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A combinatorial algorithm for finding straight skeleton of a digital object

Multimedia Tools and Applications 2025年 1-21页

作者： Maity, Anukul Dutt, Mousumi Biswas, Arindam Department of Computer Science and Engineering Narula Institute of Technology 81 Nilgunj Road Agarpara West Bengal Kolkata700109 India Department of Computer Science and Engineering St. Thomas’ College of Engineering and Technology 4 Diamond Harbour Road Kidderpore West Bengal Kolkata700023 India Department of Information Technology Indian Institute of Engineering Science and Technology Botanical Garden Road Shibpur West Bengal Howrah711103 India

The paper presents a combinatorial algorithm to find the straight skeleton of the inner isothetic cover of a digital object imposed on a uniform background grid. The isothetic polygon (orthogonal polygon) tightly inscribes the given digital object. The algorithm stated here finds the straight skeleton of any isothetic polygon (simple or non-simple) in O(nglogng) time in a single traversal by applying combinatorial rules, where n is the number of pixels on the boundary of the digital object and g is the grid size on which the digital object is imposed. To find the straight skeleton of non-simple orthogonal polygon, the polygon is divided into sub-parts which are a set of simple orthogonal polygons. The straight skeleton is generated for each of the sub-parts and the corresponding results are merged. The orthogonal polygon containing holes is cut at each holes and the polygon becomes hole-free. The algorithm to obtain the straight skeleton is applied on it. The disconnected hole parts are rejoined and the result is merged. The straight skeleton is useful shape descriptor of digital object. This algorithm stated here has applications in shape analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Combinatorial optimization

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A comparative study of recommender systems in different domains 4th

A comparative study of recommender systems in different doma...

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4th International Conference on Computational Methods in science and technology, ICCMST 2024

作者： Rohit Sharma, Aditi Kumar, Aditya Singh, Prabal Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India

ISBN: (纸本)9781032911571

Recommender systems are techniques designed to enhance user experience in various domains. They suggest relevant items to users based on their behavior and preferences (Linyuan et al. Feb 2012). These systems are being extensively utilized in domains such as e-commerce, social media, and video streaming platforms. A recommender system aims to provide personalized recommendations to users based on their past behavior, preferences, and other similar data. The recommender system receives input data from users. Several recommender systems are available nowadays, making it challenging to select one. It would be beneficial to conduct a comparative analysis of various recommendation systems. The research paper attempts to conclude the performance of various recommender systems by using a variety of evaluation metrics. Evaluation metrics provide insights into the effectiveness of the recommendation algorithms. This guides the selection of the most appropriate model for a given application (Oona et al. 2024). The comparative study aims to examine and analyze different recommender systems across various domains. We evaluate the performance of these systems in improving user experiences and increasing user interaction. By studying the performance of recommender systems in different domains, we gain insights into the strengths and limitations of each approach. © 2025 the Author(s).

关键词： collaborative content-based evaluation metric hybrid recommendation recommender

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An empirical analysis of hyperparameter tuning impact on ensemble machine learning algorithm for earthquake damage prediction

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Asian Journal of Civil engineering 2024年第4期25卷 3521-3547页

作者： Feroz, Shejuti Binte Sharmin, Nusrat Sevas, Muhammad Samee Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Earthquake damage prediction is crucial for ensuring the safety of building occupants and preventing substantial financial losses. Because it enables robust structural design, financial readiness, and well-timed expenditures in preventive measures, anticipating seismic impacts promotes sustainability and long-term building. Machine learning (ML) have transformed building damage prediction, providing efficient methodologies for assessing structural vulnerabilities and risks. ML analyzes multifaceted datasets, handling complex spatial and temporal data, enhancing accuracy in forecasting damage probabilities and enabling proactive monitoring for timely interventions. However, ensemble machine learning and the fine-tuning of such algorithms with the hyperparameter optimization with the earthquake damage prediction have not been explored in the literature yet. Hyperparameter optimization in machine learning enhances model performance and generalization capacity. Skillful adjustment of hyperparameters significantly improves predictive accuracy, resilience, and training convergence, ensuring optimal model performance across diverse datasets and real-world scenarios. This study focuses on improving earthquake damage prediction accuracy through an extensive analysis of the earthquake dataset on ensemble machine learning with hyperparameter tuning. Utilizing various hyperparameter tuning algorithms and examining five ensemble machine learning algorithms, combined with six distinct hyperparameter tuning techniques, significantly enhanced accuracy. The paper’s main contributions include exploring novel hyperparameter tuning algorithms for earthquake damage prediction and filling a knowledge gap in the field. The thorough dataset analysis revealed a scarcity of existing literature, suggesting opportunities for further research. The study underscores the critical role of hyperparameter analysis in machine learning and proposes potential applications beyond earthquake prediction,

