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检索条件"任意字段=7th IEEE International Conference on Engineering of Complex Computer Systems"

共 20108 条记录，以下是71-80 订阅

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Boosting Software Quality: A Machine Learning Technique for Predicting Defects 6

Boosting Software Quality: A Machine Learning Technique for ...

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

作者： Akilan Daniel, E. Alwin Infant, P. Jainish, G.R. Panimalar Engineering College Chennai City Campus Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Loyola ICAM College of Engineering & Technology Chennai India

ISBN: (纸本)9798331522667

In order to increase automation, strengthen decision- making, and improve efficiency while reducing costs and maintaining precision, the development of intelligent machinery is becoming more and more dependent on the integration of data, embedded software, and modern technologies. Reliability of embedded software becomes essential for guaranteeing high system performance and an improved user experience as technology and hardware merge to build smart devices. this work explores methods to quantify software reliability, which measures the likelihood of residual failures in a system. the objective is to build a statistical relationship between quality parameters and product or process metrics through the analysis of software metrics produced from development data. these indicators aid in evaluating the software's dependability and performance, enabling developers to spot and fix possible problems early on. Using pattern recognition algorithms to pinpoint software's fault-prone areas is a crucial strategy for enhancing software reliability prediction. these algorithms enable developers to focus attention on software areas that have a higher likelihood of failure by identifying patterns in the product's code or structure that suggest increased risk. In summary, these methods focus on enhancing the reliability of intelligent machines by equipping developers with advanced tools to reduce defects, improve dependability, and ensure that smart systems perform optimally. this approach aligns with modern trends in using machine learning and data-driven models to tackle complex technological challenges. © 2025 ieee.

关键词： Data reliability

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Design of a 16-Bit Power-Efficient Posit Multiplier with Selective Activation and Modified Radix-4 Booth Multiplier 6

Design of a 16-Bit Power-Efficient Posit Multiplier with Sel...

引用

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

作者： Adinarayana, T.V.S. Rajesh, G. Reddy, Yashasvi Linga Yadav, Vishwass R. Gandla, Srinivas Abhinay New Horizon College of Engineering Bangalore India

ISBN: (纸本)9798331522667

the Posit number system presents a more flexible and precise alternative to the traditional ieee 754 floating-point format, but its inherent complexity, particularly in multiplication, can lead to high power consumption in hardware implementations. this research proposes a power-efficient design for a 16-bit Posit multiplier that uses a modified Radix-4 Booth algorithm to selectively activate only the necessary sections of the multiplier based on the required bit-width for the mantissa. By minimizing unnecessary computations and switching activities, the design reduces dynamic power consumption. the architecture includes components such as the mantissa multiplication unit, normalization stage, and rounding unit, all optimized for efficiency. Simulation results using Xilinx Vivado confirm the functionality of the design, while power and area analysis on the Xilinx Artix-7 FPGA demonstrate its efficiency compared to existing implementations. the proposed design is suitable for low-power applications and offers significant improvements in both power consumption and area utilization. © 2025 ieee.

关键词： Power systems computer aided design

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Energy-Guided Topology Mamba for EEG-based BCI 13

Energy-Guided Topology Mamba for EEG-based BCI

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Jeong, Seungwoo Jeon, Jaehyun Suk, Heung-Il The Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of The Department of Artificial Intelligence The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9798331521929

Electroencephalography (EEG) signals, widely used in neuroscience and clinical research, provide non-invasive insights into brain activity. However, their high variability, noise, and complex spatiotemporal dynamics present significant challenges for effective modeling. To tackle these issues, we introduce an energy-guided topology for EEG signal analysis within the Mamba framework, designed to enhance feature propagation by leveraging spectral energy distribution. Our method first transforms EEG signals into the frequency domain and constructs an energy-based topology using channel-wise spectral power. Features are then propagated bidirectionally along energy gradients, enabling the Mamba model to capture temporal and spatial dependencies more effectively. By aligning feature learning with energy flow in the frequency domain, our approach improves the efficiency and accuracy of EEG modeling. Experimental results on the public dataset demonstrate that our method outperforms CNN-based, Transformer-based, and vanilla Mamba models, underscoring its potential to advance EEG signal processing. © 2025 ieee.

关键词： Frequency domain analysis

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the Impact of Agents Heterogeneous in Call Center Performance Measures

The Impact of Agents Heterogeneous in Call Center Performa...

