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International Conference on computer and Information Technology (ICCIT)

作者： Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Md. Zehan Alam Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Mathematics University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Diabetes, a potentially life-threatening condition if undetected, necessitates an early and accurate diagnosis. This study evaluates traditional machine learning (ML) models and deep neural networks (DNNs) for diabetes prediction, utilizing the Pima Indians Diabetes dataset (PIDD) and the Sylhet Diabetes Hospital dataset (SDHD). We optimized six ML models—Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, Support Vector Machines, and K-Nearest Neighbors—using GridSearchCV and ensemble learning, and deployed DNNs with varied train-test splits (90-10%, 80-20%, and 70-30%). Evaluations on accuracy, precision, recall, F1-score, and AUC-ROC highlighted RF’s outstanding performance after GridSearchCV on the SDHD with Accuracy: 98%, Precision: 100%, Recall: 96%, F1 Score: 98%, AUC: 100% and on PIDD with Accuracy: 76%, Precision: 63%, Recall: 80%, F1 Score:70%, AUC:81%. This underscores advanced ML’s effectiveness in handling complex datasets and enhancing early diabetes detection strategies.

关键词： Support vector machines Accuracy Hospitals Artificial neural networks Predictive models Prediction algorithms Diabetes Ensemble learning Regression tree analysis Random forests

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Bi-SIS Epidemics on Graphs - Quantitative Analysis of Coexistence Equilibria

arXiv

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arXiv 2022年

作者： Doshi, Vishwaraj Hu, Jie Eun, Do Young Data Science and Advanced Analytics team IQVIA India Department of Electrical and Computer Engineering NCSU United States

We consider a system in which two viruses of the Susceptible-Infected-Susceptible (SIS) type compete over general, overlaid graphs. While such systems have been the focus of many recent works, they have mostly been studied in the sense of convergence analysis, with no existing results quantifying the non-trivial coexistence equilibria (CE) - that is, when both competing viruses maintain long term presence over the network. In this paper, we prove monotonicity of the CE with respect to effective infection rates of the two viruses, and provide the first quantitative analysis of such equilibria in the form of upper bounds involving spectral radii of the underlying graphs, as well as positive equilibria of related single-virus systems. Our results provide deeper insight into how the long term infection probabilities are affected by system parameters, which we further highlight via numerical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Viruses

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Edge detection with multi-scale representation and refined Network

Edge detection with multi-scale representation and refined N...

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The 3rd International Conference on Distributed Sensing and Intelligent Systems (ICDSIS 2022)

作者： O. Elharrouss Y. Akbari S. Al-Maadeed A. Bouridane Department of Computer Science and Engineering Qatar University Qatar Center for Data Analytics and Cybersecurity University of Sharjah UAE

Edge detection is a representation of boundaries between objects and regions in an image. Due to the variations of types, scales, intensities as well as background, the detection of these boundaries represents a challenge for different computer vision algorithms. The exploitation of multi-scale representation using various deep learning backbones make edge detection more realistic. However, edge detection is still not effective in terms of erroneous detection in some scenarios and the quality of detected edges. In this paper, we attempted to overcome these challenges using a high-resolution and refined network by fusing the output of different scales of the network and the output of the first layers. The proposed network used horizontal and vertical blocks, while the horizontal block (HB) contains a set of convolution layers and the vertical block (VB) represent the different scale of the image using convolution and pooling layers. The output of each scale in VR is combined with the corresponding layer in HB. Then, we used an affined batch normalization layer as an erosion operation for the homogeneous region in the image before fusing these outputs for the final edge detection results. The proposed method is evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images.

关键词： computer vision deep learning (artificial intelligence) edge detection image fusion image representation image resolution

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Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study

Enhancing Transfer Learning for Medical Image Classification...

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International Conference on computer and Information Technology (ICCIT)

作者： Md Zehan Alam Tonmoy Roy H.M. Nahid Kawsar Iffat Rimi Department of Mathematics University of Dhaka Dhaka Bangladesh Department of Data Analytics and Information Systems Utah State University Logan Utah USA Department of Applied Mathematics Noakhali Science and Technology University Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL—using pre-trained models on the ImageNet dataset—varies due to domain-specific challenges. We evaluate five pre-trained models—MobileNet, Xception, InceptionV3, ResNet50, and DenseNet201—on two datasets: Brain Tumor MRI and APTOS 2019. Our results show that TL models excel in brain tumor classification, achieving near-optimal metrics. However, performance in diabetic retinopathy detection is hindered by class imbalance. To mitigate this, we integrate the Synthetic Minority Over-sampling Technique (SMOTE) with TL and traditional machine learning(ML) methods, which improves accuracy by 1.97%, recall (sensitivity) by 5.43%, and specificity by 0.72%. These findings underscore the need for combining TL with re-sampling techniques and ML methods to address data imbalance and enhance classification performance, offering a pathway to more accurate and reliable medical image analysis and improved patient outcomes with minimal extra computation powers.

