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共 264 条记录，以下是31-40 订阅

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Face Identification with Feature Learning Using Quasilinear Partial Differential Equation

Face Identification with Feature Learning Using Quasilinear ...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Muthukumaran, V. Satheesh Kumar, S. Radhika, E.K. Arpana, G.K. Nalini, R. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Computer Science Sindhi College Bangalore India Department of ECE East West College of Engineering Bangalore India

ISBN: (纸本)9783031810855

This study suggests a novel soft computing approach for enhancing face recognition performance using the feature learning methodology. The suggested system of Quasi-linear Partial Differential Equations (QPDE) based face recognition model is used to increase the accuracy of facial identification. The webcam first catches the user's image and saves it to the user's computer. The inference model receives the captured image. The input image is pre-processed to enhance the image, and then the image is split into numerous integral images at the inference model. Using speed-up robust features, an intriguing point is constructed for each integral image. The computed interest point is converted into a QPDE based weight matrix by representing the strength of features at all the regions of the face. With the generated weighted matrix of features extracted from the facial image, the method generates a Fast Artificial intelligence (AI) network with many layers. Each layer of the neural network has many neurons with dedicated functions to compute the similarity and feature weight using QPDE. The proposed approach has produced efficient results compared to earlier methods for the Flickr-Faces-HQ database (FFHQ) datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： soft computing

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A Comprehensive Review of Blockchain with IoT in Agriculture 15th

A Comprehensive Review of Blockchain with IoT in Agriculture

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Rehal, Jaijit Singh Maria, Jude Praneet Chauhan, Ishika Singh Deshpande, Shripad Kantipudi, M. V. V. Prasad Department of Electronics and Telecommunication Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

ISBN: (纸本)9783031810855

The agriculture sector has remained one of the largest employers worldwide. With such a scale comes the number of multiple levels of complexities in the functioning of the agricultural supply chain industry primarily related to food production, distribution, and retail. Any anomaly in managing the same might result in food epidemics, many of which have been witnessed in the past. The lack of transparency and a deficit of technical advancements of the systems in this domain emerge as challenges in the conception of efficient traceability systems as a solution to this predicament. The systems that are already in use prove to be ineffective in terms of their interoperability or data-sharing capabilities, thus being shielded from any kind of modification or upgrades. This paper discusses the use of both blockchain and Internet of Things (IoT) technologies in the field of agriculture through the use of supply chain management, precision agriculture, traceability systems and the use of distributed ledgers for record-keeping. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blockchain Distributed Ledger Technology Green Agriculture Internet of Things

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Machine Learning-Based Music Recommendation System from Spotify 15th

Machine Learning-Based Music Recommendation System from Spo...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Bahadure, Nilesh Bhaskarrao Patil, Prasenjeet D. Kalbande, Sanjula Pipalatkar, Shailaja Nagar, Somesh Kuhikar, Om Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India MIT School of Engineering MIT ADT University Loni Kalbhor Pune India

ISBN: (纸本)9783031889912

Music plays a vital role in our lives. There is a huge amount of information available everywhere;our task is to filter out only the information that is relevant for users in which they are interested or which suits their goals. In this work, we are aiming to build a music recommendation system that will be content-based. A recommendation system is a filtering system that will predict the user’s choices in music based on their preferences. The content filtering method relies on the characteristics of data and filters it according to the same. So, we implemented a model that works well for a large number of instances and datasets. The design and implementation of the proposed song recommendation system are highly effective and consistently stable. The encouraging results, with an accuracy of 90.5%, provide a new idea and a theoretical basis and open a new avenue for future research on song recommendation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： elbow method K-NN machine learning

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A Framework for Recommending Products to Solve Cold Starts Problem Using FP Growth Algorithm

A Framework for Recommending Products to Solve Cold Starts P...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Dani, Virendra Bhati, Nisha Nagar, Sneha Mandloi, Ayesha Matkar, Mayank Department of Computer Science and Engineering Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Department of Computer Science and Engineering Medicaps University Indore India Department of Electronics and Communication Engineering Indore Institute of Science & Technology Indore India Department of Computer Science and Engineering SKITM-Technical Campus Indore India

ISBN: (纸本)9783031889912

Different product characteristics and consumer expectations must be analyzed when making a product or service recommendation based on use. However, if all types of knowledge are inaccessible, this is known as the clod start problem in a recommendation system. To explain and address the issue of cold start, this paper suggests a structure for user experience-based product recommendation. As a result, this paper introduces a four-phase recommendation scheme. In the first step, site usage data is preprocessed and used to make product recommendations. The frequent pattern-based recommendation is performed in the next step. The decision is made in the third process based on the user's current search (user click streams) and related user habits seen in network access logs. In addition, the final recommendation is based on filtering the results obtained in the third step, as well as the product or service's cost, brand, and social review. The results show that the prediction is accurate and precise. Furthermore, compared result of proposed system to traditional system using Apriori Algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Apriori cold start problem Data Mining frequent pattern k-NN Prediction Recommendation System Web Log

