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共 565 条记录，以下是71-80 订阅

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Paradapp: an Intelligent Car Parking Assistant Mobile Application Using Convolutional Neural Networks for student and Novice Drivers

Paradapp: an Intelligent Car Parking Assistant Mobile Applic...

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student conference on research and development (scored)

作者： Ken Josh S. Cruz Alberto L. Enano Kharylle S. Sumabat Dan Michael A. Cortez Criselle J. Centeno John Ray M. Tenio College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Parking is a significant challenge for many drivers, often leading to stress and accidents. This paper presents Paradapp, an intelligent car parking assistant designed to aid both student and novice drivers. Utilizing Convolutional Neural Networks (CNN), specifically the YOLO object detection module, Paradapp leverages vehicle’s car cameras to detect obstacles and estimate distances in real-time. The application provides voice-prompted instructions to guide drivers through the parking process, enhancing safety and efficiency. The study aims to reduce parking-related stress and accidents, offering a smart solution accessible via mobile devices. Paradapp’s development follows the AGILE methodology, ensuring iterative improvements and user-centric design. To train the model, a total of 14,000 images were collected from the KTTI and COCO repository to form a dataset related to parking and driving scenarios. The mean average precision of the YOLOv8 model is at 74.3% while the F1 score is at 73% and the Recall score is at 87%. This research contributes to the growing field of intelligent driving systems, providing valuable insights for future advancements in driver-assistance technologies. The system’s effectiveness is evaluated based on ISO 25010:2011 software quality standards, focusing on functional suitability, performance efficiency, usability, reliability, and safety. The overall acceptance of the system is 4.49, interpreted as “Agree”.

关键词： YOLO Car parking Safety Software reliability Convolutional neural networks Usability Stress Standards Vehicles Accidents

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Detecting Low-Rate DDoS Attacks in SDN Using Ensemble Machine Learning Techniques

Detecting Low-Rate DDoS Attacks in SDN Using Ensemble Machin...

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student conference on research and development (scored)

作者： Abdinasir Hirsi Lukman Audah Adeb Salh Nan Mad Sahar Mohammed A. Alhartomi Salman Ahmed Faculty of Electrical & Electronics Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Information & Communication Technology University Tunku Abdul Rahman Kampar Malaysia Department of Electrical Engineering University of Tabuk Tabuk Saudi Arabia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Software-Defined Networking (SDN), a critical enabler of 6 G and IoT systems, is rapidly evolving due to its ability to simplify network management and enhance flexibility. However, its centralized architecture makes it highly vulnerable to security threats, including low-rate Distributed Denial of Service (LDDoS) attacks. These attacks exploit protocol weaknesses, disrupting SDN operations, increasing latency, and degrading reliability—key challenges for ultra-reliable and low-latency communication. This study addresses these threats by employing machine learning (ML) techniques, using the publicly available CICDoS2017 dataset to ensure reproducibility and validation. An ensemble ML classifier was proposed and achieved a 98% detection accuracy, outperforming existing approaches. This work highlights the importance of securing SDN environments as a foundational step toward enabling robust, low-latency, and reliable communication in future 6G and IoT systems.

关键词： Accuracy Machine learning Denial-of-service attack Reproducibility of results Reliability Security Low latency communication Computer crime Software defined networking research and development

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Comparative Analysis Based on Graph Theory for Network Reconfiguration Considering EV Penetration Level and Consumer Charging Behaviour

Comparative Analysis Based on Graph Theory for Network Recon...

