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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Dadashkarimi, Javid Rosenblatt, Matthew Karbasi, Amin Scheinost, Dustin Yale University Department of Computer Science New Haven United States Yale University Department of Biomedical Engineering New Haven United States Yale University Department of Electrical Engineering Department of Computer Science Department of Statistics and Data Science New Haven United States Yale School of Medicine Department of Radiology and Biomedical Imaging New Haven United States

ISBN: (纸本)9781665451819

Connectomics is a popular approach for under-standing the brain with neuroimaging data. However, a con-nectome generated from one atlas is different in size, topology, and scale compared to a connectome generated from another. Consequently, connectomes generated from different atlases can-not be used in the same analysis. This limitation hinders efforts toward increasing sample size and demonstrating generalizability across datasets. Recently, we proposed Cross Atlas Remapping via Optimal Transport (CAROT) to find a spatial mapping between a pair of atlases based on a set of training data. The mapping transforms timeseries fMRI data parcellated with an atlas to form a connectome based on a different one. Crucially, CAROT does not need raw fMRI data and thus does not require re-processing, which can otherwise be time-consuming and expensive. The current CAROT implementation leverages information from several source atlases to create robust mappings for a target atlas. In this work, we extend CAROT to combine existing mappings between a source and target atlas for an arbitrary number of mappings. This extension (labeled Stacking CAROT) allows mappings between a pair of atlases to be created once and re-used with other pre-trained mappings to create new mappings as needed. Reconstructed connectomes from Stacking CAROT perform as well as those from CAROT in downstream analyses. Importantly, Stacking CAROT significantly reduces training time and storage requirements compared to CAROT. Overall, Stacking CAROT improves previous versions of CAROT. © 2023 IEEE.

关键词： Mapping

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Exploring Sub-Nyquist Sampling for Fine Image Reconstruction 2

Exploring Sub-Nyquist Sampling for Fine Image Reconstruction

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Nithya, M. Satheeshkumar, S. Dakshana, M. Gunalan, K. Anandan, P. Saveetha, R. Bannari Amman Institute of Technology Department of Computer Technology Tamilnadu India Bannari Amman Institute of Technology Department of Artificial intelligence and data science Tamilnadu India Sathyabama Institute of Science and Technology Department of Physics Tamilnadu India

ISBN: (纸本)9798350368413

Compared to traditional methods, compressive sensing, a signal processing technique, allows for the effective acquisition and reconstruction of signals. A rate that is less than twice the highest frequency component of a signal is known as subNyquist sampling, allows for the digitization of wideband frequency-domain sparse signals without compromising reconstruction quality when employing compression sensing (CS). According to the CS framework, samples taken at sub- Nyquist sampling rates can be used to reconstruct signals with sparse representations in a known basis. Because of its inherent qualities, the Fourier domain is frequently used in compressive sensing applications. Use of a small number of Fourier transform coefficients in sparse signal recovery applications has led to successful resolutions of large-scale data recovery issues, including image recovery. During the measurement phase, input is systematically measured in blocks to obtain the necessary data, while in the recovery phase, all measurements from a single image are simultaneously utilized to reconstruct the complete image. © 2024 IEEE.

关键词： Compressed sensing

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Wheat Disease Recognition Based on a Fusion Model with Improved Spatial and Channel Reconstruction Convolution Combined with Contextual Transformer 5

Wheat Disease Recognition Based on a Fusion Model with Impro...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Yang, Zicong Jin, Aitong Wang, Junxi Xie, Haifan Zhan, Xin Li, Qiaolin Zhuhai College of Science and Technology Department of Computer Zhuhai China Zhuhai College of Science and Technology Department of Statistics and Data Science Zhuhai China Zhuhai College of Science and Technology Department of Mechanical Engineering Zhuhai China Fujian University of Traditional Chinese Medicine School of Traditional Chinese Medicine Fuzhou China

ISBN: (纸本)9798350373820

Crop diseases cause loss of agricultural products and have a critical impact on society, the economy, and the environment, so it is crucial for early defending the wheat disease. However, seeking an expert diagnosis can contribute to delays and higher costs. Therefore, this paper proposes a deep learning neural network with improved spatial and channel reconstruction convolution combined with CoT Attention module, named SCCoT-WheatNet. This innovated neural network can recognize wheat disease. The experimental dataset was obtained from the Internet and some publicly available datasets using web crawler was technology. This modification is based on the VGG13 model. In the final two sections of the VGG13 model, feature extraction replaces conventional convolutional layers with Contextual Transformer module and spatial and channel reconstruction convolution block. The experiment adopts a cross-fold approach. In the first fold test, the results show that the average recognition accuracy of the SCCoT-WheatNet network is 9 6. 9 4%, which is higher than the classical VGG network and the Vision Transformer models of the last two years. © 2024 IEEE.

关键词： Convolution

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2024年 1-27页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Designing an Energy Tracker and Smart Home Solutions Using Design Thinking Methodology 10

Designing an Energy Tracker and Smart Home Solutions Using D...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hiererra, Siti Elda Chandra, Victor Ivana, Jane Wibowo, Jessica Lynn Asadi, Faisal School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Business Analytics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

As energy consumption rises, finding ways to reduce energy usage in homes is crucial. This paper presents a case study using design thinking to create a daily energy tracker that helps users reduce energy consumption. We conduct user research, define the problem, brainstorm solutions (mobile app, smart thermostat, smart plug, physical tracker), and test prototypes. A mobile app that tracks energy usage in real-time and offers personalized tips emerges as the most promising solution. We develop and launch the app, partnering with an energy company. This case study showcases the effectiveness of design thinking in creating a user-centered solution for reducing energy consumption. © 2023 IEEE.

