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检索条件"任意字段=1st International Conference on Soft Computing in Data Science, SCDS 2015"

共 386 条记录，以下是1-10 订阅

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1st international conference on soft computing in data science, scds 2015

1st International Conference on Soft Computing in Data Scien...

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1st international conference on soft computing in data science, scds 2015

ISBN: (纸本)9789812879356

The proceedings contain 25 papers. The special focus in this conference is on data Mining and Fuzzy computing. The topics include: An improved particle swarm optimization via velocity-based reinitialization for feature selection;classifying forum questions using PCA and machine learning for improving online CQA;data projection effects in frequent itemsets mining;data quality issues in data migration;reviewing classification approaches in sentiment analysis;possibility vague soft expert set theory and its application in decision making;an iterative method for solving fuzzy fractional differential equations;contrast comparison of flat electroencephalography image;an autocatalytic model of a pressurized water reactor in a nuclear power generation;selfish gene image segmentation algorithm;solving curriculum based course timetabling by hybridizing local search based method within harmony search algorithm;a parallel latent semantic indexing (LSI) algorithm for malay hadith translated document retrieval;short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine;implementation of dynamic traffic routing for traffic congestion;a comparative study of video coding standard performance via local area network;partial differential equation (PDE) based image smoothing system for digital radiographic image;main structure of handwritten jawi sub-word representation using numeric code;evaluating the usability of homestay websites in Malaysia using automated tools;humanoid-robot intervention for children with autism and accuracy assessment of urban growth pattern classification methods using confusion matrix and roc analysis.

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Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability 1

Using Recurrent Neural Networks to Forecast Climate Change: ...

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1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rathore, Nitasha Anubha Singh, Kuljeet Kaur, Gaganpreet Sharma, Chetna Sinha, Anurag Bharatiya Vidyapeeth College of Engineering Department of Computer Science and Engineering New Delhi India KIET Group of Institutions India School of Sciences Christ University Department of Computer Science Delhi NCR India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India IGNOU Department of Computer science New Delhi India

ISBN: (纸本)9798331518578

Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Enhanced Deep Learning Techniques for Multimodal Medical Image Fusion 1

Enhanced Deep Learning Techniques for Multimodal Medical Ima...

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1st IEEE international conference on computing and data science, ICCDS 2024

作者： Sheba, M. Vanitha Sadagopan, S. School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350365337

Multimodal Medical Image Fusion is the process of integrating data from various imaging modalities to produce a full and improved analysis of a patient's medical condition. MRI, CT, and PE T scans are examples of medical imaging modalities that can be used to produce a more thorough and informative image for better disease monitoring, treatment planning, and diagnosis. Because deep learning can understand the links between the input modalities and the intended fused image, it has become a potent tool for medical image fusion. Deep learning is a subfield of artificial intelligence. Computed tomography (CT) and magnetic resonance imaging (MRI) are two popular medical imaging technologies that provide complementary insights into the human body's internal components. While CT employs X-rays to provide cross-sectional images of bones, tissues, and organs with high spatial resolution and quick imaging, MRI uses magnetic fields and radio waves to make detailed images of soft tissues and organs with great contrast resolution. This paper explains a few of the deep learning techniques like DeepFuse technique, ResN et architecture, Generative Adversarial Networks (GANs), Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) model, Autoencoders, and Attention mechanics are some of the deep learning methods for MRI and CT image fusion that are currently in use. Better visualization and delineation of anatomical features are made possible by these deep learning-based fusion approaches, which eventually improve patient diagnosis, treatment, and overall health care results. © 2024 IEEE.

