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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是31-40 订阅

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Empowering Secure and Authenticated Multi-WSN Communication Through a Hybrid Blockchain Framework 2nd

Empowering Secure and Authenticated Multi-WSN Communication...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Ayyanki, Gowri Laasya Khanna, Manju Department of Computer Science Amrita School of Computing Kasavanahalli Karnataka Bengaluru560035 India

ISBN: (纸本)9789819995301

Security has long been a paramount concern for IoT devices due to their frequent exposure to hostile environments. Ensuring the authenticity of the hub is a crucial means of upholding its security. Traditional approaches for IoT identity verification heavily rely on external sources deemed trustworthy. However, numerous IoT environments are susceptible to single point failures and are restricted by inherent limitations. With the advent of decentralized blockchain technology, viable IoT frameworks have become more attainable. This study proposes a novel multi-WSN validation mechanism for the Web of Things. Based on their capabilities, IoT hubs are categorized into base stations, cluster head hubs, and conventional hubs, establishing a network with varying levels of granularity. To address these challenges, a hybrid blockchain model is introduced, incorporating both public and local chain hubs within the blockchain system. This hybrid paradigm enables mutual authentication of hub identities across diverse communication scenarios by leveraging a local blockchain for routine hub identity verification and a public blockchain for cluster head hub character validation. Extensive analysis demonstrates that the framework ensures comprehensive security measures and demonstrates advanced performance capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network topology

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Correlation-Based data Fusion Model for data Minimization and Event Detection in Periodic WSN

Correlation-Based Data Fusion Model for Data Minimization an...

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international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Verma, Neetu Singh, Dinesh Singh, Ajmer Murthal India

ISBN: (纸本)9789819932498

In WSNs, energy consumption is the main challenging factor that needs to be reduced to enhance the lifetime of a network. Energy can be saved by adopting various data reduction techniques (data aggregation, data prediction, data compression, data fusion) that eliminates unnecessary and redundant data. But, redundant data may increase the quality of data. Keeping in view of quality data, we proposed tempo-spatial based two-level data fusion model for data minimization and event detection. At the sensor node, the first level of data fusion determines the similarities between measurements and transmitted the single most similar measurement to the CH. This process removes the similar as well as local outliers. The CH receives the data from the sensor nodes and DMED data fusion algorithm is used to distinguish the incoming data into following events: Normal, Abnormal, Suspicious, and Outliers. This model forwards the minimized data to the sink node under normal situations, and also it will accurately find the abnormal event if the suspicious situation has occurred. The proposed model is superior to the previous algorithm in terms of data transfer, energy consumption, and determining accurate events in a short time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data reduction

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SHLPM: Sentiment Analysis on Code-Mixed data Using Summation of Hidden Layers of Pre-trained Model 1

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international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Babu, Yandrapati Prakash Eswari, R. Vijay Raman, B. Department of Computer Applications National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (数字)9789819932504

ISBN: (纸本)9789819932498

Code-mixing refers to the blending of languages. The most serious issue with code-mixing is that people move between languages and type in English instead of transcribing words in Dravidian scripts. Traditional NLP models are trained on large monolingual datasets. Code-mixing is difficult since they can’t manage data code-mixing. A significantly less percentage of research is going on code-mixed data. In this paper, a Kannada-English code-mixed dataset is used. In this dataset, YouTube comments are categorized into a negative, positive, unknown state, not-intended language (not-Kannada) and mixed feelings, and categories based on message-level polarity. The summation of hidden layers of the pre-trained model (SHLPM) like BERT and XLM-RoBERTa classification models was used for this complex and challenging task. The SHLPM-XLM-RoBERTa has given F1-score of 64% for the Kannada-English YouTube comments dataset and outperforms other existing models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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An Integrated Development Of IoT Based Machine Learning For data-driven Travel Recommendations

An Integrated Development Of IoT Based Machine Learning For ...

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2024 international conference on advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Rifath, Shakeel Sridhar Xavier, Constance Brindasri, S. Hindustan Institute Of Technology And Science Computer Science And Engineering Department Chennai India

ISBN: (纸本)9798350364828

An innovative methodology is introduced in this Research to enhance data-driven travel decision-making by integrating IoT technology and data science techniques. With a specific focus on air and sound pollution, the research utilizes real-time sensor data from various urban sources to develop predictive models for forecasting future pollution levels. These models serve as the backbone for decision support tools seamlessly integrated into open-source platforms like ThingSpeak and IFTTT, facilitating proactive travel planning for both the general public and patients. The core of this methodology lies in the real-time aggregation of IoT sensor data, offering users comprehensive insights into prevailing air pollution levels at their intended destinations. Augmented by predictive machine learning algorithms such as logistic regression and decision trees, the system's real-time monitoring capabilities are significantly enhanced, with the decision tree algorithm particularly noted for its accuracy in assessing destination safety for patients. The forecasting accuracy and reliability of the model are upheld through rigorous training on historical data. Notably, the synergy between ThingSpeak and IFTTT enables the delivery of personalized travel recommendations grounded in predictive analytics and real-time environmental monitoring. This research underscores the pivotal role of data-driven decision-making in travel planning, empowering patients with tailored guidance derived from robust data analysis. © 2024 IEEE.

