Novel design of cryptographic architecture of nanorouter using quantum-dot cellular automata nanotechnology.

The article introduces a revolutionary Nanorouter structure, which is a crucial component in the Nano communication regime. To complete the connection, many key properties of Nanorouters are investigated and merged. QCA circuits with better speed and reduced power dissipation aid in meeting internet standards. Cryptography based on QCA design methodologies is a novel concept in digital circuit design. Data security in nano-communication is crucial in data transmission and reception; hence, cryptographic approaches are necessary. The data entering the input line is encrypted by an encoder, and then sent to the designated output line, where it is decoded and transferred. The Nanorouter is offered as a data path selector, and the proposed study analyses the cell count of QCA and the circuit delay. In this manuscript, novel designs of (4:1)) Mux and (1:4) Demux designs are utilized to implement the proposed nanorouter design. The proposed (4:1) Mux design requires 3-5% fewer cell counts and 20-25% fewer area, and the propsoed (1:4) Demux designs require 75-80% fewer cell counts and 90-95% fewer area compared to their latest counterparts. The QCAPro utility is used to analyse the power consumption of several components that make up the router. QCADesigner 2.0.3 is used to validate the simulation results and output validity.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology.

PURPOSE: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. METHODS: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. RESULTS: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. CONCLUSION: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Machine learning and DFT-based combined framework for predicting transmission spectra of quantum-confined bio-molecular nanotube.

CONTEXT: The Adenine-based nanotube is theoretically designed, and its transmission spectra are investigated. The quantum-confined Adenine nanotube shows electronic transmission of the carrier at minimum stress. In this paper, the prediction of transmission spectra of the quantum-confined bio-molecular nanotube is investigated and deeply studied. Molecular level structure prediction and their electronic characterization can be possible with ab initio accuracy using a machine learning algorithmic approach. At the molecular level, it is difficult to predict quantum transmission spectra as these results are always hampered by the carrier backscattering effect. However, mostly these predictive models are available for intrinsic semi-conducting materials and other inorganic structures. METHODS: Machine learning algorithms are designed to predict the electronic properties of the nano-scale structure. This task is even more difficult when quantum-confined molecular arrangements are considered, whose transmission spectra are sensitive to the confinements applied. This paper presents an effective machine learning algorithms framework for predicting transmission spectra of quantum-confined nanotubes from their geometries. In this paper, we consider regression machine learning algorithms to find maximum accuracy with varying configurations and geometries to excerpt their atoms' local environment information. The Hamiltonian components are then used to enable the utilization of the information to predict the electronic structure at any arbitrary sampling point or k-point. The theoretical basics introduced in this process help to capture and incorporate minor changes in quantum confinements into transmission spectra and provide the framework algorithm with more accuracy. This paper shows the ability to predict the accurate algorithmic models of the Adenine nanotube. In this framework, we have considered a tiny data set to achieve a rapid and reliable method for electronic structure determination and also propose the best algorithm for predictive model analysis.

XAI-reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI.

Machine learning (ML) has been used for classification of heart diseases for almost a decade, although understanding of the internal working of the black boxes, i.e., non-interpretable models, remain a demanding problem. Another major challenge in such ML models is the curse of dimensionality leading to resource intensive classification using the comprehensive set of feature vector (CFV). This study focuses on dimensionality reduction using explainable artificial intelligence, without negotiating on accuracy for heart disease classification. Four explainable ML models, using SHAP, were used for classification which reflected the feature contributions (FC) and feature weights (FW) for each feature in the CFV for generating the final results. FC and FW were taken into account in generating the reduced dimensional feature subset (FS). The findings of the study are as follows: (a) XGBoost classifies heart diseases best with explanations, with an increase in 2% in model accuracy over existing best proposals, (b) explainable classification using FS exhibits better accuracy than most of the literary proposals, and (c) with the increase in explainability, accuracy can be preserved using XGBoost classifier for classifying heart diseases, and (d) the top four features responsible for diagnosis of heart disease have been exhibited which have common occurrences in all the explanations reflected by the five explainable techniques used on XGBoost classifier based on feature contributions. To the best of our knowledge, this is first attempt to explain XGBoost classification for diagnosis of heart diseases using five explainable techniques.