关键词： Magnetic resonance imaging

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An Algorithmic Approach Using Fuzzy soft Sets in Decision- Making for Patient-Specific Diagnosis

An Algorithmic Approach Using Fuzzy Soft Sets in Decision- M...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Singh, Rashmi Wangmo, Dechen Kaushik, Keshav Chaudhary, Aryan Amity Institute of Applied Science Amity University Noida India University of Petroleum and Energy Studies Computer Science Dehradun India Bio Tech Sphere Research India Kolkata India

ISBN: (纸本)9798350376470

Fuzzy soft set (FSS) theory merges the strength of fuzzy sets (FS) and soft sets (SS) to effectively address uncertainty and vagueness in decision- making (DM) processes. It offers a systematic approach to address complex decision problems and is particularly valuable in today's data driven and uncertain world. There are numerous DM algorithms utilising FSSs. However, no prior work had employed multiple algorithms, provided algorithmic variation at each stage, and conducted a comparison analysis of the algorithms as an approach to a decision-making in a specific scenario. The current article is crafted to address these gaps. This study presents FSS theory-based algorithms for patient-disease diagnosis. In a hypothetical case study, a sequential foundational work had been set up by designating universal patient, symptom, and disease sets. The patient-symptom and symptom-disease relationships are represented using FSSs. The severity weights designated to symptoms are utilised to create a table detailing symptom severity. The sum-product composition is then applied to acquire the patient-disease diagnosis relation table, which is then defuzzified using the maximum membership principle (1st algorithm) to assign patients to the disease based on highest membership value. On the same patient-disease diagnosis output, the next algorithm introduced a modified approach in which three sets of values are constructed for each element. Triangular fuzzy numbers are used for defuzzification, and the disease suffered by each patient is indicated by highest average membership value in the row. The third algorithm incorporated disease severity and intensity-based weights. For defuzzification, the weighted average method is utilized, wherein the weighted average of membership values and allotted weights determines the disease suffered by each individual patient. The fourth algorithm identified the maximum membership value of each patient and used mean of maxima for defuzzification. We,

关键词： Fuzzy set theory

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A Two-stage Self-Supervised Learning Framework for Automated Whole Heart Segmentation in CT and MRI: Addressing Challenges in Cardiac Imaging 1st

A Two-Stage Self-Supervised Learning Framework for Automated...

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1st MICCAI Challenge Comprehensive Analysis and computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Qayyum, Abdul Mazher, Moona Niederer, steven A. Faculty of Medicine National Heart and Lung Institute Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031870088

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, highlighting the need for precise diagnostic and therapeutic strategies. Whole heart segmentation (WHS) from medical images is vital for understanding cardiac anatomy, assessing disease progression, and guiding treatments. However, WHS poses significant challenges due to the dynamic shape changes of the heart during the cardiac cycle, motion artifacts, low contrast-to-noise ratio, and variability from multi-center data. Additionally, CT and MRI modalities each present unique difficulties: CT provides high spatial resolution but poor soft-tissue contrast, while MRI offers excellent soft-tissue contrast but is prone to artifacts. To address these challenges, we propose a two-stage method. In the first stage, the DINOv2 framework leverages a 3D Vision-LstM (xLstM) model for self-supervised learning on the limited, unlabeled WHS++ dataset to overcome the scarcity of annotated data. In the second stage, we fine-tune the model for segmentation by freezing the encoder and optimizing the decoder. Our approach is rigorously evaluated on the WHS++ dataset for both CT and MRI modalities, demonstrating superior performance compared to state-of-the-art deep learning models in accurately segmenting cardiac structures. Our proposed solution achieved an average Dice Coefficient of 0.9414 using CT and 0.8891 using MRI dataset. This work advances the field of cardiac imaging by providing a robust, generalizable solution for automated whole heart segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computerized tomography

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A Comprehensive study and Implementation of the Mock Interview Simulator with AI and Pose-Based Interaction 1

A Comprehensive Study and Implementation of the Mock Intervi...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Jadhav, Balasaheb Sawant, Avadhut Shah, Arnav Vemula, Pranamya Waikar, Abhijeet Yadav, Srushti Artificial Intelligence and Data Science Vishwakarma Institute Of Technology Pune India