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Medicinal Plant recognition Using Image Processing

Medicinal Plant Recognition Using Image Processing

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Arekar, Vedanti Vaishampayan, Devashree Bachute, Mrinal Kotecha, Ketan Abraham, Ajith Department of Electronics and Telecommunication Symbiosis Institute of Technology Pune India Department of Electronics and Telecommunication Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune 412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune 412115 India UCSI University Kuala Lumpur Malaysia School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Center for Artificial Intelligence Innopolis University Republic of Tatarstan Innopolis 420500 Russian Federation

ISBN: (纸本)9783031810855

Indian medicinal herbs are widely used to produce traditional medicine. It has long been believed that medicinal plants may be a valuable source of therapeutic help. This entails the use of medicinal plants, maybe as a material for preserving good health as well as for the treatment of disorders. Locating an Ayurvedic plant among massive greenery is a difficult task. Based on a plant’s size, leaves, blooms, fruits, etc., we can identify it. The majority of the time, we must identify plants based on their leaves since a leaf is a component of the plant that is present on plants virtually all year round. When working with a plant’s leaf, it’s crucial to pay attention to even the smallest variations in the curves that describe the leaf’s form. In this study, we built a computerised system for recognising the enormous variety of medicinal plants important to Ayurveda. The methods required for significant leaf characteristics from images and for detection of leaves are included in the review. They are divided into group depending on their performance, categorization of leaf pictures such as shape, vein, texture. A data set of 25 plant species is created. It is converted into a gray scale image. Then analysis of the texture of the leaf is done, it is compared with the given database, and the result is given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image processing techniques Leaf recognition Medicinal plant

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An Effective Deep Learning Technique for Analyzing COVID - 19 Using X-Ray Images 15th

An Effective Deep Learning Technique for Analyzing COVID - 1...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Uma Maheswari, V. Aluvalu, Rajanikanth Guduri, Manisha Kantipudi, M. V. V. Prasad Department of Computer Science and Engineering Chaitanya Bharati Institute of Technology Telangana Hyderabad India Symbiosis Institute of Technology Hyderabad Campus Symbiosis International (Deemed University) Pune India School of Computing and Informatics University of Louisiana at Lafayette Lafayette 70503 LA United States Department of Electronics & Telecommunications Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune 412115 India

ISBN: (纸本)9783031810794

The COVID - 19 disease is a extremely contagious viral infection with significant global health insinuations. COVID has a negative impact on the world economy. If positive patients are identified early, the proliferation of this pandemic infection can be slackened. COVID-19 disease prophecy is useful for finding the people with health issues who are at risk of COVID. The Deep Learning and machine learning approaches for COVID prediction using X-ray images have the possibility to be tremendously helpful in addressing the shortage of clinicians and doctors in rural areas. This research presents a deep layered Convolutional Neural Network (CNN) for recognizing the COVID-19 patients by training the system with the datasets of around 6000 chest X-ray images collected from various sources. We are categorized them as normal or COVID - impacted. Our model trained the X-ray images of chest to find these patients. And it also predicted well with the help of image analysis and deep learning by showcasing the better results comparing with the state-of-the-art methods in terms of the average accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Accuracy COVID – 19 analysis Deep CNN Feature extraction Feature map

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Detecting Cyber Bullying in the Social Media Using Deep Learning Algorithms

Detecting Cyber Bullying in the Social Media Using Deep Lear...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Mridul, Aunik Hasan Ahsan, Nowreen Sultana, Zakia Asif, Asifuzzaman Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Science Clarkson University New York United States Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9783031810824

Cyberbullying is a pressing issue in the digital age, especially in non-English-speaking communities. This study focuses on using deep learning techniques to detect cyberbullying in the Bangla language. The research employs a Bangla dataset that includes both cyberbullying and non-cyberbullying text, which undergoes preprocessing, tokenization, and sequence transformation for input into a LSTM recurrent neural network. The LSTM model incorporates word embedding and dropout techniques to enhance its performance. Evaluation metrics like accuracy and AUC-ROC score are employed to assess the model's effectiveness. The study investigates many methods, such as vernacular-specific preparation and data augmenting, that are specifically designed for the identification of cyberbullying in the Bangla language. The findings demonstrate deep learning's capacity to identify cyberbullying in Bangla, which makes a significant contribution to resolving this problem in non-English settings. The research reveals impressive accuracy rates, with LSTM achieving 99.99% accuracy, and a different approach, Convolutional Neural Network (CNN), yielding 99.022%. Ensemble models, including Gradient Boosting, XGBoost, and Random Forest, are also employed and optimized using hyperparameter tuning. Notably, LSTM stands out as the most reliable classifier for cyberbullying detection, as confirmed by the experimental findings and recent research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyberbullying Deep Learning Dropout Embedding

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An Efficient Approach for Improving the Performance of Autonomous Vehicle Using Advanced Computer Vision 15th