关键词： Earthquake prediction Ensemble machine learning Hyperparameter optimization

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Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

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computer Systems science & engineering 2023年第3期44卷 2619-2636页

作者： J.Shanthini P.Punitha S.Karthik Department of Computer Science&Engineering Dr.N.G.P.Institute of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary *** tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and *** in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and *** present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile *** on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each *** analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

关键词： Diplomatic mobile Ad-hoc network grouping mobility interior stable hybrid routing scheme adaptive switch structure clustering communication

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Efficient Squeezeformer Using Fastformer Attention for American Sign Language Fingerspelling Recognition

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Journal of The Institution of Engineers (India): Series B 2025年 1-18页

作者： Karkidholi, Srijana Chebrolu, Srilatha Turaga, V. K. Hanuman Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem534101 India

This paper centers on leveraging Convolution-Augmented Transformer Models originally designed for Automatic Speech Recognition (ASR) in the realm of Sign Language—specifically, American Sign Language Fingerspelling Recognition (ASLFR). Established ASR models like Conformer and Squeezeformer have demonstrated efficacy in ASLFR. In this endeavor, we aim to explore alternative ASR models such as Zipformer and Branchformer;and assess their performance and potential advantages over Conformer and Squeezeformer. Our paper comes up with six improvised model setups, out of which an improvised Squeezeformer with an Additive Attention mechanism from Fastformer resulted in lesser training time (920 min vs 1280 min), lesser model parameters (9.16 M vs 9.74 M), and comparative Normalized Levenstein Distance inference score (0.661 vs 0.684) as compared to the baseline model: improvised Squeezeformer using LlaMA Attention considered. The above best model is followed by an improvised Squeezeformer with Fastformer Attention and Efficient Channel Attention incorporated in the Depthwise Separable Convolution, with a slight increase in its performance but with a trade-off with its model complexity. © The Institution of Engineers (India) 2025.

关键词： Speech analysis

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Augmented Array based Efficient Fill Ratio Estimation in a Bloom Filter 2

Augmented Array based Efficient Fill Ratio Estimation in a B...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Singh, Amritpal Singh, Amrinderpreet Batra, Shalini Department of Computer Science and Engineering National Institute of Technology Jalandhar Jalandhar India Department of Computer Science and Engineering Thapar Institute of Engineering & Technology Patiala India

ISBN: (纸本)9798331504106

Analysing patterns/trends and associations from heterogeneous data coming at varied speeds and formats require data structures which can handle large and dynamic data efficiently. Bloom Filter (BF), a probabilistic data structure, can be considered as one of the alternatives as it is space efficient and handles membership query effectively. To accommodate the dynamic data sets in the BF when ever the Fill Ratio (FR) exceeds the defined limits, two major approaches have been discussed in the literature: increasing the number of BFs as the data increases and refreshing the existing BF by deleting the staled data. In this paper, a novel approach motivated from Double probing named (DH-FRP) has been proposed to compute FR using augmented array to sample bit locations from parent BF. The augmented array keeps track of the sampled bit values at any given time and stores at contiguous locations for effcient bit counting and FR calculation. Markov Bound has been used to determine whether to recompute the step size or not. Since the rate of dynamic data coming from various sources may be uniform or non uniform, both cases have been considered and FR has been calculated for standard BF and Counting BF. It has been experimentally proved that the proposed scheme outperforms the given state-of-art techniques in terms of FR calculation time and accuracy. ©2024 IEEE.

关键词： Markov processes

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems 3

Secure and Efficient Deduplication for Multimedia Sharing in...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

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SN computer science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Investigation of sustainable strategies with metaheuristic algorithm on surface roughness, cutting temperature, and chip morphology during machining of Ti6Al4V alloy

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International Journal of Machining and Machinability of Materials 2024年第3期26卷 223-243页

作者： Misra, Sutanu Kumar, Yogesh Paul, Goutam Pradhan, Buddhadeb Department of Mechanical Engineering University of Engineering and Management Kolkata India Department of Mechanical Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cutting-edge temperature, and chip morphology to understand the machinability of Ti-6Al-4V alloy using sustainable manufacturing techniques. Here, a metaheuristic algorithm approach has been introduced to determine the experimental data’s accuracy, which is approximately 75% for further investigation. Logistic regression and support vector machine provided higher accuracy and best fitted with the dataset and problem formulation. During this work, it has been observed that chip morphology is influenced by cutting parameters and lubrication strategy. The cryogenic plus MQL hybrid approach is the best method over cryogenic cooling by decreasing the heat by 12% and giving 8% better surface quality. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Titanium alloys

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