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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Jagadeesan, S. Ramalingam, Venkadeshan Abinash, P. Ilayaraja, V. Masood, Jafar Ali Ibrahim Syed Tamil Priya, D. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India UTAS-Shinas branch Department of Computing and Information Sciences Oman School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331542573

Stroke and related neurological disorders are difficult to classify accurately, hence new computational approaches are needed to enhance diagnosis. In order to transform the categorization of brain strokes, this study presents Neuro-Xception, a novel framework that combines the Xception architecture with Extreme Learning Machines (ELM). The rapid learning speed and enhanced generalisation capability of ELMs are combined with the feature extraction expertise of Xception to create Neuro-Xception. Through training on large neuroimaging datasets, Neuro-Xception learns complex patterns that are essential for differentiating between different types and intensities of stroke. By means of extensive testing on various datasets, Neuro-Xception exhibits notable gains in performance compared to traditional techniques, indicating its potential to revolutionise stroke detection in medical environments. Furthermore, Neuro-Xception's scalability and flexibility are enhanced by the combination of Xception and ELMs, which makes it a viable tool for real-time clinical decision support systems and personalised medicine. This study highlights how merging Xception and ELMs can revolutionise neurological healthcare and this development could lead to more accurate and efficient diagnostic techniques for treating stroke patients. Neuro-Xception offers a potential path for increasing diagnosis accuracy and expediting timely medical intervention. It is a major step forward in the use of artificial intelligence to enhance patient outcomes and advance the profession of stroke care. © 2024 IEEE.

关键词： Personalized medicine

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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Computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Repetitive Path Planning with Experience-Based Bidirectional RRT 1

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International Conference on Robots for Space Applications in Orbital Stations, TORVEASTRO 2023

作者： Zuo, Guoyu Wu, Chun Huang, Gao Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (数字)9783031284472

ISBN: (纸本)9783031284465

Repetitive motion planning in semi-structured environments is involved in many robotics applications, such as manufacturing and in-orbit service of spatial robotic arms. In these scenarios, the environment is mainly static, but it may contain some movable obstacles that the robot needs to avoid, and the planning problems that the robot will encounter are very similar. However, most current sampling-based motion planning methods ignore the correlation among tasks, and instead plan each task instance from scratch, or assume that the environment is highly structured. In order to overcome this problem, this paper proposes an experience-based bidirectional rapidly-exploring random trees* (EBRRT*) for repetitive path planning in semi-structured environments. First, an adaptive sampler learned from the demonstration paths by a Gaussian mixture model (GMM) is constructed, which can guide the planner to generate samples in task-related regions. Second, an experience graph is proposed to capture the exploration of configuration space by previous similar tasks. Subsequent tasks can retrieve locally valuable information from the graph to avoid exploring the configuration space repeatedly. In addition, the validity of edges contained in the experience graph is lazily verified during the planning process so that the experience graph can adapt to small environmental changes. Finally, the proposed method is verified in a 2D simulation environment and a 7-DOF manipulator scene. Experimental results show that the proposed method can return a high-quality path in less time than other state-of-the-art sample-based motion planning for repetitive path planning in semi-structured environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion planning

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GenreNet: A Deep Based Approach for Music Genre Classification

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SN Computer Science 2024年第8期5卷 1135页

作者： Bala Ganesh, N. Bhuvaneswari, M.S. Bhagavathi Sankar, K. Ganesh, P. Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Department of Smart Computing Vellore Institute of Technology Tamilnadu Vellore India Ciena India Haryana Gurgaon India Tata Consultancy Services Tamilnadu Chennai India

Neural network-based Music Genre Classification is a key component in helping users narrow down the selection of songs and listen to music in a certain genre. Audio segmentation has been done as a preprocessing step on the available audio files using the benchmark dataset “GTZAN”. To improve accuracy, audio segmentation divides a 30-s audio recording into ten 3-s audio files. Segments are utilized to create spectrograms, which are then used to extract features. A spectrogram is a visual depiction of a signal’s frequency band. Applying techniques like magphase and Short Time Fourier Transform to the audio data enhances the quality of the spectrograms. Adaptive Moment estimation, Nesterov-accelerated Adaptive Moment estimation (NADAM), Adaptive Moment estimation with Maximum, and Root Mean Squared Propagation are among the optimization techniques used for feature weight optimization to increase the classification rate. NADAM is found to be effective based on performance measures. The proposed GenreNet model is compared with the state-of-art techniques and performance measures such as Accuracy, Precision, Recall, Root Mean Square Error, F1 Score, Loss and Sensitivity have yielded positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alexnet18 Audio classification CNN MGC Music genre classification Spectrogram SVM

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Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach 38

Replay-and-Forget-Free Graph Class-Incremental Learning: A T...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Niu, Chaoxi Pang, Guansong Chen, Ling Liu, Bing AAII University of Technology Sydney Australia School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science University of Illinois Chicago United States

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID prediction on graph data can be achieved by a Laplacian smoothing-based graph task profiling approach, in which each graph task is modeled by a task prototype based on Laplacian smoothing over the graph. It guarantees that the task prototypes of the same graph task are nearly the same with a large smoothing step, while those of different tasks are distinct due to differences in graph structure and node attributes. Further, to avoid the catastrophic forgetting of the knowledge learned in previous graph tasks, we propose a novel graph prompting approach for GCIL which learns a small discriminative graph prompt for each task, essentially resulting in a separate classification model for each task. The prompt learning requires the training of a single graph neural network (GNN) only once on the first task, and no data replay is required thereafter, thereby obtaining a GCIL model being both replay-free and forget-free. Extensive experiments on four GCIL benchmarks show that i) our task prototype-based method can achieve 100% task ID prediction accuracy on all four datasets, ii) our GCIL model significantly outperforms state-of-the-art competing methods by at least 18% in average CIL accuracy, and iii) our model is fully free of forgetting on the four datasets. Code is available at https://***/mala-lab/TPP. © 2024 Neural information processing syste

