A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities

Sivakumar, P.; Nagaraju, Regonda; Samanta, Debabrata; Sivaram, M.; Hindia, Mohammad Nour; Amiri, Iraj Sadegh

doi:10.1007/s11276-019-02075-7

Cited by 33 publications

(9 citation statements)

References 42 publications

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“…As a result, the system extracts essential morphological aspects from processed ECG. Many random tests and cross-validation tests are used to train the proposed method predictor on 125 samples from the training dataset [30,31].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Technology-Based Heart Disease Detection Models

Nagavelli

Samanta

Chakraborty

2022

Journal of Healthcare Engineering

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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Technology-Based Heart Disease Detection Models

Nagavelli

Samanta

Chakraborty

2022

Journal of Healthcare Engineering

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“…Thanks to the various advantages of FSO communications, FSO link can be part of the backhaul communication network for rural or remote substations monitoring applications. In [ 103 ], the FSO system based on a microring resonator (MRR) with the ability to deliver up to gigabit (line-of-sight) transmission per second is proposed for the two SG applications (AMI and DR). The experimental results demonstrate up to 10 times bandwidth improvement over the radius as large as 600 m and maintain receive power higher than the minimum threshold (−20 dBm) at the controller/users, so the overall system is still able to detect the FSO signal and extract the original data without detection.…”

Section: Smart Grid Communicationmentioning

confidence: 99%

Communication Technologies for Smart Grid: A Comprehensive Survey

Abrahamsen

Cheffena

2021

Sensors

111

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With the ongoing trends in the energy sector such as vehicular electrification and renewable energy, the Smart Grid (SG) is clearly playing a more and more important role in the electric power system industry. One essential feature of the SG is the information flow over high-speed, reliable, and secure data communication networks in order to manage the complex power systems effectively and intelligently. SGs utilize bidirectional communication to function whereas traditional power grids mainly only use one-way communication. The communication requirements and suitable techniques differ depending on the specific environment and scenario. In this paper, we provide a comprehensive and up-to-date survey on the communication technologies used in the SG, including the communication requirements, physical layer technologies, network architectures, and research challenges. This survey aims to help the readers identify the potential research problems in the continued research on the topic of SG communications.

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“…The passive attack, are geared to gather information as opposed to gain the access to that information [35]. The attacks such as denial of service, breaking into the site, Data breach, resource usage, deception belong to active cadre while the sniffing, network traffic, sensitive information gathering belong to passive attacks cadre [41]. Data security is the vital goal of network security mechanisms.…”

Section: A Network Security Challengementioning

confidence: 99%

Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture

Samanta

Alahmadi

Karthikeyan³

et al. 2021

IEEE Access

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The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications,all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure,software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services..

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A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities

Cited by 33 publications

References 42 publications

Machine Learning Technology-Based Heart Disease Detection Models

Machine Learning Technology-Based Heart Disease Detection Models

Communication Technologies for Smart Grid: A Comprehensive Survey

Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture

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