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7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： thiongane, Mamadou Ould Deye, Mohamed M. Gueye, Modou Bame, Ndiouma Department of Mathematics and Computer Science University Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031864926

Modern call centers are highly complex queuing systems, in which there are several possible call types, and many agents groups. Due to this complexity, simulators are now preferred for their management than standard Erlang queuing models. Several studies have shown through data analysis that agents often have quite different speeds for processing a call type. However, this agent heterogeneity is often neglected, which is why most simulators have not been designed to use heterogeneous agents to process a service. In this work, we use simulation to study the impact of agent heterogeneity on the performance of call centers. We developed and integrated a module into the call center simulator, ContactCenter, which allows the use of heterogeneous agents to process a service. We have analyzed data collected from a real call center and have shown that agents have different processing speeds for handling a call type. We have modeled the distribution of arrivals, service times and patience times that will enable us to simulate this call center. Simulation result show that call center performance on a given day depends largely on the agents chosen to answer the calls. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Sorting

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Sustainable Drone Surveillance System Using Eternal Vertex Cover and Periodic Charging 17

Sustainable Drone Surveillance System Using Eternal Vertex C...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Dhawde, Navya Chakraborty, Nilotpal Husain Baig, Mirza Galib Anwarul Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India Indian Institute of Technology School of Mathematical and Statistical Sciences Mandi India

ISBN: (纸本)9798331531195

Unmanned aerial vehicles (UAVs), or drones, are transforming surveillance strategies in various fields, including border security and wildlife conservation. their ability to monitor large, challenging areas in real time has established them as a valuable tool, particularly in scenarios where traditional surveillance methods face operational and logistical limitations. this research focuses on optimizing drone-based surveillance along the international borders, and in wildlife conservation areas, where challenges like illegal crossings, cattle smuggling, and poaching present serious security and economic threats. Leveraging UAVs provides a cost-effective, flexible, and highly deployable solution that overcomes the limitations of static surveillance systems and limited human patrols. We propose an optimized drone-based system designed to enhance operational efficiency, coverage, and adaptability in these complex environments, with potential applications in various sectors requiring comprehensive, real-time surveillance. We propose modeling the problem using the Eternal Vertex Cover problem, a dynamic extension of the classical Vertex Cover problem. Given the NP-hard nature of determining this cover number, our research explores heuristic and algorithmic approaches to achieve an efficient and scalable solution. We also propose an intelligent charging mechanism for the drones for their continuous operation. All proposed approaches are evaluated using randomly generated datasets as long as benchmark datasets. © 2025 ieee.

关键词： Drones

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An Enhanced Keylogger Detection systems Using Recurrent Neural Networks Enabled with Feature Selection Model 7th

An Enhanced Keylogger Detection Systems Using Recurrent Neur...

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7th international conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Rodríguez, Demóstenes Zegarra Akanji, Boluwatife Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Computer Science Federal University of Lavras Minas Gerais Lavras Brazil

ISBN: (纸本)9789819764648

Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. they can steal sensitive information like credit card numbers and passwords. they pose a significant threat to users’ privacy and security since they capture keystroke data. Keylogger detection systems play a vital role in safeguarding users from cyberattacks and mitigating the potential harm caused by keyloggers. Keylogger detection systems employing deep learning have been widely used for identifying and mitigating cyber threats associated with keyloggers. However, they struggle to identify new or unfamiliar keylogger samples. this study aims to explore deep learning techniques with feature selection model to detect keylogger. this study employs Recurrent Neural Networks (RNNs) using a collection of known keylogger dataset. Furthermore, a correlation-based feature extraction method was applied to identify the most relevant features for the model, highlighting the importance of specific features, such as URG flag Count, ACK flag count, and Idle Mean, in differentiating between benign and keylog classes. the proposed model demonstrates superior accuracy of 0.8763 and precision of 0.8569 compared to a baseline model using Logistic Regression (LR) 0.7721 and 0.7407, respectively, indicating a better balance between accurate classification and minimizing false negatives and false positives. the findings from this study help to improve keylogger detection methods, making them more powerful and efficient. these results suggest that a keylogger-based detection system employing a deep learning approach can be a valuable tool for addressing the complex and evolving landscape of cyber threats related to keylogging. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Phoneme Classification in Imagined Speech Using Explainable Machine Learning 13

Phoneme Classification in Imagined Speech Using Explainable ...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Kim, Sejin Kang, Hyunwook Jeong, Ji-Hoon Lee, Minji Dept. Artificial Intelligence The Catholic University of Korea Bucheon Korea Republic of Dept. Electrical and Computer Engineering Inha University Incheon Korea Republic of Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Imagined speech is gaining attention as a next-generation paradigm for brain-computer interfaces in terms of its intuitiveness in communication. Many studies have focused on classifying imagined words as the basis of communication, but some studies on phoneme classification based on imagined speech have begun to be conducted in terms of the fact that combinations of phonemes can further expand sound images. In addition, explainability is a major issue in classification results in machine learning, but there have been few studies that analyzed the explainability of imagined speech classification. In this study, we examined explainability using SHAP for phoneme classification based on imagined speech. FEIS dataset was used, and 16 phonemes were classified using the XGBoost classifier. In 21 subjects, the average accuracy was 40.36%, the average precision was 41.7%, the average recall was 40.34%, and the average F1-score was 40.7%. In addition, when looking at Sub 04, which had the highest performance, the SHAP values were high in AF3 and F7 channels, which correspond to Wernicke’s area related to speech. In this regard, we could observe the feature importance of this area. these results prove that Wernicke’s area actually plays an important role in classifying phoneme units in imagined speech, and can provide important insights to improve the performance of imagined speech for intuitive communication based on brain-computer interface in the future. © 2025 ieee.