ISBN: (纸本)9798350361780

The contemporary job market is increasingly competitive, placing a premium on a combination of technical prowess and essential soft skills. The ability to navigate job interviews effectively has emerged as a decisive factor in securing coveted positions. Recognizing this imperative, our contribution to the field is a pioneering solution - the Mock Interview Simulator. This simulator stands as a transformative tool in the landscape of job preparation, offering users an unprecedented advantage. In the current job market milieu, the Mock Interview Simulator redefines conventional preparation methods, providing a revolutionary approach for job seekers. This innovative platform leverages state-of-the-art technologies to empower users to tailor their interview experiences. The simulator introduces AI-driven interviewers, delivering a personalized and dynamic environment. This not only prepares candidates for a diverse range of interview scenarios but also imparts valuable insights into their performance. This paper delves into the intricate design and functionality of our Mock Interview Simulator, elucidating how cutting-edge technologies converge to create a realistic and highly beneficial interview preparation experience. By amalgamating speech recognition, text-to-speech synthesis, and the OpenAI GPT-3.5 Turbo API, the simulator crafts a lifelike interview setting. Additionally, posture detection using the Mediapipe framework adds a physical dimension, ensuring candidates not only articulate their responses effectively but also present themselves professionally. © 2024 IEEE.

关键词： Commerce

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Analysis of the Mechanical Behavior of Rammed Earth Mixes Using soft computing Methods with an Emphasis on Sustainability 1st

Analysis of the Mechanical Behavior of Rammed Earth Mixes Us...

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1st international conference on Net-Zero Built Environment: Innovations in Materials, structures, and Management Practices, NTZR 2024

作者： Baibordy, Aryan Yekrangnia, Mohammad Khodabakhshian, Fatemeh Shahid Rajaee Teacher Training University Tehran Iran

ISBN: (纸本)9783031696251

The construction industry is expected to be transformed due to its significant contribution to the consumption of natural resources, emissions, and solid waste. Despite its slow progression in achieving sustainable development goals (SDGs), in recent years, engineers have been adopting several techniques to address sustainability issues. Among the available sustainable construction techniques, the utilization of earthen materials, particularly rammed earth, which has been adapted from historical practices, has attracted significant attention from the public. Notwithstanding being recognized as a sustainable technique, its compressive strength is not on par with certain applicable construction materials, especially concrete. Therefore, its compressive strength remains a controversial issue. In the first place, this study performs a sentiment analysis using an NLP-based tool to assess the public’s comments and sentiments toward implementing sustainable techniques in the construction industry. In the second place, through a bibliometric study, the trends and gaps in rammed earth research are studied. In the third place, this research article employs soft computing methods such as ANN and DT and data science principles so as to predict and analyze the mechanical behavior, particularly compressive strength, of both unstabilized- and stabilized-rammed earth materials. Finally, the results are compared and critically discussed with an emphasis on sustainability. Overall, this article offers valuable insights into gauging public perception of sustainable construction methods such as rammed earth and exploring the practicality of computer-aided modeling for predicting the mechanical behavior of rammed earth mixtures. © The Author(s) 2025.

关键词： Sustainable development goals

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MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble 1st

MRI-Based Head and Neck Tumor Segmentation Using nnU-Net wit...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Mol, Frank N. Hoek, Luuk van der Ma, Baoqiang Nagam, Bharath Chowdhary Sijtsema, Nanna M. van Dijk, Lisanne V. Bunte, Kerstin Vlijm, Rifka Van Ooijen, Peter M. A. Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands University of Groningen University Medical Center Groningen Groningen9700 RB Netherlands

ISBN: (纸本)9783031832734

The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment planning. The Head and Neck Tumor Segmentation for MR-Guided Applications chal- lenge (HNTSMRG-24) comprises two tasks: segmentation of primary gross tumor volume (GTVp) and metastatic lymph nodes (GTVn) on T2- weighted MRI volumes obtained at (1) pre-radiotherapy (pre-RT) and (2) mid-radiotherapy (mid-RT). The training dataset consists of data from 150 patients, including MRI volumes of pre-RT, mid-RT, and pre-RT registered to the corresponding mid-RT volumes. Each MRI volume is accompanied by a label mask, generated by merging independent annotations from a minimum of three experts. For both tasks, we propose adopt- ing the nnU-Net V2 framework by the use of a 15-fold cross-validation ensemble instead of the standard number of 5 folds for increased robust- ness and variability. For pre-RT segmentation, we augmented the initial training data (150 pre-RT volumes and masks) with the corresponding mid-RT data. For mid-RT segmentation, we opted for a three-channel input, which, in addition to the mid-RT MRI volume, comprises the registered pre-RT MRI volume and the corresponding mask. The mean of the aggregated Dice Similarity Coefficient for GTVp and GTVn is computed on a blind test set and determines the quality of the proposed methods. These metrics determine the final ranking of methods for both tasks separately. The final blind testing (50 patients) of the methods proposed by our team, RUG_UMCG, resulted in an aggregated Dice Similarity Coefficient of 0.81 (0.77 for GTVp and 0.85 for GTVn) for Task 1 and 0.70 (0.54 for GTVp and 0.86 for GTVn) for Task 2. © The Author(s) 2025.