关键词： Predictive analytics

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Symbolic Regression Using Dynamic Structured Grammatical Evolution with Digit Concatenation and Linear Scaling 2nd

Symbolic Regression Using Dynamic Structured Grammatical Evo...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Geethani, V. Velayutham, C. Shunmuga Balraj, Aditya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

ISBN: (纸本)9789819995301

Symbolic Regression’s vast search space can lead to computational inefficiencies. However, Grammatical Evolution (GE) narrows down the search by focusing on solutions adhering to specific grammar and guiding the algorithm toward more promising regions. Dynamic Structured Grammatical Evolution (DSGE), a variant of GE, enables the evolution of variable-length structures, addressing the issue of finding concise and interpretable solutions as problem complexity increases. Integrating constants is challenging, as disruption of values may occur. DSGE, by using digit concatenation as a constant creation method, enables better preservation and accurate representation of constant values during the evolutionary process. The evolved equations can overfit in certain cases. The search algorithm mitigates overfitting by incorporating Linear Scaling with error minimization, prioritizing equations with lower errors to promote better generalization and produce more reliable models. Experiments were demonstrated on the Feynman equations, one of the benchmark datasets. The combined methodology results in capturing the exact and symbolically equivalent equations. These findings highlight the potential of the proposed approach for symbolic regression and related problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Regression analysis

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Integrated Whale Swarm and Neuro-Evolutionary computing for Large-Scale Sparse Optimization Problems 2nd

Integrated Whale Swarm and Neuro-Evolutionary Computing for ...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Balakrishnan, Biju Shanthini, B. Amudha, R. St. Peter’s Institute of Higher Education and Research Avadi Tamilnadu Chennai India Hindusthan College of Engineering and Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819995233

Sparse optimization problems at a large scale present considerable difficulties in diverse fields, such as machine learning, data mining, and signal processing. The aim is to identify the most efficient solutions within expansive search spaces, while constraining the number of non-zero variables. The present study aims to tackle this obstacle by introducing an innovative methodology that integrates whale swarm optimization (WSO) with neuro-evolutionary computing (NEC). The WSO algorithm draws inspiration from the social behavior of humpback whales and presents a proficient approach for investigating and utilizing the search space. The integration of evolutionary algorithms with neural networks by NEC has demonstrated potential in effectively addressing intricate problems. Through the simultaneous evolution of neural networks and optimization processes, the algorithm is capable of adapting and enhancing its search strategy progressively. The study aims to improve the efficiency of WSO in addressing optimization problems characterized by sparsity at a significant scale by incorporating techniques from neuro-evolutionary computing. The method under consideration entails the integration of neural networks into the WSO algorithm with the aim of directing and augmenting the exploration procedure. The study evaluates the efficacy of the hybrid algorithm in relation to established methodologies based on criteria such as the quality of solutions, rate of convergence, and computational efficiency. The results obtained from the experiment provide evidence to support the efficacy of the proposed approach. The hybrid algorithm effectively utilizes the sparsity structure inherent in the problems, resulting in expedited convergence and enhanced precision of the solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Evolutionary algorithms

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data Consumption Behaviour and Packet Delivery Delay Analysis in OTT Services Using Machine Learning Techniques

Data Consumption Behaviour and Packet Delivery Delay Analysi...

