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International Conference on computing and Networking Technology (ICCNT)

作者： Sachin Paranagama Isiwara Uditha Pamodya Daundasekara Shehani Hettiarachchi Lokesha Weerasinghe Dharshana Kasthurirathna Department of Computer Science and Software Engineering Faculty Of Computing Sri Lanka Institue Of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Schizophrenia is a chronic mental disorder with distorted thinking, hallucinations, and social difficulties. Early diagnosis is vital, but current methods are limited. This research proposes a web-based application integrating machine learning, emotion recognition, and game-based elements to track symptoms and aid in early diagnosis. Interactive exercises focus on specific symptoms like cognitive evaluation, emotional analysis, and memory assessment. By combining computer science, psychology, and machine learning, the project aims to improve early detection and ultimately enhance the quality of life for affected individuals. Key components include using Deep Convolutional Neural Network (DCNN) architecture for recognizing facial and physical expressions associated with schizophrenia with an 87% accuracy rate, and utilizing Vector Regressor (SVR) with a 93% accuracy rate, along with Vision Transformer (ViT) and Custom CNN models for cognitive assessment, achieving accuracy rates of 99% and 98%, respectively.

关键词： Computer science Emotion recognition Computer vision Accuracy Face recognition Psychology Machine learning Transformers Vectors Convolutional neural networks

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Kernel Fashion Context Recommender System (KFCR): A Kernel Mapping Fashion Recommender System Algorithm Using Contextual Information

Kernel Fashion Context Recommender System (KFCR): A Kernel M...

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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Waseem Abbas Uzair Rasheed Sadia Khalid Mustansar Ghazanfar Ali Hassan Dept of Computer and Software Engineering College of EME NUST Islamabad Pakistan Department of Engineering and Computing University of East London London United Kingdom

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

In Fashion, Recommender System represents a growing trend. They enable to offer the customer online fully personalized shopping experience. Many known names on the Fashion market such as Asos (***) or Zalando (***), have already bet on this technology to retain customers, boosting profits. Multiple filtering approaches exist, developed to cope with all the inherent challenges driven by the lack of information and the ephemeral nature of fashion items. However, even if the methods adopted are very varied, the main motive remains the same: reducing the margin of error at all costs to produce the nearest reality prediction. Accuracy, scalability, flexibility, and performance have become the keywords when creating a fully skilled Recommender System. To achieve these objectives, it appears that including in the model user's context information, such as time, location, mood, occasion, weather, or people's influence can be the answer. Not including contextual information is eluding a fundamental element in the decision-making process for the purchase of a particular piece of clothing. In this paper, we decided to apply to the Fashion domain issues the scalable context-aware algorithm to target the customer's tastes better, producing predictions as close to his preferences as possible. This new algorithm named KFCR uses the kernel mapping framework developed by (KMR) to complement context information. We evaluated and compared this new system to the Basic KMR, as well as to other widely used context-aware approaches like preand post-filtering techniques. Once evaluated, the KFCR was more accurate than both non-context-aware and context-aware approaches.

关键词： System kernels Accuracy Mood Filtering Scalability Decision making Prediction algorithms Market research Recommender systems Meteorology

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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A Soft Voting Classification Model for Network Traffic Prediction in VANET/V2X

A Soft Voting Classification Model for Network Traffic Predi...

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IEEE International Conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Parvin Ahmadi Doval Amiri Samuel Pierre Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory Polytechnique Montreal Montreal Canada Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory (LARIM) Polytechnique Montreal Montreal Canada

Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a promising solution to address network traffic in VANETs with V2X data. In this paper, we propose a soft voting classification model, which consists of hybrid supervised machine learning algorithms to predict traffic in the network. We evaluate the prediction performance of five well-known machine learning models and the proposed model based on various classification evaluation metrics. The simulation results show that the proposed network traffic prediction model performs better than other considered machine learning models in terms of accuracy (0.94%), time consumption (12.25 seconds) and AUROC (0.907) that proves its stability.

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Towards Trustworthy Aircraft Safety: Explainable AI for Accurate Incident and Accident Predictions 20th

Towards Trustworthy Aircraft Safety: Explainable AI for Accu...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Amin, Maryam Noor, Umara Fatima, Manahil Rashid, Zahid Altmann, Jörn Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul Korea Republic of College of Engineering Seoul National University Seoul Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul Korea Republic of