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USTW Vs. STW: A Comparative Analysis for Exam Question Classification based on Bloom’s Taxonomy

Mendel

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Mendel 2022年第2期28卷 25-40页

作者： Gani, Mohammed Osman Ayyasamy, Ramesh Kumar Fui, Tien Sangodiah, Anbuselvan Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia Department of Information Systems Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh Malaysia

Bloom’s Taxonomy (BT) is widely used in educational institutions to produce high-quality exam papers to evaluate students’ knowledge at different cognitive levels. However, manual question labeling takes a long time, and not all evaluators are familiar with BT. The researchers worked to automate the exam question classification process based on BT as a solution. Enhancement in term weighting is one of the ways to increase classification accuracy while working with text data. However, all the past work on the term weighting in exam question classification focused on unsupervised term weighting (USTW) schemes. The supervised term weighting (STW) schemes showed effectiveness in text classification but were not addressed in past studies of exam question classification. As a result, this study focused on the effectiveness of STW in classifying exam questions using BT. Hence, this research performed a comparative analysis between the USTW schemes and STW for exam question classification. The STW schemes used in this study are TF-ICF, TF-IDF-ICF, and TF-IDF-ICSDF, whereas the USTW schemes used for comparison are TF-IDF, ETF-IDF, and TFPOS-IDF. This study used Support Vector Machines, Naïve Bayes, and Multilayer Perceptron to train the models. Accuracy and F1 score were used in this study to evaluate the classification results. The experiment results showed that overall, the STW scheme TF-ICF outperformed all the other schemes, followed by the USTW scheme ETF-IDF. Both the ETF-IDF and TFPOS-IDF outperformed standard TF-IDF. The outcome of this study indicates the future research direction where the combination of STW and USTW schemes may increase the accuracy of BT-based exam question classification. © 2022, Brno University of technology. All rights reserved.

关键词： Taxonomies

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Serverless Machine Learning Platform: A Case for Real-Time Crisis Detection over Social Media 2

Serverless Machine Learning Platform: A Case for Real-Time C...

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2nd International Conference on Computer Science, Engineering and Applications, ICCSEA 2022

作者： Jambi, Sahar King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Jeddah Saudi Arabia

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Serverless computing is an emerging execution model in the cloud in which all required infrastructure and resources are fully managed by cloud providers. The promising features of serverless technology have created a new research focus on building the next generation serverless machine learning. In this paper, we propose a serverless machine learning platform for serving models in real-time by leveraging Google serverless cloud services. We evaluate the proposed platform by serving an NLP model built for crisis detection over Twitter platform. Our experimental results substantiate that serverless machine learning provides lightweight and cost effective cloud services for building highly scalable applications for real-time ML model predictions. © 2022 IEEE.

关键词： Machine learning

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A Tool for Sales Forecasting based on the Neural Network 10

A Tool for Sales Forecasting based on the Neural Network

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17th INDIACom;2023 10th International Conference on computing for Sustainable Global Development, INDIACom 2023

作者： Basahel, Sarah Sen, Adnan Ahmed Abi King Abdulaziz University Department of Management Information Systems Jeddah Saudi Arabia Islamic University Department of Computing and Information Technology Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Time series is a well-known way of successively predicting the behavior of a variable by considering its preceding behavior. It is well known that predicting future sales of large companies is a critical and sensitive issue because it is linked to the production quantity. Accuracy of predictions can save a significant amount of money by reducing the amount of waste due to excessive production. Several other methods are used for prediction. This paper provided a comparison between three methods of predicting based on time series, namely regression, moving averages, and neural networks. Also, the paper provided a tool to facilitate working with neural networks for non-specialists (normal users). The results on real data for a Saudi company have proved that the neural networks model outperformed other models with an accuracy rate or more than 96%. The tool of this paper will be useful for building more complex models by introducing additional factors into the sales forecasting process and publishing it as a web service for ease of use and access. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Sales

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An Artificial Intelligence (AI) based Energy Efficient and Secured Virtual Machine Allocation Model in Cloud 3

An Artificial Intelligence (AI) based Energy Efficient and S...

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3rd International Conference on computing, Analytics and Networks, ICAN 2022

作者： Alhebaishi, Nawaf King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Jeddah Saudi Arabia

ISBN: (纸本)9781665499446

Ensuring security in cloud systems is one of the most significant tasks for users facing security problems in load balancing. Many conventional works have developed secured load balancing models for allocating Virtual Machines (VMs) in cloud systems. Still, it limits by the problems of high migration overhead, increased processing time, storage complexity, high energy consumption, and lack of security. So, the proposed work objects to implement an energy-efficient and secured Artificial Intelligence (AI) based VM allocation model for cloud load balancing. The novel contribution of this work is to develop an Intelligent Mine Blow Optimization (IBMO) technique to securely allocate the VMs in the cloud. Also, it helps to mitigate the security risks created by the attacks of VMs from multiple users. Moreover, it maintains the workloads among all VMs in the cloud server. The primary advantages of this work are reduced energy consumption, resource consumption, and overhead. During analysis, the performance and results of the proposed IBMO-based VM allocation model are validated and compared using various evaluation indicators. © 2022 IEEE.

关键词： Virtual machine

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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Computer Modeling in Engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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