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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Wang, Han Niu, Wenlai Liu, Yahui Tan, Ye Xiu, Chunlian Chongqing University Of Technology College Of Computer Science And Engineering Chongqing China Chongqing University Of Technology School Of Vehicle Engineering Chongqing China Chongqing University Of Technology School Of Foreign Languages Chongqing China

ISBN: (纸本)9798350381665

The low-speed collision prevention warning system for intelligent vehicles is a critical safety technology aimed at reducing low-speed road collision accidents. This study introduces an intelligent vehicle low-speed collision prevention warning method based on digital twin technology and deep learning algorithms. Firstly, a digital twin model was constructed to simulate the vehicle and its surrounding environment, providing a better understanding of the road situation. Subsequently, convolutional neural networks (CNNs) were employed for object detection, identifying other vehicles and obstacles from video frames captured by the vehicle's front camera. On this basis, a spatial attention mechanism was introduced to reduce the impact of irrelevant targets on collision prediction, thereby improving the system's accuracy. Through motion tracking, collision prediction, and warning decision-making, the system can detect potential collision hazards and provide timely warnings to the driver, helping to reduce the occurrence of low-speed collision accidents. The experimental results indicate that the method has achieved good performance in various road scenarios, showing potential for playing a crucial role in the safety of intelligent vehicles and road safety. This study provides new insights and approaches for low-speed collision prevention technology in the field of intelligent vehicles. © 2024 IEEE.

关键词： Deep learning

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Automatic Unusual Activities Recognition Using Deep Learning in Academia

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computers, Materials & Continua 2022年第1期70卷 1829-1844页

作者： Muhammad Ramzan Adnan Abid Shahid Mahmood Awan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan School of Electronics Computing and MathematicsUniversity of DerbyDerbyUnited Kingdom

In the current era,automatic surveillance has become an active research problem due to its vast real-world applications,particularly for maintaining law and order.A continuous manual monitoring of human activities is a tedious *** use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few *** computer vision techniques have been applied for observation and surveillance of real-world *** research study focuses on detecting and recognizing unusual activities in an academic situation such as examinationhalls,which may help the invigilators observe and restrict the students from cheating or using *** the best of our knowledge,this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual *** proposed model has been named Automatic Unusual Activity Recognition(AUAR),which employsmotion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse *** evaluation using standard performancemeasures confirmthat theAUARmodel outperforms the already proposed approaches for unusual activity *** from evaluating the proposed model on the examination dataset,we also apply AUAR on Violent and Movies datasets,widely used in the relevant literature to detect suspicious *** results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

关键词： Deep learning unusual activities examination CNN surveillance human activity recognition

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Multimodal Medical Image Feature Representation and Fusion for AD Early Diagnosis

Multimodal Medical Image Feature Representation and Fusion f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qu, Jing Qu, Ying Bai, Xueliang Xu, Lanxin Wang, Han Liu, Guixia Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China North China University of Science and Technology College of Science Tangshan China Institute of Computational Biology Northeast Normal University School of Information Science and Technology Changchun China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer's disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression. © 2024 IEEE.

关键词： Neuroimaging

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Cross-Domain Heterogeneous Hyperspectral Image Classification Based on Meta-Learning with Task-Adaptive Loss Function 13

Cross-Domain Heterogeneous Hyperspectral Image Classificatio...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Jin, Yuheng Ye, Minchao Xiong, Fengchao Qian, Yuntao China Jiliang University College of Information Engineering China Nanjing University of Science and Technology School of Computer Science and Engineering China Zhejiang University College of Computer Science China

ISBN: (纸本)9798350395570

The scarcity of labeled samples poses a significant challenge in hyperspectral image (HSI) classification. Cross-domain classification offers a potential solution by leveraging information from a source domain with abundant labeled samples to classify the target domain, even when only limited labeled samples are available. Our work is to classify the target domain where there are classes that are not in the source domain, which is a difficult task. This paper presents a method called cross-domain meta-learning with task-adaptive loss function (CD-MTALF) for heterogeneous HSI classification. CD-MTALF is a transfer learning-based method specifically designed to address the issue of limited labeled samples in the target domain. CD-MTALF adopts a meta-learning approach based on optimization. It consists of two optimization tasks: 1) Inner-loop optimization: adapting the base learner to a new task using a learning algorithm. 2) Outer-loop optimization: training the base learner, meta-learner, and meta-learnable loss function and employing an adaptive weighting strategy to balance the losses between the source and target domains. CD-MTALF aims to enhance the model's generalization performance by incorporating both inner-loop and outer-loop optimizations. Experiments on publicly available HSI datasets demonstrate the efficacy of CD-MTALF in improving classification performance. © 2023 IEEE.