Determining human-coronavirus protein-protein interaction using machine intelligence.

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus -19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

Temperature Imposed Sensitivity Issues of Hetero-TFET Based pH Sensor.

An underlapped hetero-structure electrolyte Bio-TFET for potential of hydrogen (pH) sensing has been presented in this article. Intersection charge density ( [Formula: see text]) near the substrate-oxide junction can be employed to represent pH value within the simulation. A feasible fabrication scheme for the proposed model is specified here. A detailed simulation is performed with an ATLAS device simulator to examine the efficiency of the projected sensor. The impact of pH alterations on device features akin to the drain current ( [Formula: see text]), threshold potential ( [Formula: see text]), sensitivity regarding voltage ( [Formula: see text]), and current ( [Formula: see text]) is examined. The effect of phosphate-buffered saline (PBS) concentrations on the pH buffer are also scrutinized. Moreover, the impact of the reference voltage and current ( [Formula: see text] and [Formula: see text]), and channel doping concentration ( [Formula: see text]) over [Formula: see text] and [Formula: see text] is analyzed methodically. Here, [Formula: see text] attains ≈100 mV/pH, which is superior to the Nernstian limit (59 mV/pH) and [Formula: see text] enhances nearly ten times per pH variation. Benchmarking is included to provide a quantitative assessment of the proposed model with the published literature. The impact of temperature on pH buffer, [Formula: see text], temporal drift parameters and sensitivities have been emphasized. Finally, the temperature-immunity aspect of the proposed Bio-HTFET based pH sensor is highlighted by comparing sensitivity parameter among state-of-the-art literature. Hence, the recommended pH sensor can be utilized as an outstanding substitute for the succeeding generation of biosensor applications.

Forecasting adversities of COVID-19 waves in India using intelligent computing.

The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days' intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.

Reduced Graphene Oxide Nanosheets for Selective Picomolar Detection of Bovine Serum Albumin.

In this paper, ultra-low level selective detection of bovine serum albumin (BSA) has been demonstrated, based on chemically derived graphene i.e., reduced graphene oxide (RGO) nanosheets. The working principle of the sensor is based upon change in conductance of the RGO nanosheets with different concentration of BSA. The change in conductance is based on the charge transfer between BSA and functional groups of RGO. The morphological and structural characterizations of RGO nanosheets were carried out by scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Raman spectroscopy is performed to further validate the interaction between RGO sensing layer and BSA molecules. Electrical impedance spectroscopy is performed to observe the impedance variation when BSA interacts with RGO. The sensor device exhibits sensitivity of 10 nA/pM. The lower limit of detection (LOD) of the sensor is found to be 1 pM and response time around 35 s, confirming very high sensitivity for BSA. All electrical (current-voltage) measurements were carried out at 2 V bias for low power operation. The sensor exhibits highest sensitivity at 30 °C and for RGO thickness ~4 nm. The RGO based sensor device is selective towards BSA when compared to proteins like L-Histidine, HSA, BHB and Biotin. Our results suggest that RGO based devices are promising for low-cost, portable and real time detection of BSA at room temperature.

Wet chemical development of CuO/GO nanocomposites: its augmented antimicrobial, antioxidant, and anticancerous activity.