ISBN: (纸本)9783031812255

Despite technological advancements, ensuring aircraft safety remains a challenge, however, Machine learning (ML)-based approaches for predicting future incidents play a crucial role in addressing flight safety. As ML models increase in complexity, their decision-making process becomes less transparent, posing significant challenges to trustworthiness. While simpler models demonstrate lower accuracy, more intricate models such as deep neural networks achieve higher accuracy but sacrifice interpretability. In this study, we enhance trustworthiness in aircraft safety prediction by leveraging a dataset of past accidents and incidents to prevent similar accidents from occurring in the future. To achieve this, we apply Random Forest and Extreme Gradient Boosting models to classify different categories of aircraft incidents. Additionally, we apply two powerful explainable artificial intelligence (XAI) techniques: Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to provide insights into both local and global predictions made by the models. Notably, our results reveal high accuracy in these predictions while maintaining trustworthiness. This research contributes to the advancement of XAI and offers valuable insights for safety-critical applications and decision support systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft accidents

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Combinatorial Incentive Mechanism for Bundling Spatial Crowdsourcing with Unknown Utilities

Combinatorial Incentive Mechanism for Bundling Spatial Crowd...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Hengzhi Wang Laizhong Cui Lei Zhang Linfeng Shen Long Chen College of Computer Science and Software Engineering Shenzhen University China School of Computing Science Simon Fraser University Canada

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Incentive mechanisms in Spatial Crowdsourcing (SC) have been widely studied as they provide an effective way to motivate mobile workers to perform spatial tasks. Yet, most existing mechanisms only involve single tasks, neglecting the presence of complementarity and substitutability among tasks. This limits their effectiveness in practice cases. Motivated by this, we consider task bundles for incentive mechanism design and closely analyze the mutual exclusion effect that arises with task bundles. We then develop a combinatorial incentive mechanism, including three key policies: In the offline case, we propose a combinatorial assignment policy to address the conflict between mutual exclusion and assignment efficiency. We next study the conflict between mutual exclusion and truthfulness, and build a combinatorial pricing policy to pay winners that yields both incentive compatibility and individual rationality. In the online case with unknown workers’ utilities, we present an online combinatorial assignment policy that balances the exploration-exploitation trade-off under the mutual exclusion constraints. Through theoretical analysis and numerical simulations using real-world mobile networking datasets, we demonstrate the effectiveness of the proposed mechanism.

关键词： Crowdsourcing Mechanism design Pricing Numerical simulation Task analysis

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Super-exponential quantum advantage for finding the center of a sphere

arXiv

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arXiv 2024年

作者： Li, Guanzhong Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

This article considers the geometric problem of finding the center of a sphere in vector space over finite fields, given samples of random points on the sphere. We propose a quantum algorithm based on continuous-time quantum walks that needs only a constant number of samples to find the center. We also prove that any classical algorithm for the same task requires approximately as many samples as the dimension of the vector space, by a reduction to an old and basic algebraic result—Warning’s second theorem. Thus, a super-exponential quantum advantage is revealed for the first time for a natural and intuitive geometric problem. © 2024, CC BY.

关键词： Spheres

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Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

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Computers, Materials & Continua 2022年第11期73卷 2967-2984页

作者： Gulzar Ahmed Tahir Alyas Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science University of South AsiaLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,*** is the national language of Pakistan,and it is the fourth spoken language in the ***,it is still challenging to recognize Urdu handwritten characters owing to their cursive *** paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online *** research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this *** offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online ***,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text *** this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline ***,we carried out detailed experiments for character recognition,as detailed in the *** proposed CNN model outperformed as compared to previously published approaches.

关键词： Urdu handwritten text recognition handwritten dataset convolutional neural network artificial intelligence machine learning deep learning

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Implementation of Continuous-Time Quantum Walk on Sparse Graph

arXiv

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arXiv 2024年

作者： Chen, Zhaoyang Li, Guanzhong Li, Lvzhou Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

Continuous-time quantum walks (CTQWs) play a crucial role in quantum computing, especially for designing quantum algorithms. However, how to efficiently implement CTQWs is a challenging issue. In this paper, we study implementation of CTQWs on sparse graphs, i.e., constructing efficient quantum circuits for implementing the unitary operator e−iHt, where H = γA (γ is a constant and A corresponds to the adjacency matrix of a graph). Our result is, for a d-sparse graph with N vertices and evolution time t, we can approximate e−iHt by a quantum circuit with gate complexity (d3||H||tN log N)1+o(1), compared to the general Pauli decomposition, which scales like (||H||tN4 log N)1+o(1). For sparse graphs, for instance, d = O(1), we obtain a noticeable improvement. Interestingly, our technique is related to graph decomposition. More specifically, we decompose the graph into a union of star graphs, and correspondingly, the Hamiltonian H can be represented as the sum of some Hamiltonians Hj, where each e−iHjt is a CTQW on a star graph which can be implemented efficiently. © 2024, CC BY.

关键词： Quantum computers

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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