关键词： Classification (of information)

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Pesticide Usage Forecasting for Agriculture using Prophet Model 5

Pesticide Usage Forecasting for Agriculture using Prophet Mo...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Wani, Afshan Hassan Moharana, Bhimasen Samant, Tapaswini De, Utpal Chandra Dey, Snehasis Banerjee, Shobhan Department of Computer Science and Engineering Lovely Professional University Punjab India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India College of Engineering Bhubaneswar India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350361155

Agriculture plays a vital role in our day-to-day life for the life sustenance of not just human beings but also various domestic and livestock animals. In addition to this, agriculture also contributes significantly to the economy of a country, impacting its growth across the globe. India had always been an agriculture-dominated country before the companies from the West started investing in India. But alongside agriculture come various challenges such as infestation by insects or weeds which eventually lead to the damage of crops and even diseases. But pesticides can even contribute to the contamination of soil, water, and other vegetation in areas where they are been used. In this paper, we have used the Prophet model to study the use of pesticides and analyze the trends of usage of organic pesticides over inorganic pesticides. © 2024 IEEE.

关键词： Pesticides

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Weber Law Based Approach for Multi-Class Image Forgery Detection

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computers, Materials & Continua 2024年第1期78卷 145-166页

作者： Arslan Akram Javed Rashid Arfan Jaffar Fahima Hajjej Waseem Iqbal Nadeem Sarwar Department of Computer Science Superior UniversityLahore54000Pakistan Information Technology Services University of OkaraOkara56300Pakistan Departmet of Computer Science MLC LabOkara56300Pakistan Department of CS&SE International Islamic UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science Bahria UniversityLahore CampusLahore54600Pakistan

Today’s forensic science introduces a new research area for digital image analysis formultimedia ***,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered *** forgery detectionmethods can classify only one of the most widely used Copy-Move and splicing ***,an image can contain one or more types of *** study has proposed a hybridmethod for classifying Copy-Move and splicing images using texture information of images in the spatial ***,images are divided into equal blocks to get scale-invariant *** law has been used for getting texture features,and finally,XGBOOST is used to classify both Copy-Move and splicing *** proposed method classified three types of forgeries,i.e.,splicing,Copy-Move,and ***(CASIA 2.0,MICCF200)and RCMFD datasets are used for training and *** average,the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation,which is far better than existing methods.

关键词： Copy-Move and splicing non-overlapping block division texture features weber law spatial domain xgboost

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A Hybrid Firefly with Dynamic Multi-swarm Particle Swarm Optimization for WSN Deployment

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Journal of Internet technology 2023年第4期24卷 825-836页

作者： Chang, Wei-Yan Soma, Prathibha Chen, Huan Chang, Hsuan Tsai, Chun-Wei Department of Computer Science and Engineering National Chung Hsing University Taiwan Department of Information Technology Sri Sai Ram Engineering College India Department of Computer Science and Engineering National Sun Yat-Sen University Taiwan

Enhancing the coverage area of the sensing range with the limiting resource is a critical problem in the wireless sensor network (WSN). Mobile sensors are patched coverage holes and they also have limited energy to move in large distances. Several recent studies indicated the metaheuristic algorithms can find an acceptable deployed solution in a reasonable time, especially the PSO-based algorithm. However, the speeds of convergence of most PSO-based algorithms are too fast which will lead to the premature problem to degrade the quality of deployed performance in WSN. A hybrid metaheuristic combined with dynamic multi-swarm particle swarm optimization and firefly algorithm will be presented in this paper to find an acceptable deployed solution with the maximum coverage rate and minimum energy consumption via static and mobile sensors. Moreover, a novel switch search mechanism between sub-swarms will also be presented for the proposed algorithm to avoid fall into local optimal in early convergence process. The simulation results show that the proposed method can obtain better solutions than other PSO-based deployment algorithms compared in this paper in terms of coverage rate and energy consumption. © 2023 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Energy utilization

