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4th International Conference on Intelligent engineering and Management, ICIEM 2023

作者： Devi Padma, K. Harshitha, P. Nafiza, R. Lilian, J. Felicia Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350341126

The neurological ailment Parkinson's disease affects millions of individuals globally. Contrarily, an early detection of the illness will aid in its efficient treatment. With machine learning, which is still in its infancy, there are a number of prospects for computer-aided identification and diagnosis that may lessen inescapable medical issues and intrinsic clinical uncertainty, offer direction, and enhance decision-making. In an effort to improve the technique for diagnosing Parkinson's disease, we have proposed a model for early detection of the condition. Performance and speed were taken into consideration when XGBoost, a new machine learning technique, was developed. A decision tree-based technique called Extreme Gradient Boosting, or XGBoost, was developed. © 2023 IEEE.

关键词： Decision trees

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A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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How accurately can soft error impact be estimated in black-box/white-box cases? - A case study with an edge AI SoC 24

How accurately can soft error impact be estimated in black-b...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Cheng, Quan Li, Qiufeng Lin, Longyang Liao, Wang Dai, Liuyao Yu, Hao Hashimoto, Masanori Department of Communications and Computer Engineering Kyoto University Kyoto Japan School of Microelectronics Southern University of Science and Technology Shenzhen China School of Systems Engineering Kochi University of Technology Kochi Japan Department of Computer Science and Engineering University of California Merced United States

ISBN: (纸本)9798400706011

Artificial intelligence (AI) edge devices often feature numerous storage units and sequential logic circuits, making them vulnerable to soft errors. For reliable and critical edge AI applications, assessing System-on-Chip (SoC) reliability in advance is essential. Here, there are two cases: a self-designed SoC (white-box), or a commercial off-the-shelf (COTS) chip (black-box). This study uses alpha particle irradiation results on our 22nm AI SoC as a golden reference to estimate soft error impacts, injecting faults across the entire chip in the white-box case and into the accessible memory and registers in the black-box case. The results demonstrate a high degree of consistency between the white-box case and golden reference, meaning that pre-silicon reliability assessment is feasible. As for the black-box case, the proportion of memory in the SoC remains unchanged and is still significantly larger than that of registers, and hence the simulation results between black-box and white-box are not substantially different. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： System-on-chip

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Developing a Smart Drip Infusion System to Elevate Precision in Healthcare 4

Developing a Smart Drip Infusion System to Elevate Precision...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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Employing Feature engineering for River Stage Forecasting to Improve Hybrid Model Performance 32

Employing Feature Engineering for River Stage Forecasting to...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Mihel, Anna Maria Pecnik, Spela Vrbancic, Grega Lerga, Jonatan Krvavica, Nino University of Rijeka Faculty of Engineering Department of Computer Engineering Rijeka Croatia University of Maribor Intelligent Systems Laboratory Faculty of Electrical Engineering and Computer Science Maribor Slovenia University of Rijeka Faculty of Civil Engineering Department of Hydrotechnics and Geotechnics Rijeka Croatia

ISBN: (纸本)9789532901382

Establishing early warning systems and efficient management of water resources in tidal reaches is crucial for achieving adequate flood protection. In tidal reaches, the river stage interacts non-linearly with tides (downstream) and discharge (upstream), making the modeling of river processes extremely complex. Data-driven methods have proven helpful for predicting the river stage parameter. This contrasts with existing numerical approaches, which suffer from several limitations. The aim of this study was to predict the river stage of the upstream part of the tidal river, where the influence of salinity is present during the summer months. Using the CNN-LSTM model trained on historical records of the discharge (station of the variable to be predicted), the river stage of three downstream stations, and the point of interest, the prediction was carried out up to 24 hours in advance. By incorporating feature engineering, we achieved an improvement in model results for the longest horizon, confirmed by standard performance metrics, and considered it efficient and effective for risk mitigation. Introducing feature engineering into the data preprocessing resulted in a predictive performance improvement of 0.98% for the Nash-Sutcliffe Efficiency (NSE) metric, 7.0% for the root mean squared error (RMSE) metric, and 7.25% for the mean absolute error (MAE) metric over the second and third-best scenarios using a spectrogram and time-series data. © 2024 University of Split, FESB.

关键词： Mean square error

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ILRM: Imitation Learning Based Resource Management for Integrated CPU-GPU Edge systems with Renewable Energy Sources

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2024年第6期44卷 2392-2397页

作者： Hou, Xiangpeng Zhou, Junlong Li, Liying Zhao, Mingzhou Cong, Peijin Wu, Zebin Hu, Shiyan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China University of Hong Kong Department of Data and Systems Engineering Hong Kong Hong Kong

This paper focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability *** propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: offline Oracle generation and online imitation learning. In the offline phase, we design a fast-converging heuristic to generate near optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize imitation learning using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms. © 1982-2012 IEEE.

关键词： Graphics processing units Reliability Central Processing Unit Renewable energy sources Real-time systems Integrated circuit modeling Imitation learning Generators Energy consumption Edge computing

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A Parallel Hybrid Testing Technique for Tri-programming Model-Based Software systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Toward Parallelization Technique for Stream-based Lossless Data Compression

Toward Parallelization Technique for Stream-based Lossless D...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kato, Taiki Yamagiwa, Shinichi Wada, Koichi Master's Program in Computer Science Degree Program in Systems and Information Engineering University of Tsukuba Ibalaki Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Ibalaki Tsukuba Japan

ISBN: (纸本)9798350324457

IoT edge platform has become popular in various distributed environments. The edge devices need to communicate BigData among them or with the cloud servers by collaborating with AI technologies for finding events from the applications. Those devices exchange data streams from such as distributed sensors and remote image/video devices. We focus on an acceleration technique for the communication performance using a stream-based lossless data compression technology. This paper proposes a parallelization technique for the compression process in a software environment running on a multicore processor. The technique invokes concurrent compression processes assigned to multiple threads with splitting a data stream to chunks. The paper exposes three scheduling methods for assigning the chunks to the threads: in-order, hybrid and out-of-order. As an original data order of chunks must be obtained in decompression side, the proposed technique introduces packeting mechanisms in each chunk by adding headers to support the scheduling methods. Through experimental performance evaluations, we discuss the packeting overhead focusing on compression ratio and speedup by the parallelization with three scheduling methods. © 2023 IEEE.

关键词： Data Stream IoT Lossless Data Compression Multi-Threading Parallelization

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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Performance Enhancement of Stream-Based Decompression Process by Notifying Compression Buffer Size 28

Performance Enhancement of Stream-Based Decompression Proces...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Kato, Taiki Yamagiwa, Shinichi Marumo, Koichi University of Tsukuba Master's Program in Computer Science Degree Program in Systems and Information Engineering Ibalaki Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Ibalaki Tsukuba Japan

ISBN: (纸本)9798350300482

This paper focuses on a performance enhancement of communication performance by compressing data stream. ASE coding is an effective lossless data compression method for data stream. The software implementation of the coding/decoding method inevitably meets a performance mismatch in memory and storage devices. In the compressor side, it is predictable to decide the size of an original data block and is available to process a flexible buffer memory. However, the decompressor is not able to predict the buffer size because the original data size is not obvious before the decompression. This causes a performance mismatch in the filesystem level. This paper proposes a novel method to address the performance mismatch by applying a notification mechanism of compression size from the compressor. This paper describes the mechanism focusing on the system call usage. Through experimental evaluations, we show the performance improvement of the decompression performance for handling data stream. © 2023 IEEE.

关键词： Virtual storage

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