关键词： Energy utilization

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LGMD-based Neural Network for Detecting Abnormal Velocity Targets in Moving Crowd 9

LGMD-based Neural Network for Detecting Abnormal Velocity Ta...

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9th International Symposium on computer and Information Processing Technology, ISCIPT 2024

作者： Zhao, Zhongxiang Hu, Bin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University College of Computer Science & Technology Department of Computer Science Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (纸本)9798350388404

Detecting abnormal velocity targets is critical especially in public surveillance for crowd activity monitoring. Although there are some explorations on one such issue, traditional methods still perform poorly in complex scenes. In this paper, a novel visual neural network is investigated to perceive abnormal velocity targets in moving crowd, based on the latest neurophysiological achievements revealed in locusts' vision systems. The proposed neural network contains two neural counterparts, i.e., the presynaptic and the postsynaptic networks. The former one receives visual signals and processes them to capture the motion cues of different targets, and the latter one filters the salience energies to perceive abnormal velocity targets in the field of view. Numerical experiments carried out show that the proposed neural network can effectively detect abnormal velocity targets in moving crowd. This study is an important step towards dynamic visual information processing in crowd behavior analysis. © 2024 IEEE.

关键词： Visual servoing

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter

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Procedia computer science 2018年 135卷 579-586页

作者： Kahlil Muchtar Faris Rahman Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges.

关键词： change detection foreground segmentation texture features

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Optimizing Blister Detection With CNN-based Infrared Shape Recognition 2

Optimizing Blister Detection With CNN-based Infrared Shape R...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Dakshana, M. Gunalan, K. Anandan, P. Saveetha, R. Nithya, M. Satheeshkumar, S. Sathyabama Institute of Science and Technology Department of Physics Tamilnadu India Bannari Amman Institute of Technology Department of Computer Technology Tamilnadu India Bannari Amman Institute of Technology Department of Artificial intelligence and data science Tamilnadu India

ISBN: (纸本)9798350368413

The present research employs texture analysis and a Convolutional Neural Network to discover anomalous temperature patterns related with plantar foot ulcers in diabetes patients. Diabetic foot ulcers are a serious health danger if not managed. The suggested approach distinguishes diabetic foot ulcers from healthy tissue based on their distinct textural properties. The study tries to accurately detect these ulcers based on their individual thermal textures by using multiple layers within a CNN model. The suggested method's efficacy is assessed and compared to existing methodologies for identifying diabetic foot ulcers, revealing the possibility for earlier diagnosis and care in this essential health problem. © 2024 IEEE.

关键词： Convolutional neural networks

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An Ambiguous Edge Detection Method for Computed Tomography Scans of Coronavirus Disease 2019 Cases

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2024年第1期54卷 352-364页

作者： Singh, Pritpal Huang, Yo-Ping Central University of Rajasthan Quantum Optimization Research Lab Department of Data Science and Analytics Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Magong88046 Taiwan The Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan The Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan The Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

Recently, the coronavirus disease of 2019 (COVID-19), as named by the World Health Organization (WHO), has spread to over 200 countries. The WHO has declared this disease as a worldwide public health emergency. One of the most difficult tasks in combating this epidemic is to identify and segregate the afflicted people. The reverse transcription-polymerase chain reaction test (RT-PCR) is the most common pathology test used to diagnose this infection. Studies show that the RT-PCR test has a low-positive rate and sometimes becomes ineffective in diagnosing infection. In some cases, computed tomography (CT) scans reveal acute pneumonia and pulmonary anomalies. Therefore, CT scans are used together with RT-PCR tests to confirm infected people. Existing artificial intelligence and machine learning techniques require a large number of CT scans for training, which is a time-consuming process. Visual inspection shows that most CT scans of COVID-19 cases have broken, blurred, and ambiguous edges for infectious areas. Another major issue with these images is the heterogeneous intensity of the pixels, high noise, and low resolution. As a result of all these issues, the problem of effective edges/boundaries of various areas of CT scans of COVID-19 cases cannot be resolved by the current edge detection approach. Indeed, improper selection of edges can lead to an incorrect diagnosis of diseases through CT scans of COVID-19 cases. Therefore, there is an urgent need for a diagnostic method in addition to the RT-PCR test that can extract useful information from the minimum number of chest CT scans of suspected COVID-19 cases. This study introduces a new ambiguous edge detection method (AEDM) for identifying the edges/boundaries of different regions in CT scans of COVID-19 cases. The proposed AEDM is developed on the basis of ambiguous set (AS) theory, which is highly efficient in processing ambiguous pixel information. For simulation purposes, various CT scans of COVID-19 cases are c

关键词： Coronavirus

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Richie Muljana Syauqi Akmal Deffansyah Hari Andrew Alexander Agung Santoso Gunawan Edy Irwansyah Fabian Surya Pramudya Muhamad Keenan Ario Muhammad Edo Syahputra Said Achmad Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard’s primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang’s districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development.

关键词： Waste management Satellites Local government Urban areas data visualization Systems architecture Production Real-time systems Geospatial analysis Artificial intelligence

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