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Semi-Supervised Carotid Plaque Image Classification Using Feature Correction and Pseudo-Label Balance Correction

Semi-Supervised Carotid Plaque Image Classification Using Fe...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Yu, Wenjie Gan, Weiyan Wang, Furong Yang, Zhi Shi, Ming Zhou, Ran School of Computer Science Hubei University of Technology Wuhan China Tongji Medical College Huazhong University of Science and Technology Liyuan Hospital Wuhan China

ISBN: (纸本)9798350337020

Carotid plaque classification is of great significance for the diagnosis and treatment of carotid artery disease and the prediction of ischemic stroke. However, the ultrasound image structure of carotid plaques is complex, and manual labeling is time-consuming, which results in lots of unlabeled images. Semi-supervised methods can be well applied in this field by using unlabeled samples to improve model performance. Most of the existing semi-supervised methods are based on pseudo-labels and consistency regularization;however, these kinds of methods will cause error pseudo-labels during sample selection and an imbalanced distribution of pseudo-labels due to cognitive biases in model training. To solve these problems, We propose a method based on feature correction and pseudo-label balance correction, which utilizes a semi-supervised approach. The feature correction module uses the features of labeled data to correct the predicted probabilities of unlabeled data and improve the discrimination of pseudo-labels. The pseudo-label balance correction module can dynamically adjust the sample weight in the loss function according to the number of pseudo-labels to alleviate the problem of data imbalance. Evaluated on 1270 ultrasound carotid plaque images, our method achieved superior performance to other state-of-the-art methods (i.e., Mixmatch, Fixmatch, and Flexmatch) on 10%, 30%, and 50% labeled training data. Our method demonstrated accurate carotid plaque classification using a small number of labeled training datasets, potentially benefiting clinical practice and trials. © 2023 IEEE.

关键词： Carotid plaques Pseudo-labels Semi-supervised learning

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An empirical study to test the significant effect of bellwethers on predictive modeling in smart homes

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Journal of Data, Information and Management 2024年第2期6卷 111-123页

作者： Mensah, Solomon Kudjo, Patrick Kwaku Brown, Selasie Aformaley Dick, Elizabeth Akuafum Koffie-Ocloo, Dzordzoe Woelorm Department of Computer Science University of Ghana Legon Accra Ghana Department of Information Technology Wisconsin International University College Accra Ghana Department of Information Technology University of Professional Studies Accra Ghana Department of Information Technology Ghana Communication Technology University Accra Ghana

Smart homes are envisioned and expected to be furnished with effective and reliable sensors for constant monitoring, sensing, and actuating the environment. Data from these sensors can be analyzed and used for the provision of various services by automating common tasks undertaken in the homes with minimal disruption to human activities. Thus, in order to provide these services, there is the need to obtain an intelligent system to accurately predict the most likely event to occur in a smart home. These intelligent systems rely on prediction algorithms trained from existing data for their functioning. The study seeks to invesigate the feasibility and significance of using Bellwethers for predictive modelling in smart homes. Recent works in the domain of software engineering – software defect and effort estimation have shown that the use of exemplary data (referred to as Bellwether) in the training set relatively improves prediction accuracy. These results were obtained based on an empirical analysis of the Bellwether effect i.e., the existence of Bellwethers in a given dataset. Existing studies in the context of smart homes focus on using all data from various sensors for training and validating the prediction algorithms for intelligent systems in smart homes. This mode of training and validating the prediction algorithms does not always yield accurate prediction results due to the variations and heterogeneous nature of the population dataset. A novel framework based on Markov chains and Deep learning algorithms is constructed to sample Bellwethers for predictive modelling in smart homes. It was observed that the sampled Bellwethers yielded improved prediction accuracy for the studied dataset as compared to using all data instances or population set (growing portfolio). It was evident that Bellwethers can improve the reliability and accuracy of intelligent systems built for smart homes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bellwether Intelligent systems Internet of Things Prediction Smart Homes

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