Enriched cluster head selection using augmented bifold cuckoo search algorithm for edge‐based internet of medical things

Anguraj, Dinesh Kumar; Thirugnanasambandam, Kalaipriyan; R.S, Raghav; S.V, Sudha; Saravanan, D.

doi:10.1002/dac.4817

Cited by 18 publications

(4 citation statements)

References 48 publications

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“…Another work devoted to CH selection in WSNs has been presented in [ 104 ]. The proposed method is called the augmented bio fold cuckoo search algorithm and aims to reduce overall energy consumption by wireless medical devices.…”

Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

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Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

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Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the course of this study effort, comparisons were conducted between the CNN-based strategy, the LSTM-based technique, the CNN-LSTM combined approach, and the Bi-LSTM approach. There are just a few additional works that are relevant to this topic in [20][21][22][23].…”

Section: Related Workmentioning

confidence: 99%

Detection of Sarcasm Using Bi-Directional RNN Based Deep Learning Model in Sentiment Analysis

Muddada Murali Krishna,

Midhunchakkaravarthy,

Jayavani Vankara

2023

ARASET

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Detecting sarcasm in text can be challenging, but it's an opportunity to exercise the ability to consider various perspectives and aspects. Considering multiple viewpoints while analysing sentences can lead to comprehensive results and constructive discussions. Sentiment analysis algorithms are constantly improving and becoming more accurate in identifying sarcasm, even in implicit expressions where the sarcasm is not explicitly stated. It's exciting to have the opportunity to detect sarcasm conveyed through subtle cues, as it adds another layer of complexity to the task. The field of sarcasm detection has made great strides with the use of supervised classification models that can accurately distinguish between sarcastic and non-sarcastic sentences through labelled data. This approach shows promise for identifying explicitly incongruous statements, and there is an increasing need for techniques that can successfully detect sarcasm in sentences with implicit sentiment incongruity. This study employs a deep learning model to tackle the challenge of distinguishing implicit sarcasm. The model being used is a recurrent neural network (RNN), which has the ability to retain numerical representations of previously processed information. The proposed model is designed to effectively identify sarcasm by capturing context-based incongruity within the presented text, which is a crucial factor. The proposed model, Proposed LSTM (P-LSTM), effectively captures the dependencies among words in both suffixes and prefixes by scanning the given sentences bi-directionally. The classification is organised into two levels: emotional and semantic. The suggested approach offers a way to accurately evaluate and categories tweets based on their sarcastic state. Excitingly, the proposed method is being evaluated through experiments on two automatically annotated datasets and two manually annotated datasets to determine its effectiveness. The suggested model is compared to numerous state-of-the-art methods, highlighting its efficacy. The findings demonstrate that the suggested model is highly effective for sarcasm detection compared to other methods currently in use. The suggested model has great potential in recognising implicit sarcasm by utilising deep learning and context-based incongruity analysis, which could lead to significant improvements over existing methods.

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“…A convolutional neural network system that integrates SpotNet with SNIPER (Scale normalization in image pyramids) has been proposed to enhance the detection of small objects [11]. Energy consumption is another important consideration for networks, with solutions like ABCSA [12] applying clustering techniques to effectively manage energy consumption using a cluster-based head selection technique in the health domain.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Object Detection Framework for UAV Imagery Utilizing Transformer-Based Architecture and Split Attention Module: PvSAMNet

Sirisha,

Sudha

2023

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In recent years, advancements in deep learning have fostered the development of sophisticated object detectors, specifically in the realm of computer vision. The inherent complexity of images captured by unmanned aerial vehicles (UAVs) presents a multitude of challenges for object detection. These include, but are not limited to, the detection of small and densely clustered objects, scale variance, occluded objects, and intricate backgrounds, which are particularly prevalent in drone-captured imagery when compared to natural scenes with larger and more distinct objects. The current landscape of object detection research has seen a surge in interest surrounding advanced, anchor-free object detectors, attention mechanisms, and the use of transformers as an alternative to convolutional neural networks. In light of these developments, this study introduces a novel object detection framework that eschews anchor utilization and leverages a transformer backbone for feature extraction. A cardinal grouping-based split attention module is integrated into this network to selectively extract the most pertinent features. The object detection head, termed the Pyramid Vision Split Attention Module Network (PvSAMNet), comprises three branches: classification, confidence, and regression, which collaboratively facilitate the final object detection from drone images. Additionally, an Intersection over Union (IoU) balanced loss function is employed to effectively equilibrate the classification and localization steps. The performance of the proposed detector is evaluated using the Visdrone-DET dataset, with the efficacy gauged by the average precision (AP) and average recall (AR) metrics. The results demonstrate that the proposed model outperforms other detector models with an average precision of 38.74. This study contributes to the ongoing discourse in the field of object detection, providing a novel framework that addresses the unique complexities of UAV imagery and demonstrates promising results in comparative evaluations.

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Enriched cluster head selection using augmented bifold cuckoo search algorithm for edge‐based internet of medical things

Cited by 18 publications

References 48 publications

Swarm Intelligence in Internet of Medical Things: A Review

Swarm Intelligence in Internet of Medical Things: A Review

Detection of Sarcasm Using Bi-Directional RNN Based Deep Learning Model in Sentiment Analysis

An Advanced Object Detection Framework for UAV Imagery Utilizing Transformer-Based Architecture and Split Attention Module: PvSAMNet

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