关键词： Image segmentation

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Towards a Sustainable Future: Analyzing Water Quality Changes for Environmental Sustainability 1

Towards a Sustainable Future: Analyzing Water Quality Change...

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1st IEEE international conference on Smart Technology, ICE-SMARTec 2023

作者： Mansor, Nur Suhaili Awang, Hapini Harun, Nor Hazlyna Hassan, Mohamad Ghozali Shehu Malami, Sarkin Tudu Shamsuddin, Noraishah Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Sintok Malaysia School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia Sokoto State University Faculty of Science Department of Computer Science and Information Technology Sokoto Nigeria Ici in Smart Manufacturing Sirim Bhd Shah Alam Malaysia

ISBN: (纸本)9798350306156

The development of industrial areas is frequently thought to have a good impact on society because it helps to meet the population's requirements. However, it also negatively impacts the environment, such as river pollution. If a country experiences rapid economic growth without considering the effects of industrial development on the environment, then human well-being will be affected. In this context, advanced development is attained by a developed nation while ensuring the well-being of society. Uncontrolled and unsustainable development increases environmental pollution, adversely affecting human health. As water is essential for human life, water resources, such as rivers, must be properly managed and protected to ensure human well-being. Thus, the study conducted on the changes in the water quality of the Kim Kim River provides insights into how unintended consequences of urban and industrial development can be identified and addressed using three sets of satellite imagery data from 2015, 2019 and 2020. The study was led to identify research problems and achieve the study objectives to obtain research results. The results of image classification analyses indicate that the development area near Sungai Kim Kim increased to 50%, with 38% from industrial areas. The RMSE for the image classification is 1.51, significantly better than the minimum allowed value of 2.00. It provides an understanding of the change in river water quality because of urban and industrial development. By highlighting the importance of sustainable development and the need to balance economic growth with environmental protection, this study contributes to the development of effective policies and strategies for the sustainable development of industrial areas. © 2023 IEEE.

关键词： Water quality

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Classification of Symptoms for Malaria using soft computing Method 1

Classification of Symptoms for Malaria using Soft Computing ...

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1st international conference on Machine Learning, Computer Systems and Security, MLCSS 2022

作者： Nayak, Subrata Kumar Pradhan, Sateesh Kumar Mishra, Sujogya Patra, Narayan Pradhan, Sipali GVHSS Sishu Ananda Mahavidyalaya Department of Computer Science Odisha Balipatna India Utkal University Department of Computer Science and Application Bhubaneswar India Odisha University of Technology and Research Department of Mathematics Odisha Bhubaneswar India Institute of Technical Education and Research SoA Deemed to Be University Department of Csit Bhubaneswar India Rbvrr Women's College Dept. of Computer Science Hyderabad India

ISBN: (纸本)9781665454933

Detection of Malaria Fever is a challenge for the doctors as symptoms of Malaria fever vary concerning places;for example, the types of malaria fever found in our country are not the same in South Africa, England, and other western countries. Malaria is not fatal if detected early, but due to its complex nature, it is difficult to side-line this fever with other fevers like Typhoid, Dengue, and other class of fevers. Our objective is to find the minimal attributes responsible for malaria by using Rough Set Theory (Rst), a soft computing technique that is a useful tool to handle imprecise and vague datasets. Our objective is to find a set of attributes (symptoms) of malaria from the vague medical data set, which are helpful for the doctors for concrete diagnosis of malaria. We validate our claim by using statistical techniques. © 2022 IEEE.

关键词： Rough set theory

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