关键词： Diabetic retinopathy Accuracy Sensitivity Transfer learning Brain tumors Brain modeling Reliability Medical diagnostic imaging Image classification Residual neural networks

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Private algorithms for stochastic saddle points and variational inequalities: beyond euclidean geometry 24

Private algorithms for stochastic saddle points and variatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Raef Bassily Cristóbal Guzmán Michael Menart Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute

ISBN: (纸本)9798331314385

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ε, δ)-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the ℓp/ℓq setup, p, q ∈ [1,2]. That is, we consider the case where the primal problem has an ℓp-setup (i.e., the primal parameter is constrained to an ℓp bounded domain and the loss is ℓp-Lipschitz with respect to the primal parameter) and the dual problem has an ℓq setup. Here, we obtain a bound of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$ on the strong SP-gap, where n is the number of samples and d is the dimension. This rate is nearly optimal for any p, q ∈ [1,2]. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when p = q = 2 (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of p and q and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever p, q ∈ [1, 2]. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider ℓp-setups, p ∈ [1, 2]. Here, we provide the first analysis which obtains a bound on the strong VI-gap of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$. For p - 1 = Ω(1), this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularizat

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Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks 2

Image Analysis of MRI-based Brain Tumor Classification and S...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Saranya, R. Pushpakumar, R. Srinivasan, P. Aparna, N. Karthika, K. Chauhan, Amit Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of ECE Sona College of Technology Tamilnadu Salem India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Tamil Nadu Veerappanur India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Karnataka Bengaluru India

ISBN: (纸本)9798350340235

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. © 2023 IEEE.

关键词： Brain Tumor Segmentation Central Nervous System (CNS) FuzzyClustering Method (FCM)

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Single-cell transcriptomics and epigenomics point to CD58-CD2 interaction in controlling primary melanoma growth and immunity

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Cancer Communications 2025年第4期45卷 465-470页

作者： Antonia Stubenvoll Maria Schmidt Johanna Moeller Max Alexander Lingner Chango Carolyn Schultz Olga Antoniadou Henry Loeffler-Wirth Stephan Bernhart Florian Groβe Beatrice Thier Annette Paschen Ulf Anderegg Jan CSimon Mirjana Ziemer Clara TSchoeder Hans Binder Manfred Kunz Department of Dermatology Venereology and AllergologyUniversity of Leipzig Medical CenterLeipzigGermany Interdisciplinary Center for Bioinformatics University of LeipzigLeipzigGermany Institute for Drug Discovery University of Leipzig Medical CenterLeipzigGermany Bioinformatics Group Department of Computer ScienceUniversity of LeipzigLeipzigGermany Department of Diagnostics Fraunhofer Institute of Cell Therapy and Immunology(IZI)LeipzigGermany Center for Scalable Data Analytics and Artificial Intelligence(ScaDS.AI)Dresden/Leipzig LeipzigGermany Department of Dermatology University Hospital EssenUniversity Duisburg-Essen and German Cancer Consortium(DKTK)Partner Site Essen/DüsseldorfEssenGermany

Immunotherapy is currently one of the most promising treatment options for malignant melanoma[1].To uncover new immunological targets for future treatment approaches,single-cell transcriptomic and epigenomic analyses ... 详细信息

关键词： malignant melanoma single cell transcriptomics melanocytic nevus immunity immunotherapy primary melanoma epigenomic analyses epigenomics cd cd interaction

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Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models

arXiv

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arXiv 2024年

作者： Song, Ruixin Spadon, Gabriel Pelot, Ronald Matwin, Stan Soares, Amilcar Department of Computer Science Memorial University of Newfoundland St. John’sNL Canada Institute for Big Data Analytics Dalhousie University HalifaxNS Canada Industrial Engineering Department Dalhousie University HalifaxNS Canada Institute of Computer Science Polish Academy of Sciences Warsaw Poland Department of Computer Science and Media Technology Linnaeus University Växjö Sweden

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV

BMA for the BMA: Boosting Mobility Analysis for the Bangkok ...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Poonnaphop Kujareanpaisal Rujira Mayhasap Pin Pin Tea-makorn Pavitra Jindahra Stefano Starita Nagul Cooharojananone Kanokwan Atchariyachanvanich Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Data Analytics Center Sasin School of Management Bangkok Thailand School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.

关键词： Visualization Pedestrians Head Surveillance Urban planning Transportation Cameras Personnel Object tracking Python

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Big data analytics for Quality Control in The Pharmaceutical Industry

Big Data Analytics for Quality Control in The Pharmaceutical...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P. Malathi S. Jansi M.V. Prabhakaran K. Senbagam M.P. Sujatha R Sadaieswaran Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer science and Engineering Acharya college of Engineering and Technology Achariyapuram Puducherry India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram Tamil Nadu India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Applications Tagore College of arts and science Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns.

关键词： Waste reduction Technological innovation data analysis Fault detection Quality control Big data Real-time systems Manufacturing Predictive analytics Pharmaceuticals

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