This study employed a bottom-up technique to synthesize copper oxide (CuO) nanoparticles over hydrophilic graphene oxide (GO) nanosheets. The CuO/GO nanocomposite has been prepared using two selected precursors of copper nitrate and citric acid with an intermittent mixing of GO solutions. The synthesized Nanocomposites were characterized using different biophysical techniques like FT-IR, NMR, FE-SEM, and HR-TEM analyses. FT-IR analyses confirm the nanocomposites' successful formation, which is evident from the functional groups of C=C, C-O, and Cu-C stretching vibrations. Morphological analyses reveal the depositions of CuO nanoparticles over the planar rough GO sheets, which has been elucidated from the FE-SEM and HR-TEM analyses supported by respective EDAX analyses. The antimicrobial activities have been evident from the surface roughness and damages seen from the FE-SEM analyses. The CuO/GO sheets were tested against Gram-positive (e.g., Staphylococcus aureus) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa). It is evident that the intrinsic antibacterial activity of CuO/GO sheets, when combined in equal proportions, elicited a robust antibacterial activity when tested over Gram -ve representative bacteria Escherichia coli. The antioxidant behaviour of synthesized CuO/GO nanocomposite was evaluated by scavenging the free radicals of DPPH and ABTS. Moreover, the cytotoxic activity was also studied against epidermoid carcinoma cell line A-431. A brief mathematical formulation has been proposed in this study to uncover the possibilities of using the nanocomposites as potential drug candidates in theranostic applications in disease treatment and diagnosis. This study would help uncover the electronic properties that play in the nano-scaled system at the material-bio interface, which would aid in designing a sensitive nano-electromechanical device bearing both the therapeutic and diagnostic attributes heralding a new horizon in the health care systems.

Implementation of biomolecular logic gate using DNA and electrically doped GaAs nano-pore: a first principle paradigm.

One of the emerging areas of today's research arena is molecular modeling and molecular computing. The molecular logic gate can be theoretically implemented from single-strand DNA which consists of four basic nucleobases. In this study, the electronic transmission characteristics of DNA chain are investigated to form the logic gate. This biomolecular single-strand DNA chain is passed through an electrically doped gallium-arsenide nano-pore to achieve reasonably improved transmission along <1 1 1> direction. Current-voltage characteristic and device density of states with HOMO-LUMO plot of the device are explained along with the conductivity of the device to confirm the characteristics of some important logic gates like a universal gate. Ultimately the property of resistivity proves the law of Boolean logic of AND gate and universal logic gate, viz., NAND and NOR gate. All the electronic properties of the Boolean logic gate are explored based on the first principle approach by non-equilibrium Green's function coupled with density functional theory in room temperature.

Electrically Doped Nanoscale Devices Using First-Principle Approach: A Comprehensive Survey.

Doping is the key feature in semiconductor device fabrication. Many strategies have been discovered for controlling doping in the area of semiconductor physics during the past few decades. Electrical doping is a promising strategy that is used for effective tuning of the charge populations, electronic properties, and transmission properties. This doping process reduces the risk of high temperature, contamination of foreign particles. Significant experimental and theoretical efforts are demonstrated to study the characteristics of electrical doping during the past few decades. In this article, we first briefly review the historical roadmap of electrical doping. Secondly, we will discuss electrical doping at the molecular level. Thus, we will review some experimental works at the molecular level along with we review a variety of research works that are performed based on electrical doping. Then we figure out importance of electrical doping and its importance. Furthermore, we describe the methods of electrical doping. Finally, we conclude with a brief comparative study between electrical and conventional doping methods.

Router design for nano-communication using actin quantum cellular automata.

Logic expressions can be designed from actin filaments. It is a protein that makes the cellular structure and plays an important role in intracellular communication. Nano communication technique has been established using actin cellular automata. Among several rules, (1, 30) and (4, 27) rules have been used to design 2 to 1 multiplexer, 4 to 1 multiplexer, 1 to 2 demultiplexer and 1 to 4 demultiplexer. Router or data selector has been made of using multiplexer and demultiplexer. Three novel circuits such as multiplexer, demultiplexer and nano-router have been designed using the projected mechanism. The primary focus of this proposed technique is on different designs of the multiplexer, demultiplexer and minimum cell count with minimum time steps. The different router circuits have been simulated with the help of Simulink by which output has been verified for different circuits. Stuck at fault analysis is also done in this study. Device density and power consumption have also been included in this study. A comparative analysis of the different designs of the router provides a better concept of circuit optimisation. Furthermore, this study analyses convenient forthcoming applications in nano-technology and nano-bio-molecular systems involving the proposed parameters.

A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients.

In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.

