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 ...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.展开更多
Quantum-dot cellular automata (QCA) based on cryptography is a new paradigm in the field of nanotechnology. The overall performance of QCA is high compared to traditional complementary metal-oxide semiconductor (CMOS)...Quantum-dot cellular automata (QCA) based on cryptography is a new paradigm in the field of nanotechnology. The overall performance of QCA is high compared to traditional complementary metal-oxide semiconductor (CMOS) technology. To achieve data security during nanocommunication, a cryptography-based application is proposed. The devised circuit encrypts the input data and passes it to an output channel through a nanorouter cum data path selector, where the data is decrypted back to its original form. The results along with theoretical implication prove the accuracy of the circuit. Power dissipation and circuit complexity of the circuit have been analyzed.展开更多
Quantum-dot cellular automata(QCA) is an emerging area of research in reversible computing. It can be used to design nanoscale circuits. In nanocommunication, the detection and correction of errors in a received messa...Quantum-dot cellular automata(QCA) is an emerging area of research in reversible computing. It can be used to design nanoscale circuits. In nanocommunication, the detection and correction of errors in a received message is a major factor. Besides, device density and power dissipation are the key issues in the nanocommunication architecture. For the first time, QCA-based designs of the reversible low-power odd parity generator and odd parity checker using the Feynman gate have been achieved in this study. Using the proposed parity generator and parity checker circuit, a nanocommunication architecture is proposed. The detection of errors in the received message during transmission is also explored. The proposed QCA Feynman gate outshines the existing ones in terms of area, cell count, and delay. The quantum costs of the proposed conventional reversible circuits and their QCA layouts are calculated and compared, which establishes that the proposed QCA circuits have very low quantum cost compared to conventional designs. The energy dissipation by the layouts is estimated, which ensures the possibility of QCA nano-device serving as an alternative platform for the implementation of reversible circuits. The stability of the proposed circuits under thermal randomness is analyzed, showing the operational efficiency of the circuits. The simulation results of the proposed design are tested with theoretical values, showing the accuracy of the circuits. The proposed circuits can be used to design more complex low-power nanoscale lossless nanocommunication architecture such as nano-transmitters and nano-receivers.展开更多
Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement ...Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems.Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud.This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system.Wireless sensor nodes are used to monitor the biosignals dynamically.All accrued data is temporarily stored in the dew layer.It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data.Data synchronization plays an essential role in the cloud dew framework.We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud.Sometimes sensors produce erroneous data,which poses a significant threat to the sustainable health monitoring system.Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers.Further,we compared the proposed MedGini model with the existing models and analyzed the system performance.MedGini is found to outperform others concerning cost and power consumption.展开更多
文摘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.
文摘Quantum-dot cellular automata (QCA) based on cryptography is a new paradigm in the field of nanotechnology. The overall performance of QCA is high compared to traditional complementary metal-oxide semiconductor (CMOS) technology. To achieve data security during nanocommunication, a cryptography-based application is proposed. The devised circuit encrypts the input data and passes it to an output channel through a nanorouter cum data path selector, where the data is decrypted back to its original form. The results along with theoretical implication prove the accuracy of the circuit. Power dissipation and circuit complexity of the circuit have been analyzed.
基金Project supported by the University Grants Commission of India(No.41-631/2012(S.R.))
文摘Quantum-dot cellular automata(QCA) is an emerging area of research in reversible computing. It can be used to design nanoscale circuits. In nanocommunication, the detection and correction of errors in a received message is a major factor. Besides, device density and power dissipation are the key issues in the nanocommunication architecture. For the first time, QCA-based designs of the reversible low-power odd parity generator and odd parity checker using the Feynman gate have been achieved in this study. Using the proposed parity generator and parity checker circuit, a nanocommunication architecture is proposed. The detection of errors in the received message during transmission is also explored. The proposed QCA Feynman gate outshines the existing ones in terms of area, cell count, and delay. The quantum costs of the proposed conventional reversible circuits and their QCA layouts are calculated and compared, which establishes that the proposed QCA circuits have very low quantum cost compared to conventional designs. The energy dissipation by the layouts is estimated, which ensures the possibility of QCA nano-device serving as an alternative platform for the implementation of reversible circuits. The stability of the proposed circuits under thermal randomness is analyzed, showing the operational efficiency of the circuits. The simulation results of the proposed design are tested with theoretical values, showing the accuracy of the circuits. The proposed circuits can be used to design more complex low-power nanoscale lossless nanocommunication architecture such as nano-transmitters and nano-receivers.
文摘Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems.Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud.This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system.Wireless sensor nodes are used to monitor the biosignals dynamically.All accrued data is temporarily stored in the dew layer.It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data.Data synchronization plays an essential role in the cloud dew framework.We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud.Sometimes sensors produce erroneous data,which poses a significant threat to the sustainable health monitoring system.Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers.Further,we compared the proposed MedGini model with the existing models and analyzed the system performance.MedGini is found to outperform others concerning cost and power consumption.