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student conference on research and development (scored)

作者： Nur Aina Fatini Hasmaini Mohamad Kanendra Naidu Hafiz Sofiuddin College of Engineering Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The growing adoption of Electric Vehicles (EVs) poses challenges to distribution networks, such as voltage instability, increased losses, and overload conditions. This study proposes an optimization-based network reconfiguration approach to improve overall network performance. Graph theory is employed to ensure radiality, reducing the search space and enhancing optimization efficiency. Two optimization techniques, the Grey-Wolf Optimizer (GWO) and the Jaya optimization algorithm (JOA), are applied to identify near-optimal solutions. The methods are tested on the IEEE-33 bus distribution network under low, medium, and high EV penetration levels, with varying charging behaviours and a 24 hour Malaysian loading profile. Key factors like charging capacity, rate, and timing during peak and off-peak periods are analysed. Results show that graph theory prevents non-radial configurations, and the GWO consistently outperforms the JOA, improving the network’s performance by an average of $\mathbf{9 7. 1 4 \%}, \mathbf{9 7. 1 3 \%}$, and $\mathbf{9 7. 1 1 \%}$ for low, medium, and high EV penetration levels respectively.

关键词： Loading Distribution networks Voltage Electric vehicles Graph theory Timing Optimization research and development

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Prediction of In-vitro Dissolution Profile for Oral Dispersible Film (ODF) using Artificial Neural Network

Prediction of In-vitro Dissolution Profile for Oral Dispersi...

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student conference on research and development (scored)

作者： Fetriya Fatihah Nasar Noor Fitrah Abu Bakar Jamaluddin Mahmud School of Chemical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia School of Mechanical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Ascorbic acid (AA), commonly called ascorbic acid, is a recognised supplement that performs several vital bodily activities. Dissolution assesses the medication release dose over time and verifies compliance with its prescribed administration. The electrospinning technique produces the oral dispersible film (ODF) with a mixture of polyvinyl alcohol (PVA) and Kappa-carrageenan with varying quantities of ascorbic acid. The concentrations of ascorbic acid were modified before electrospinning. This study aims to predict the in vitro dissolution profile using the artificial neural network (ANN) and the experimental value as the input data. About 48 samples were used and randomly used for $5 \%$ validation and $5 \%$ testing. The training algorithm used is Levenbrg-Marquardt, with three input and 10 hidden neurons. The value of R2 obtained is close to 0.99999, indicating that the model achieved the aim as the error is low. Artificial Neural Networks (ANN) are superior for forecasting dissolution profiles since they yield more accurate predictions without necessitating more resources for model construction.

关键词： Training Accuracy Input variables Neurons Artificial neural networks Predictive models Prediction algorithms In vitro research and development Testing

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development of Color Detection Based on Camera Infrared Filter with Python Image Processing

Development of Color Detection Based on Camera Infrared Filt...

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student conference on research and development (scored)

作者： Joshu Leonardy Nawal Zuhdaniel Aflah Mohd Rashidi Bin Salim Mohd Haniff Bin Ibrahim Hadi Bin Manap Wildan Panji Tresna Faculty of Electrical Engineering Universiti Teknologi Malaysia JohorBahru Malaysia Military Physics Study Program The Republic of Indonesia Defense University Bogor Indonesia Faculty of Electrical & Electronic Engineering Technology University Malaysia Pahang Al Sultan Abdullah Pekan Malaysia Research Center for Photonics National Research and Inovation Agency Indonesia South Tangerang Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

This research explores the use of a modified infrared (IR) camera equipped with a high-pass filter to distinguish between natural objects (vegetation) and artificial objects (synthetic military uniforms) in an environment where vegetation makes objects difficult to detect. In military operations, especially guerrilla warfare in tropical forests, detecting enemies camouflaged in vegetation makes it difficult to distinguish targeted objects. Visible light cameras usually struggle to distinguish between synthetic and natural colors. This methodology involves the use of Python programming and various libraries, such as Computer Vision (CV), to process images captured by modified cameras. A graphical user interface (GUI) was developed to analyze color values, focusing on the red, green, and blue spectra. The results show that the modified IR camera can more effectively separate synthetic and natural object color peaks compared to a standard visible light camera, offering potential applications in military reconnaissance and sniper operations. This research demonstrates how image processing can improve object detection in complex environments.

