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S. Shridevi

Bio: S. Shridevi is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Ontology (information science). The author has an hindex of 4, co-authored 13 publications receiving 27 citations.

Papers
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Journal ArticleDOI
TL;DR: The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images, and the results prove the genuine nature of the proposed technique.
Abstract: Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure that the transmitted data are secure. This paper focuses on the security, speed and load of transmission. To prove security, combined steganographic methods involving cryptographic algorithms are used. The proposed model begins by updating two entries, medical image data and medical report data. Digital imaging and communications in medicine image data hold the medical report data to be encrypted and transmitted over the network channel. Although the proposed work follows the conventional method of data transmission from encryption until transmission, an effort has been made to split up the given data without transmitting them as such. As a public cryptography mechanism, the algorithm is also capable of transmission during decryption. The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images. The proposed method justifies its performance when tested in hiding medical transcription data of different sizes varying across 30, 45, 64, 128 and 256 bytes in sample images with an average PSNR ranging from 55 to 70 dB, an MAE averaging from 0.2 to 0.7, and an SSIM, SC and correlation coefficient averaging to 1. This research is proven to work well in a simulation environment, and the results prove the genuine nature of the proposed technique.

16 citations

Journal ArticleDOI
Ayush Thada1, Uday Karan Kapur1, Saif Gazali1, Nikhil Sachdeva1, S. Shridevi1 
TL;DR: An intelligent cyber-physical system for the waste management in a locality which will not only keep records of the use of the trash bins in the but also ensure they have an additional blockchain based verification system which will ensure the proper working even in the presence of an adversary.

10 citations

Journal ArticleDOI
TL;DR: The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features and uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features.
Abstract: Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data.

10 citations

Book ChapterDOI
L. Rachana1, S. Shridevi1
01 Jan 2021
TL;DR: This paper condenses the work of semantic technology approach in machine learning and its idea put forward and contains summarized view of the papers with a graph plotted on the analysis of paper throughout the decade; a table with summary of the related works and a review analysis.
Abstract: Semantic technology approach in machine learning is an emerging technique to solve the problems in the machine learning. Semantic technology has been the improvised from decades according to the human needs and industrial demands. This new era is all about teaching a machine to learn on its own and to make it understand the concept and the purpose for what it is used, using algorithms. This paper, condenses the work of semantic technology approach in machine learning and its idea put forward. The introduction details with brief explanation followed by description of the semantic technology and machine learning, important role. The literature survey contains summarized view of the papers with a graph plotted on the analysis of paper throughout the decade; a table with summary of the related works and concluded with review analysis.

7 citations

Journal ArticleDOI
TL;DR: A novel deep learning model named Spatial Feature Attention Long Short-Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature maintains the state-of-the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability.
Abstract: Weather prediction and meteorological analysis contribute significantly towards sustainable development to reduce the damage from extreme events which could otherwise set-back the progress in development by years. The change in surface temperature is one of the important indicators in detecting climate change. In this research, we propose a novel deep learning model named Spatial Feature Attention Long Short-Term Memory (SFA-LSTM) model to capture accurate spatial and temporal relations of multiple meteorological features to forecast temperature. Significant spatial feature and temporal interpretations of historical data aligned directly to output feature helps the model to forecast data accurately. The spatial feature attention captures mutual influence of input features on the target feature. The model is built using encoder-decoder architecture, where the temporal dependencies in data are learnt using LSTM layers in the encoder phase and spatial feature relations in the decoder phase. SFA-LSTM forecasts temperature by simultaneously learning most important time steps and weather variables. When compared with baseline models, SFA-LSTM maintains the state-of the-art prediction accuracy while offering the benefit of appropriate spatial feature interpretability. The learned spatial feature attention weights are validated from magnitude of correlation with target feature obtained from the dataset.

7 citations


Cited by
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Journal ArticleDOI
TL;DR: A reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions is presented.
Abstract: Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

32 citations

Journal ArticleDOI
TL;DR: In this paper , the authors provide a holistic review of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations.
Abstract: With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on a high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and the meaning of information into data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart Internet. In this paper, we first highlight the motivations and compelling reasons for SemCom in 6G. Then, we provide an overview of SemCom-related theory development. After that, we introduce three types of SemCom, i.e., semantic-oriented communication, goal-oriented communication, and semantic-aware communication. Following that, we organize the design of the communication system into three dimensions, i.e., semantic information (SI) extraction, SI transmission, and SI metrics. For each dimension, we review existing techniques and discuss their benefits and limitations, as well as the remaining challenges. Then, we introduce the potential applications of SemCom in 6G and portray the vision of future SemCom-empowered network architecture. Finally, we outline future research opportunities. In a nutshell, this paper provides a holistic review of the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations.

23 citations

Journal ArticleDOI
TL;DR: A systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately is presented in this article .
Abstract: Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

20 citations

Journal ArticleDOI
TL;DR: In this paper , the authors highlight the motivations and compelling reasons of SemCom in 6G and outline the major 6G visions and key enabler techniques which lay the foundation of semantic communication, and present a SemCom-native 6G network architecture.
Abstract: —With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and meaning of information into the data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we outline the major 6G visions and key enabler techniques which lay the foundation of SemCom. Meanwhile, we highlight some benefits of SemCom- empowered 6G and present a SemCom-native 6G network architecture. Next, we show the evolution of SemCom from its introduction to classical SemCom related theory and modern AI-enabled SemCom. Following that, focusing on modern SemCom, we classify SemCom into three categories, i.e., semantic-oriented communication, goal-oriented communication, and semantic- aware communication, and introduce three types of semantic metrics. We then discuss the applications, the challenges and technologies related to semantics and communication. Finally, we introduce future research opportunities. In a nutshell, this paper investigates the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues for further direction. based SemCom is given and open issues that need to be tackled is discussed explicitly.

19 citations