关键词： Adversarial machine learning

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Terminating a Two-stage Contract: From Collaboration to Competition 16

Terminating a Two-stage Contract: From Collaboration to Comp...

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16th international conference on ENTERprise Information systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Rofstad, Kristian Wilberg Engebø, Atle Lædre, Ola Department of Civil and Environmental Engineering NTNU Høgskoleringen 7a TrondheimN-7491 Norway

the Norwegian construction industry has an increased use of two-stage contracts where clients and contractors collaborate on developing the project in the first stage before executing it in the stage. this study examines: 1) How did the two-stage model work from procurement to present, and 2) the positive and negative experiences with this model. A qualitative case study design combining literature review, 11 semi-structured interviews, and document studies has been used. As the client and contractor agreed on not to continue the collaboration after the first stage, the studied public hospital project turned from one two-stage contract to 28 design-bid-build contracts. Positive experiences from the first stage included optimised design, improved technical solutions, and multidisciplinary collaboration. Negative experiences involved challenges in reducing costs for a complex hospital with strict budget and functional requirements. the study concludes that early involvement in public hospital projects can be challenging if the budget is too low compared to the predefined requirements. In future two-stage contracts, the client should settle the minimum key requirements before the contractors submit their tenders. the study is one of the first to document the experiences from a two-stage contract where the client and the initial contractor agreed not to continue the collaboration after the first stage. these experiences can help future projects that encounter challenges in the first stage. © 2025 the Authors.

关键词： Budget control

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PyGrossone: A Python Library for the Infinity computer 4th

PyGrossone: A Python Library for the Infinity Computer

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Falcone, Alberto Garro, Alfredo Sergeyev, Yaroslav D. Rende87036 Italy

ISBN: (纸本)9783031812460

computers typically adopt the ieee 754-1985 binary floating point standard to represent and work with numbers. Although computers can work with finite numbers, numerical computations that involve infinite and infinitesimal quantities are impossible due to architectural limitations. this paper is dedicated to the Infinity computer, a new type of supercomputer that allows one to perform calculations involving finite, infinite, and infinitesimal numbers. the existent simulators of the Infinity computer available for the Matlab/Simulink environment are already used in several research domains to solve real-world problems, where accuracy is a crucial aspect. However, the Matlab/Simulink simulators are not well suited for solving problems in the Artificial Intelligence and Machine Learning domains, where the Python programming language is largely adopted. For this purpose, the main aim of this paper is to introduce PyGrossone, a Python library for the Infinity computer. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Python

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Short-Term Load Forecasting Using Multilayer Neural Networks: A Residential complex Case Study 5

Short-Term Load Forecasting Using Multilayer Neural Networks...

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5th National and the 1st international conference on Applied Research in Electrical engineering, AREE 2025

作者： Mehr, Mehdi Mohammadian Farzin, Hossein Mashhour, Elahe Shahid Chamran University of Ahvaz Department of Electrical Engineering Ahvaz Iran

ISBN: (纸本)9798350377170

Short-term electric load forecasting (STLF) is a crucial tool for optimizing energy consumption and load management in power systems. this study aims to develop an accurate short-term electric load forecasting model using multilayer neural networks (MLNNs) that integrates historical electrical load data with meteorological variables. the dataset used for training the model was collected from a residential complex in Ahvaz, Iran, spanning from March 21, 2021, to August 24, 2021. It includes 7,222 records of half-hourly electrical load data, along with the corresponding weather data such as temperature, humidity, wind speed, and atmospheric pressure. the primary objective of the model is to improve the prediction accuracy for energy consumption in dynamic environments, such as large buildings or industrial complexes, where various internal and external factors influence electricity demand. the results revealed that the model performed exceptionally well on the first day, with the forecasted loads closely aligning with the actual loads and minimal deviation. However, as the forecasting period extended, the accuracy gradually declined, especially in high-demand scenarios. Specifically, over the seven-day period, the forecasted loads were approximately 6.5% higher than the actual loads. Despite these discrepancies, the model effectively captured the overall trend of electricity demand, providing valuable insights for energy management and grid optimization. Overall, while the model's performance can be enhanced, it provides a solid foundation for improving short-term electric load forecasting and serves as a valuable tool for demand-side management and energy efficiency strategies. © 2025 ieee.

关键词： Multilayer neural networks

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