关键词： Image color analysis Vegetation mapping Object detection Reconnaissance Programming Cameras Standards research and development Graphical user interfaces Python

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Rolling Bearing Fault Diagnosis Method Based on Variational Modal Decomposition Envelope Spectrum

Rolling Bearing Fault Diagnosis Method Based on Variational ...

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student conference on research and development (scored)

作者： Zhang Jingsong Yang Soo Siang Jing Zhe Zhou Xiaolong Tan Min Keng Wang Yanzhen Faculty of Engineering Universiti Malaysia Sabah Sabah Malaysia College of Mechanical Engineering Beihua University Jilin China

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Aiming at the problem that the early characteristics of rolling bearing faults are weak, and the traditional envelope spectrum analysis method lacks quantitative feature parameters, a fault diagnosis method based on variational modal decomposition (VMD) envelope spectrum is proposed. Firstly, the maximum correlation kurtosis deconvolution (MCKD) algorithm is used to denoise the rolling bearing fault signal to enhance the fault characteristic information of the signal. Secondly, VMD is used to decompose the filtered signal, and the selection method of the key parameters that affect the accuracy of the decomposition is deeply studied, and the relevant selection method is proposed. Finally, the intrinsic mode function (IMF) sensitive to fault information is selected based on the energy entropy increment-kurtosis criterion, and the amplitude feature ratio of the envelope spectrum and the envelope spectrum entropy are used to quantify the fault feature information of the sensitive IMF components. Construct a multi-feature high-dimensional state feature vector and use the fuzzy C-means (FCM) clustering method to classify and diagnose rolling bearing faults. Through the analysis of experimental data and measured data, the validity and universality of the proposed method are verified.

关键词： Fault diagnosis Accuracy Rolling bearings Filtering algorithms Information filters Entropy Kurtosis Vectors Spectral analysis research and development

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Accelerating CNN Inference: Hybrid Model Pruning for Reduced Computational Demands and High Accuracy

Accelerating CNN Inference: Hybrid Model Pruning for Reduced...

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student conference on research and development (scored)

作者： Abdulgader Ramadan Gadah Suhaila Isaak Abdul-Malik H. Y. Saad Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia College of Engineering University of Buraimi Al Buraimi Oman

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Convolutional Neural Networks (CNNs) have revolutionized various fields, including image recognition, autonomous vehicles, and medical imaging. These models are computationally expensive by including irrelevant processing if the model also affects the system’s accuracy. Model pruning is one of the practical approaches to deal with the computation of the model. This paper proposed the hybrid pruning technique using the EfficientNetB3 model, which achieved a remarkable 50% reduction in model parameters and a 10% decrease in inference time while maintaining an accuracy of over 96% (96.09% vs. 96.22% for the unpruned model). The hybrid approach also demonstrated a significantly faster execution time of 2864.18 seconds, outperforming single pruning methods (2982.77 seconds) and the unpruned model (3197.25 seconds)—this substantial reduction in computation time positions hybrid pruning as the most effective strategy for enhancing CNN performance. The proposed approach provides effective results while reducing the overall computation of the model.

关键词： Accuracy Image recognition Object oriented modeling Computational modeling Focusing Computer architecture Computational efficiency Convolutional neural networks Resource management research and development

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An Efficient Skyline Model for Processing Multiple Continuous Skyline Queries over Uncertain Data Stream

An Efficient Skyline Model for Processing Multiple Continuou...

