosts, and advanced software algorithms. This system uses digital signal processing technology and artificial intelligence algorithms to achieve real-time acquisition, intelligent analysis, and high-precision positioning of partial discharge signals. By improving the variational mode decomposition algorithm (IVMD) and the target detection algorithm based on YOLOv5, the system can effectively separate noise from multi-source partial discharge signals and obtain accurate fault diagnosis information. In practical tests, the system has demonstrated excellent anti-interference ability and detection accuracy, providing strong technical support for maintenance and fault prevention of GIS equipment.

关键词： Partial discharges Accuracy Switchgear Software algorithms signal processing algorithms Power system stability Software Sensor systems Real-time systems Gas insulation

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Advancing Techniques for Deepfake Detection and Evaluation: Challenges and Innovations

Advancing Techniques for Deepfake Detection and Evaluation: ...

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Computational Intelligence, Communication Technology and Networking (CICTN), International conference on

作者： Pradum Kumar Singh Sourav Sharma Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

The proliferation of deepfakes-also termed artificial intelligence-generated synthetic media poses unprecedented challenges to the digital authenticity of media, for media integrity, and to societies’ trust in its credibility. Despite such significant technological advancements, current methodologies for detecting deepfakes remain somewhat fragmented, reactive, and often unable to keep pace with rapidly evolving technologies in generative AI. This article considers a comprehensive multi-modal approach toward deepfake detection by integrating an advanced machine learning algorithm, novel statistical correlation techniques, and forensic image analysis that would fill in the critical gaps in the existing frameworks. By using an extensive dataset of 10,000 synthetic and authentic media samples from diverse domains, we developed a hybrid neural network architecture that attained 94.3% accuracy on identifying AI-generated content. Our methodology utilized ensemble learning by combining spatial-temporal inconsistency detection, biological signal analysis, and advanced feature extraction algorithms. Key findings demonstrate significant vulnerabilities in current deepfake generation models, with our proposed technique successfully identifying subtle artifacts and inconsistencies across multiple generative AI platforms. This research conclusively demonstrates that proactive, adaptive detection strategies are critical to mitigating the potential risks associated with synthetic media, providing a robust framework for future technological interventions in digital forensics and media authentication.

关键词： Deepfakes Machine learning algorithms Generative AI Neural networks Digital forensics signal processing algorithms Authentication Media Feature extraction Information integrity

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