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international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Thakur, Rohit Kumar Kumari, Raj University Institute of Engineering and Technology Panjab University Chandigarh India

ISBN: (纸本)9789819909803

Network operators must monitor their networks and analyse their customer's consumption patterns. The data gathered on consumption patterns enables the creation of new data plans targeted at specific consumers, as well as a proper network perspective. The high amount of network resources consumed by over-the-top (OTT) apps are well-known. This data is transferred through protocols that can give us valuable information about consumption and time delay. With this in mind, using data mining and traditional machine learning, a concept for tailoring consumption behaviour and time delay for users, has been considered. data set with 1581 instances and 87 attributes are there which is sufficient to find the behaviour of the user. Two protocols are extracted from the data set on which various machine learning classifiers are implemented to find the best accuracy. Only four attributes are used for the analysis of consumption patterns: Flow ID, Source IP, Destination IP, and timestamp. After reviewing the results, the best suitable algorithm from the traditional approach was Random Forest with an accuracy of 99.67% while the best classifier from the incremental approach is the decision tree with accuracy 99.38%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time delay

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A Multi-objective Evolutionary Algorithm Based on Decomposition—Dynamic Resource Allocation with Mixture Model 2nd

A Multi-objective Evolutionary Algorithm Based on Decomposi...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Alagumathi, M. Thangavelu, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819995301

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) is the basis for studying the concept of multi-objective optimization problems on the basis of decomposition. MOEA/D has computational complexity in solving the multi-objective optimization sub-problems. To reduce computational complexity, in 2009, Zhang et al. proposed a method called MOEA/D-DRA (MOEA/D-Dynamic Resource Allocation). In MOEA/D-DRA, each sub-problem utilizes the computational resources dynamically in a different way. The construction of weight vectors is very important for the success of these algorithms. They actually define the search directions and distribution of the Pareto optimal solutions (POS). This paper proposes a weight vector generation method using uniform design and a mixture model (called MOEA/D-DRA-MM). The MOEA/D-DRA-MM builds a new weight vector generation method that makes the distribution of the weights uniform and broadens the diversity and convergence. MOEA/D-DRA-MM uses two weight vectors that are obtained by uniform design with a mixture distribution. Simulation results suggest that MOEA/D-DRA-MM may provide better convergence and diversity with respect to inverted generational distance and hypervolume metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multiobjective optimization

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An intelligent Antenna Optimization Using Machine Learning Algorithm for 5G Applications 2nd

An Intelligent Antenna Optimization Using Machine Learning A...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Ramasamy, R. Rajavel, V. Ghoshal, Dibyendu Jain, Rachit Nanmaran, R. Srimathi, S. Ramco Institute of Technology Tamilnadu Rajapalayam India National Institute of Technology Tripura Agartala India Madhav Institute of Technology and Science Gwalior India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9789819995172

In the suggested methodology, a microstrip patch antenna with a rectangular shape is employed, along with the utilization of modified ant colony optimization (MACO) algorithms. The operating frequency for the patch antenna is set at 28 GHz. The antenna is designed to exhibit a single-band response, analyzed using HFSS software and optimized results obtained using MATLAB tool. For the antenna design, the chosen substrate is FR4, with a dielectric constant of 4.4 and a thickness of 0.6 mm. The selection of FR4 substrate is based on its accessibility and cost-effectiveness. Various parameters are validated to evaluate the proposed antenna structure’s performance, including return loss, voltage standing wave ratio, radiation pattern, and gain. These measurements are further analyzed to optimize the antenna's performance using MACO approaches. The results of this suggested methodology demonstrate improved performance for the single-band microstrip rectangular patch antenna, making it a suitable choice for 5G applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microstrip antennas

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Screening Application for Possible Mental Health Issues in Adolescents and PWDs Using data Science 2nd

Screening Application for Possible Mental Health Issues in A...

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Gunasundari, S. Nandhithaa, B. Mahalakshmi, L. Aparna, P. Velammal Engineering College Chennai India Fleet Studio Technologies Pvt Ltd Chennai India Power of N TechStudio Private Limited Chennai India Light and Wonder Chennai India

ISBN: (纸本)9789819995172

Mental health issues, such as depression, anxiety, and stress, are prevalent among adolescents and Persons with Disabilities (PwDs), yet access to mental health professionals is limited, especially in rural areas. A screening application for mental health issues in adolescents and PwDs is developed to address this challenge, utilizing Flutter, Dart, and a fine-tuned NLP model of GPT-4. The application consists of a questionnaire module that analyzes user responses, a Results module providing mental health assessments, and a Support and resources module offering over 100+ healthcare resources. data on mental health issues faced by adolescents is collected from Kilpauk Medical Hospital (KMC) and Velammal Medical College to inform the application’s development. The GPT-4 model is fine-tuned for phobia screening, resulting in an 80% accuracy rate in identifying phobia types and levels. The application effectively diagnoses various mental health issues, including anxiety disorders, bipolar disorder, depression, eating disorders, obsessive–compulsive disorder, post-traumatic stress disorder, schizophrenia, and various phobias. The application holds promise in enabling early identification and intervention for mental health issues, particularly in underserved areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Natural language processing systems

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