First principle study of the self-switching characteristics of the guanine based single optical molecular switch using carbon nanotube electrodes.

The switching property of an optical single molecular switch based on a single DNA molecule guanine with a single walled carbon nanotube electrode has been investigated using density functional theory along with non-equilibrium Green's function based first principle approach. The semi-empirical model of this single bio-molecular switch has been operated at an ultra-high 25 THz frequency in mid-UV range. This single bio-molecule comprises switching activity upon UV photo-excitation. The influence of the highest occupied molecular orbital and lowest unoccupied molecular orbital gap and the quantum ballistic transmission into the switching activity are discussed in detail in this study. It has been observed that the maximum ON-OFF ratio, i.e. 327 is obtained at +0.8 V bias voltage. Theoretical results show that current through the twisted form is sufficiently larger than the straightened form, which recommends that this structure has smart prospective application in the future generation switching nanotechnology.

First principle approach towards logic design using hydrogen-doped single-strand DNA.

Molecular logic gate has been proposed using single-strand DNA (ssDNA) consisting of basic four nucleobases. In this study, density functional theory and non-equilibrium Green's function based first principle approach is applied to investigate the electronic transmission characteristics of ssDNA chain. The heavily hydrogen-doped-ssDNA (H-ssDNA) chain is connected with gold electrode to achieve enhanced quantum-ballistic transmission along 〈1 1 1〉 direction. Logic gates OR, Ex-OR, NXOR have been implemented using this analytical model of H-ssDNA device. Enhanced logic properties have been observed for ssDNA after H adsorption due to improved electronic transmission. Dense electron cloud is considered as logic 'high' (1) output in presence of hydrogen molecule and on the contrary sparse cloud indicate logic 'low' (0) in the absence of hydrogen molecule. Device current is significantly increased from 0.2 nA to 2.4 µA (approx.) when ssDNA chain is heavily doped with hydrogen molecule. The current-voltage characteristics confirm the formation of various Boolean logic gate operations.

Differential antibacterial response exhibited by graphene nanosheets toward gram-positive bacterium Staphylococcus aureus.

Two different morphological forms of graphene nanosheets: improved reduced graphene oxide (IRGO) and modified reduced GO (rGO) (MRGO) have been synthesised by improved and modified methods, respectively. Physical characterisations of these graphene nanosheets were carried out using X-ray diffraction, Fourier transform infrared spectroscopy, and Raman spectroscopy. Colloidal stability of these nanosheets toward a selected bacterium (e.g. Staphylococcus aureus) was ascertained by zeta potential. In the present study, the authors for the first time made an attempt to study and compare the potentialities of these two different forms of graphene nanosheets as efficient bactericidal agents. Field-emission scanning electron microscopy and TEM with energy dispersive X-ray spectroscopy (EDAX) studies of IRGO and MRGO have been carried out to explore their underlying mechanism of antibacterial responses through physical as well as chemical interactions with the selected bacterial species.

Phyto-assisted synthesis of bio-functionalised silver nanoparticles and their potential anti-oxidant, anti-microbial and wound healing activities.

Bio- synthesis of silver nanoparticles (AgNPs) was made by using the aqueous leaf extract of Ardisia solanacea. Rapid formation of AgNPs was observed from silver nitrate upon treatment with the aqueous extract of A. solanacea leaf. The formation and stability of the AgNPs in the colloidal solution were monitored by UV-visible spectrophotometer. The mean particle diameter of AgNPs was calculated from the DLS with an average size ∼4 nm and ∼65 nm. ATR-FTIR spectroscopy confirmed the presence of alcohols, aldehydes, flavonoids, phenols and nitro compounds in the leaf which act as the stabilizing agent. Antimicrobial activity of the synthesized AgNPs was performed using agar well diffusion and broth dilution method against the Gram-positive and Gram-negative bacteria. Further, robust anti-oxidative potential was evaluated by DPPH assay. The highest antimicrobial activity of synthesized AgNPs was found against Pseudomonas aeruginosa (28.2 ± 0.52 mm) whereas moderate activity was found against Bacillus subtilis (16.1 ± 0.76), Candida kruseii (13.0 ± 1.0), and Trichophyton mentagrophytes (12.6 ± 1.52). Moreover, the potential wound healing activity was observed against the BJ-5Ta normal fibroblast cell line. Current research revealed that A. solanacea was found to be a suitable source for the green synthesis of silver nanoparticles.

