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3rd IEEE International Conference on Recent Advances in Systems science and engineering, RASSE 2023

作者： Ramani Bai, V. Sudhir, Pranav Joshi, Rashmi Nair, Vighnesh Anilkumar, Vishnu Vidya Academy of Science and Technology Department of Computer Science Engineering Kerala Thrissur India

ISBN: (纸本)9798350341676

Carpooling offers an effective solution to mitigate pollutant emissions and alleviate traffic congestion. This paper presents a novel system designed to address the shortcomings of centralized ride-sharing services by leveraging smart contracts. By ensuring data security, confidentiality, and privacy, the proposed system initiates the decentralization process for carpooling systems. Users can assume the role of either a driver or a passenger, enabling them to publish rides or search for and book desired rides. Smart contracts facilitate seamless execution of user requests, triggered by events such as ride creation or booking. The fare calculation is based on the passenger's route distance, current fuel price, and the driver's car mileage, resulting in significantly reduced costs. Upon booking a ride, the fare amount is debited from the passenger and credited to the driver only upon the passenger reaching their destination. The system's overarching objective is to simplify people's lives by minimizing travel expenses and traffic congestion, all while upholding user security. © 2023 IEEE.

关键词： Smart contract

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Unveiling Themes in Judicial Proceedings: A Cross-Country Study Using Topic Modeling on Legal Documents from India and the UK

Unveiling Themes in Judicial Proceedings: A Cross-Country St...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Didwania, Krish Toshniwal, Durga Agarwal, Amit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science Indian Institute of Technology Roorkee India

Legal documents are indispensable in every country for legal practices and serve as the primary source of information regarding previous cases and employed statutes. In today’s world, with an increasing number of judicial cases, it is crucial to systematically categorize past cases into subgroups, which can then be utilized for upcoming cases and practices. Our primary focus in this endeavor was to annotate cases using topic modeling algorithms such as Latent Dirichlet Allocation, Non-Negative Matrix Factorization, and BerTopic for a collection of lengthy legal documents from India and the UK. This step is crucial for distinguishing the generated labels between the two countries, highlighting the differences in the types of cases that arise in each jurisdiction. Furthermore, an analysis of the timeline of cases from India was conducted to discern the evolution of dominant topics over the years. © 2024 Copyright for this paper by its authors.

关键词： Non-negative matrix factorization

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

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Big Data Mining and Analytics 2023年第2期6卷 185-200页

作者： Jiancheng Zhong Zuohang Qu Ying Zhong Chao Tang Yi Pan College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences ShenzhenGuangzhou 518055China

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential ***,gene expression data are prone to significant fluctuations due to noise interference in topological *** this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise *** then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression *** the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential *** strategy exhibited a high recognition rate and good *** validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy *** with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and *** also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene *** experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

关键词： Protein-Protein Interaction(PPI)network continuous and discrete similarity coefficient essential proteins

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Analysis of CNN Models using Augmented Data in Tomato Diseases 18

Analysis of CNN Models using Augmented Data in Tomato Diseas...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sharma, Pavan Yadav, Rakesh Kumar Rawat, Sur Singh Iftm University Department of Computer Science and Engineering Moradabad India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Jss Academy of Technical Education Department of Computer Science and Engineering Noida India

ISBN: (纸本)9789380544519

A vital source of nourishment for millions of people, tomatoes are one of the most significant vegetables in the world. However, many diseases may substantially decrease the yield and quality of tomato plants. Early blight, Late blight and Powdery mildew are the three most destructive diseases in tomatoes that have the capacity to drastically lower tomato crop yields. A trusted and efficient automated approach for identifying diseases needs to be created in order to address this problem. To recognize the three primary disorders that impact tomato plants based on leaf images, we created a model using a convolution neural network (CNN) and data augmentation. We also compared the performance of different CNN models, and assessed its efficacy. With an average accuracy rate of -61.75% and average loss of 6.75%, theResNet50 model is a valuable tool that tomato growers can use to keep the diseases mentioned above from affecting their tomatoes during the harvesting process. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： augmentation CNN mobilenet resnet50 vgg16 xception

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IDTCKS: An Intelligent Integrative Approach for Disaster Tweet Classification and Disaster-Related Document Classification Using Knowledge-Driven Hybrid Semantics 2nd

IDTCKS: An Intelligent Integrative Approach for Disaster Twe...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Nama, Vihaan Deepak, Gerard Department of Computer Science Engineering RV College of Engineering Bengaluru India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819995172

There is an immediate requirement for a strategic semantically inclined, semantic web accommodating framework for disaster tweet classification and disaster document and newsfeed documents recommendation to perform disaster risk mitigation and management. This framework proposes an IDTCKS framework for disaster management as a domain of choice, wherein an agent-driven model for integrating auxiliary knowledge from several news and Twitter APIs is encompassed. Subsequently, the TF-IDF discovers informative terms from the documents. The frameworks encompass two distinct classifiers, namely the AdaBoost Classifier to classify the disaster tweets using the upper ontology for the disaster domain as the features and upper ontology is considered by using the LDA to the enriched disaster terms. Subsequently, the upper ontology and the LDA-enriched disaster terms are also used as a feature-driven logistic regression classifier that helps in classifying the dataset. Semantic relevance is achieved using Twitter Semantic Similarity and Horn’s Index with a threshold and step deviance measure and overall precision of 97.39, an accuracy of 98.24, and a very low False Negative Rate of 0.01 has been achieved by the IDTCKS model for disaster classification while an overall precision of 96.57, accuracy of 97.65, and a very low False Negative Rate of 0.02 has been achieved by the IDTCKS model for document classification, which performs much better than other baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disasters

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An ensemble framework of decision trees for class imbalance using partitioning

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International Journal of Innovative Computing and Applications 2024年第1期15卷 1-13页

作者： Kadappa, Vijayakumar Guggari, Shankru Veerabhaskar, Rajeshwari Devi Doddapoojari Department of Computer Applications B.M.S. College of Engineering Bengaluru560019 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India Department of Electronics and Communication Engineering Global Academy of Technology Bengaluru560098 India

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. Some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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Enhancing Automatic Image Captioning: A Systematic Analysis of Models, Datasets, and Evaluation Metrics 4

Enhancing Automatic Image Captioning: A Systematic Analysis ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ganesan, Kothai Amutha, S. Saketh, G. Venkata Reddy, G. Vishnu Vardhan Chowdary, G. Harinath Sai, J. Ajay Kpr institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331529635

The fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human intervention. This area of research has attracted considerable interest due to its potential uses in improving accessibility, managing content, and enhancing search engine optimization. Over the past years, there has been a drastic increase in the accuracy and detail of captions thanks to recent advances in deep learning, especially with convolutional neural networks (CNNs), long short-term memory (LSTMs), and recurrent neural networks (RNNs). This research examines cutting-edge approaches to automatic image captioning, including attention mechanisms, models based on transformers, and techniques utilizing reinforcement learning. The effectiveness of these methods was evaluated using benchmark datasets, and their advantages and limitations were assessed. Additionally, the obstacles faced in producing contextually appropriate and linguistically precise captions were explored, such as dealing with intricate scenes, uncommon objects, and abstract ideas. The paper concludes by highlighting potential areas for future research, including learning across multiple modalities, captioning across languages, and incorporating common-sense knowledge to improve caption generation. © 2024 IEEE.

关键词： Deep reinforcement learning

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Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： Big data Hadoop Music information retrieval Optimization Spark

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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