An Efficient Approach for Improving the Performance of Auton...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Saini, Vaibhav Jain, Ayushi Anurag Kantipudi, M. V. V. Prasad Department of Electronics and Telecommunication Symbiosis International (Deemed University) Symbiosis Institute of Technology Pune India Department of Computer Science Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9783031810855

The advancement of Autonomous Vehicle technology and the development of the automotive sector both takes the benefit with the use of computer vision. In this research article, we have provided a thoroughly organized, high-level analysis of computer vision, which is widely employed in the automotive industry. By using machine learning with all the features and operating procedures necessary for employing the same technology, we have proposed a concept of sophisticated computer vision for autonomous vehicles. software and hardware aspects of it using a method based on machine learning has been discussed that will produce reliable results. Technological advancements have been highlighted with predicted reliable results, and difficulties that can be overcome when putting our suggested approach into practice. Finally, actual-world examples of cutting-edge computer vision with applications like self-driving cars, assembly automation, quality control, and more have been presented. The automotive industry may greatly profit from this strategy of superior computer vision with machine learning, data optimization, and cutting-edge tools & technology. For future prospective, this level of methodology can be used for other industries as well like health and agriculture etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Automotive Industry Autonomous Vehicle Computer Vision Cutting-edge tools & technology Machine Learning Self-driving cars

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Effective Early Hypothyroid Disease Prediction Using Traditional and Ensemble Machine Learning Algorithms 15th

Effective Early Hypothyroid Disease Prediction Using Traditi...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Mridul, Aunik Hasan Ahsan, Nowreen Saleh, Md. Abu Asif, Asifuzzaman Afrose, Sonia Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Data Science Clarkson University New York United States Computer Science and Engineering Lovely Professional University Punjab India Nutrition and Food Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031810794

In recent years, there has been a noticeable rise in the prevalence of physical ailments, most notably hypothyroidism, a condition that has garnered substantial attention due to its substantial impact on a significant portion of the population. To gain a comprehensive understanding of the disease’s severity, distinguishing between standard and affected diagnostic reports is imperative. In this work, we suggest using algorithmic models to facilitate early identification and increase knowledge of possible health hazards linked to hypothyroidism. Our approach is straightforward and well-suited for the prediction of uncomplicated cases of hypothyroid illness in real-world scenarios. Our dataset, sourced from Kaggle, served as the foundation for our research, which involved the utilization of a variety of classifiers, including RF, LR, GB, KN, ABC, DT, GS, and ensemble techniques. The DT classifier proved to be the most accurate, with an astounding accuracy rate of 98.278%. The findings were quite successful. In addition, the accuracy of the Voting Classifier RDGL was 97.218%, while the Stacking Classifier RDAS demonstrated an accuracy of 97.483%. Our optimization efforts, which included hyperparameter tuning, further enhanced the performance of each classifier. Based on extensive experimentation and a review of contemporary research, our findings unequivocally endorse the Decision Tree (DT) boosting classifier as exceptionally proficient, demonstrating a remarkable accuracy rate of 98.278% in the precise prediction of hypothyroid disease. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Bagging Boosting Ensemble Hypothyroidism

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Wristband for Monitoring the Safety of Elderly People Using IoT and Deep Learning Algorithms: A Review 15th

Wristband for Monitoring the Safety of Elderly People Using ...

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15th international conference on soft computing and pattern recognition, socpar 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Gidugu, Mansi Lakshmi Yanduri, Krishna Guptha Paliviri, Manoj Kumar Aluvalu, Rajanikanth Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad 500075 India Symbiosis Institute of Technology Hyderabad Campus Symbiosis International (Deemed University) Pune India

ISBN: (纸本)9783031810794

As the world’s population is aging continuously, the need for healthcare support services, particularly those related to fall-related injuries, is poised to surge. Effective fall detection technology can alleviate this strain on healthcare resources and reduce the burden on caregivers and family members. The increasing prevalence of health issues in the aged has highlighted the need for innovative healthcare solutions prioritizing safety, independence, and proactive health management. The risk of falling while walking is a significant concern among the many challenges elders face with certain medical conditions. Falling can lead to severe injuries and complications, making timely detection and response needed to safeguard individuals’ health. The significance of this problem is multifaceted and profound. Rapid detection and response to falling can mitigate these consequences, thereby enhancing the safety and health of individuals. In this article, we proposed wristband-integrated deep-learning techniques for wearable sensors. The use of mobile applications exemplifies the potential of technology to revolutionize healthcare. It underscores how AI-driven solutions can empower individuals to lead independent lives while providing comprehensive support for their health needs. The proposed device further seamlessly combines fall detection with medication reminders. This approach promotes proactive healthcare management. Adherence to medication regimens is critical for individuals with chronic conditions, and technology can simplify and enhance this aspect of self-care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aging Population AI-Driven Solutions Deep Learning Techniques Elderly Care Fall Detection Technology Health Tech Revolution Healthcare Innovation Healthcare Resource Optimization Independent Living Support Medication Adherence Mobile Applications Proactive Healthcare Management Wearable Sensors Wristband Devices

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