Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path

Quadir, Abdul; Agrawal, Divyank; Mehta, Monark; Sivaraman, Arun Kumar; Tee, Kong Fah

doi:10.3390/fi13120308

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…By a realistic example discussed in [22][23][24][25][26][27][28][29], we further explain the possible application of the generation in this report.…”

Section: Introductionmentioning

confidence: 89%

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Dhanalakshmi¹,

Samanta²,

Sivaraman³

et al. 2023

Computer Systems Science and Engineering

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Fuzzy sets have undergone several expansions and generalisations in the literature, including Atanasov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets, to name a few. They can be regarded as fuzzy multisets from a formal standpoint; nevertheless, their interpretation differs from the two other approaches to fuzzy multisets that are currently available. Hesitating fuzzy sets (HFS) are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships. However, these possible memberships can be not only crisp values in [0,1], but also interval values during a practical evaluation process. Hesitant bipolar valued fuzzy set (HBVFS) is a generalization of HFS. This paper aims to introduce a general framework of multi-attribute group decision-making using social network. We propose two types of decision-making processes: Type-1 decision-making process and Type-2 decision-making process. In the Type-1 decision-making process, the experts' original opinion is proces for the final ranking of alternatives. In Type-2 decision making processs, there are two major aspects we consider. First, consistency tests and checking of consensus models are given for detecting that the judgments are logically rational. Otherwise, the framework demands (partial) decision-makers to review their assessments. Second, the coherence and consensus of several HBVFSs are established for final ranking of alternatives. The proposed framework is clarified by an example of software packages selection of a university.

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“…By a realistic example discussed in [22][23][24][25][26][27][28][29], we further explain the possible application of the generation in this report.…”

Section: Introductionmentioning

confidence: 89%

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Dhanalakshmi¹,

Samanta²,

Sivaraman³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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“…Machine learning techniques are divided into two learning classes as supervised and unsupervised learning. For task of Sentiment analysis mostly preferred supervised algorithms as Support Vector Machine (SVM), Maximum Entropy and Naïve Bayes (NB) [17][18][19]. It includes feature-based sentiment analysis and summarization.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

Shelke¹,

Lee²,

Samanta³

et al. 2023

Computer Systems Science and Engineering

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In today's digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.

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“…Most of the WBAN energy efficiency-based research is mainly focused on the transmission between the gateway and SN [ 8 ]. As contrasted with SN, the gateway node is required with high battery capacity to execute multiple heavy tasks like computation, long-range wireless communications, and display and so, energy consumption challenges to the gateway node [ 9 ]. Remote health monitoring is allowed to permit WBAN for accepting the technology and achieved physiological signal-delivering and acquiring processes in the monitoring system [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Nithiyanantham¹,

Sivaraman²

2023

Biomedicines

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Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient’s health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

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Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path

Cited by 10 publications

References 19 publications

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

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