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student conference on research and development (scored)

作者： Mudathir Ahmed Mohamud Hamidah Ibrahim Fatimah Sidi Siti Nurulain Mohd Rum Department of Computer Science Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Pareto dominance-based skyline queries have drawn a lot of attention for their ability to extract a selection of interesting objects from potentially large multidimensional datasets. These queries are particularly useful for multi-criteria decision support applications. A few techniques have recently been put forth for handling continuous skyline queries over uncertain data streams; however, these techniques primarily address uncertainty resulting from objects having many instances. Uncertainty owing to objects having range values wherein the exact values of the objects are not known at the point of processing has not been extensively explored. Furthermore, it is evident that while identical objects may reappear in the stream at a different time, multiple continuous skyline queries submitted by different users may fall within a similar time frame. Thus, it would be imprudent to process the same portion of the data stream and the same objects repeatedly in order to satisfy these queries. This study attempts to resolve the aforementioned problems by proposing an efficient skyline model in computing multiple continuous skyline queries over uncertain data stream. This research is significant as it contributes in cost reduction and offers faster and better decision making.

关键词： Radio frequency Analytical models Uncertainty Computational modeling Customer relationship management Search problems Data models Streams research and development Random forests

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Identifying Key Mental Health Topic on Youtube Comments using Non-negative Matrix Factorization

Identifying Key Mental Health Topic on Youtube Comments usin...

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student conference on research and development (scored)

作者： Atikah Binti Nazaruddin Noor Hasimah Ibrahim Teo Azmi Bin Aminordin College of Computing Informatics and Mathematics Universiti Teknologi MARA Melaka Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

This study addresses the challenge of identifying key mental health topics within YouTube comments, a resource-rich yet underutilized data source for mental health discourse analysis. The research employed Non-Negative Matrix Factorization (NMF) for topic modeling, coupled with sentiment analysis to map out the landscape of mental health discussions. The methodology pivoted around the data science lifecycle, ensuring systematic data collection, preparation, and modeling. A notable deliverable was a dynamic dashboard visualizing the findings through various graphical representations, including bar graphs, pie chart, donut chart and word clouds. The analysis unearthed prevalent themes and sentiments, offering granular insights into public discourse on mental health. The implications of this study are twofold: informing mental health providers about emerging trends and sentiments for more tailored intervention strategies and assisting content creators and platform managers in shaping more impactful communication strategies around mental health topics. The study’s findings underscore the value of machine learning techniques, particularly NMF and sentiment analysis, in gleaning actionable insights from large-scale, unstructured social media datasets, thereby advancing the conversation around mental health awareness and support.

关键词： Sentiment analysis Video on demand Systematics Social networking (online) Soft sensors Mental health Oral communication Tag clouds Web sites research and development

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Virtual Reality for Stress Reduction: Usability Insights into Exposure Environments and Relaxation Therapy

Virtual Reality for Stress Reduction: Usability Insights int...

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student conference on research and development (scored)

作者： Chiou Sheng Chew Muhammad Muizuddin Mohd Yasin Siti Nuramalina Binti Johari Nurul Hidayah Mat Zain Raihah Aminuddin Siti Feirusz Ahmad Fesol Mohamad Faiz Dzulkalnine College of Computing Informatics and Mathematics Universiti Teknologi MARA Melaka Branch Jasin Campus Melaka Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Mental health issues can have a negative impact on physical health and well-being, leading to stress, burnout and decreased productivity and quality of life. Traditional therapy’s generalized approach may not always work for people who have different preferences and stress triggers. These collective challenges might create barriers to receiving the appropriate treatment for the patient. These challenges emphasize the need for advanced treatment tools for mental illness, such as a virtual reality (VR) application called OasisVR. This application leverages immersive and interactive experiences, incorporating exposure environments and relaxation therapy in its design. The OasisVR has implemented an Analysis, Design, development, Implementation, and Evaluation Model (ADDIE Model) focusing on VR immersive experience. The usability of the application was evaluated by three experts in mental health who are experienced in those fields and knowledgeable about mental health in detail using the Suitability Evaluation Questionnaire (SEQ). The study findings indicate that OasisVR received an average score of 42.33, signifying suitability for stress reduction. Future research aims to address issues about efficacy, privacy, data security, and incentives to use therapy treatment using VR applications.

关键词： Productivity Solid modeling Data privacy Data security Medical treatment Focusing Mental health Immersive experience Usability research and development

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