A Systematic Review of Wearable Systems for Cancer Detection: Current State and Challenges.

Rapid growth of sensor and computing platforms have introduced the wearable systems. In recent years, wearable systems have led to new applications across all medical fields. The aim of this review is to present current state-of-the-art approach in the field of wearable system based cancer detection and identify key challenges that resist it from clinical adoption. A total of 472 records were screened and 11 were finally included in this study. Two types of records were studied in this context that includes 45% research articles and 55% manufactured products. The review was performed per PRISMA guidelines where considerations was given to records that were published or reported between 2009 and 2017. The identified records included 4 cancer detecting wearable systems such as breast cancer (36.3%), skin cancer (36.3%), prostate cancer (18.1%), and multi-type cancer (9%). Most works involved sensor based smart systems comprising of microcontroller, Bluetooth module, and smart phone. Few demonstrated Ultra-Wide Band (i.e. UWB) antenna based wearable systems. Skin cancer detecting wearable systems were most comprehensible ones. The current works are gradually progressing with seamless integration of sensory units along with smart networking. However, they lack in cloud computing and long-range communication paradigms. Artificial intelligence and machine learning are key ports that need to be attached with current wearable systems. Further, clinical inertia, lack of awareness, and high cost are altogether pulling back the actual growth of such system. It is well comprehended that upon sincere orientation of all identified challenges, wearable systems would emerge as vital alternative to futuristic cancer detection.

Atomic scale modeling of electrically doped p-i-n FET from adenine based single wall nanotube.

The Field Effect Transistor (FET) characteristics has been observed from a single-walled Adenine nanotube device using Density Functional Theory associated with Non Equilibrium Green's Function based First Principle approach. This device is electrically doped which shows both n and p channel characteristics of a p-i-n FET. This device is designed and originated from a single-walled biomolecular nanotube structure. The p and n regions have been induced at the two ends of the device using electrical doping process. Thus both n and p channel current-voltage response can be obtained within a single nano-scale device at room temperature operation. The device is 3.35nm long and 1.4nm wide. The quasi-ballistic quantum transmission property reveals impressive and almost ideal current-voltage characteristics of the FET. Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) gap reveals the possibility of quasi-ballistic coherent transmission of the device. The electronic properties based on Molecular Projected Self-consistent Hamiltonian are analyzed using Hilbert space spanned basis functions. The maximum tunneling current observed for the bio-molecular FET is 15.9µA for n-channel and 13.8µA for p-channel. The device is operated in atomic scale regime with 1000THz frequency. The present results reveal the role of quantum-ballistic tunneling phenomenon in the current-voltage characteristics and channel conductance properties of the bio nanotube structure, which is useful in future generation nano-electronics.

Design of smart neonatal health monitoring system using SMCC.

Automated health monitoring and alert system development is a demanding research area today. Most of the currently available monitoring and controlling medical devices are wired which limits freeness of working environment. Wireless sensor network (WSN) is a better alternative in such an environment. Neonatal intensive care unit is used to take care of sick and premature neonates. Hypothermia is an independent risk factor for neonatal mortality and morbidity. To prevent it an automated monitoring system is required. In this Letter, an automated neonatal health monitoring system is designed using sensor mobile cloud computing (SMCC). SMCC is based on WSN and MCC. In the authors' system temperature sensor, acceleration sensor and heart rate measurement sensor are used to monitor body temperature, acceleration due to body movement and heart rate of neonates. The sensor data are stored inside the cloud. The health person continuously monitors and accesses these data through the mobile device using an Android Application for neonatal monitoring. When an abnormal situation arises, an alert is generated in the mobile device of the health person. By alerting health professional using such an automated system, early care is provided to the affected babies and the probability of recovery is increased.