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Comprehensive review on ethnomedicinal, phytochemistry and pharmacological profile of Ficus Religiosa L.

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Journal of Traditional Chinese Medicine 2024年第5期44卷 1052-1057页

作者： Rahul Rawat Harish kumar Neetu Singh Aakash deep Balasubramanian Narasimhan Surender Singh Yadav Sanjiv kumar Department of Pharmaceutical Sciences Chaudhary Bansi Lal University Department of Botany Maharshi Dayanand University Faculty of Pharmaceutical Sciences Maharshi Dayanand University

Ficus religiosa L.(F. religiosa) or sacred fig is a large perennial tree belonging to the family Moraceae or mulberry family. Though the tree has pan-tropical distribution but originally it is indigenous to the Indian subcontinent and Indochina region. Popularly the tree is named "Pepal or bodhi tree". Traditionally, it is practiced for the treatment of asthma, nose bleeding, heart disorders, diabetes, wound healing, ear problems, constipation, hyperlipidemia, gonorrhea, ulcers and infectious disorders. Chemical analysis demonstrated the presence of numerous bioactives including tannins, phenols, saponins, sugars, alkaloids, methionine, terpenoids, flavonoids, glycosides, proteins, separated amino acids, essential and volatile oils and steroids etc., which are probably responsible for its diverse pharmacological actions. The present work is an attempt to compile up-to-date comprehensive information on F. religiosa that covers its taxonomy, ethnomedicinal importance, phytochemistry, pharmacological attributes and clinical trials. keeping in mind the various health attributes of F. religiosa, future research can be aimed at in-depth elucidation of the structure-function relationship and multifactorial signalings pathways.

关键词： Ficus religiosa clinical trials ethnomedicine health benefits pharmacology phytoconstituents

Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1613-1632页

作者： k.CHANDAN k.kARTHIk k.V.NAGARAJA B.C.PRASANNAkumarA R.S.VARUN kumar T.MUHAMMAD Amrita School of Artificial Intelligence Amrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of Studies in Mathematics Davangere UniversityDavangere 577002KarnatakaIndia Computational Science Lab Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of Pure and Applied Mathematics School of Mathematical SciencesSunway UniversityPetaling Jaya 47500Malaysia Department of Mathematics College of ScienceKing Khalid UniversityAbha 61413Saudi Arabia

The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)*** governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless ***,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical ***,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network *** results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

关键词： heat transfer fin porous fin local thermal non-equilibrium(LTNE)model physics-informed neural network(PINN)

Prediction of bearing capacity of pile foundation using deep learning approaches

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 870-886页

作者： Manish kumar Divesh Ranjan kumar Jitendra kHATTI Pijush SAMUI kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

A deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 2117-2137页

作者： Ahmed Barnawi krishan kumar Neeraj kumar Bander Alzahrani Amal Almansour Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyDeemed to be UniversityPatialaIndia

Landmines continue to pose an ongoing threat in various regions around the world,with countless buried landmines affecting numerous human *** detonation of these landmines results in thousands of casualties reported worldwide ***,there is a pressing need to employ diverse landmine detection techniques for their *** effective approach for landmine detection is UAV(Unmanned Aerial Vehicle)based AirborneMagnetometry,which identifies magnetic anomalies in the local terrestrial magnetic *** can generate a contour plot or heat map that visually represents the magnetic field *** the effectiveness of this approach,landmine removal remains a challenging and resource-intensive task,fraughtwith *** computing,on the other hand,can play a crucial role in critical drone monitoring applications like landmine *** processing data locally on a nearby edge server,edge computing can reduce communication latency and bandwidth requirements,allowing real-time analysis of magnetic field *** enables faster decision-making and more efficient landmine detection,potentially saving lives and minimizing the risks involved in the ***,edge computing can provide enhanced security and privacy by keeping sensitive data close to the source,reducing the chances of data exposure during *** paper introduces the MAGnetometry Imaging based Classification System(MAGICS),a fully automated UAV-based system designed for landmine and buried object detection and *** have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset *** simulating the proposal in various network scenarios,we have successfully detected landmine signatures present in themagnetometry *** trained models exhibit significant performance improvements,achieving a maximum mean average precision value of 97.8%.

关键词： CNN deep learning landmine detection magnetometer mean average precision UAV

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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Computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil k.Singh Sudhakar kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

Traffic Engineering and Quality of Service in Hybrid Software Defined Networks

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China Communications 2024年第2期21卷 96-121页

作者： Samiullah Mehraban Rajesh kumar Yadav IOT Lab Department of Computer Science&EngineeringDelhi Technological UniversityDelhi 110042India

For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: deep Reinforcement Learning(DRL), Heuristic algorithm, k path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.

关键词： DRL hSDN QoE QoS SDN TE

Investigation of proton structure function F_(2)^(p)at HERA in light of an analytical solution to the Balitsky–kovchegov equation

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Communications in Theoretical Physics 2024年第3期76卷 120-126页

作者： Ranjan Saikia Pragyan Phukan Jayanta kumar Sarma HEP laboratory Department of PhysicsTezpur UniversityTezpurAssam-784028India Department of Physics Moran CollegeMoranhatAssam-785670India

In this paper,the proton structure function F_(2)^(p)(x,Q^(2))at small-x is investigated using an analytical solution to the Balitsky–kovchegov(Bk)*** the context of the color dipole description of deep inelastic scattering(DIS),the structure function F_(2)^(p)(x,Q^(2))is computed by applying the analytical expression for the scattering amplitude N(k,Y)derived from the Bk *** transverse momentum k and total rapidity Y,the scattering amplitude N(k,Y)represents the propagation of the quark-antiquark dipole in the color dipole description of *** the Bk solution we extracted the integrated gluon density xg(x,Q^(2))and then compared our theoretical estimation with the LHAPDF global data fits,NNPDF3.1sx and ***,we have investigated the behavior of F_(2)^(p)(x,Q^(2))in the kinematic region of 10^(-5)≤x≤10^(-2)and 2.5 GeV^(2)≤Q^(2)≤60 GeV^(2).Our predicted results for F_(2)^(p)(x,Q^(2))within the specified kinematic region are in good agreement with the recent high-precision data for F_(2)^(p)(x,Q^(2))from HERA(H1 Collaboration)and the LHAPDF global parametrization group NNPDF3.1sx.

关键词： quantum chromodynamics(QCD) perturbative QCD(pQCD) proton structure function small-x Balitsky-kovchegov equation

Unlocking new potential of clinical diagnosis with artificial intelligence:Finding new patterns of clinical and lab data

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World Journal of Diabetes 2024年第3期15卷 308-310页

作者： Pradeep.kumar Dabla Department of Biochemistry Govind Ballabh Pant Institute of Postgraduate Medical Education and ResearchDelhi 110002India

Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)*** are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle ***-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician *** medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful *** technological advancements enhance the computational analyses for both research and clinical *** plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor *** potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy ***,an AI ethics framework is essential to guide its responsible *** AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.

关键词： Laboratory medicine Artificial intelligence Machine learning Association rule mining Cardiovascular diseases

Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

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Journal of Safety Science and Resilience 2024年第1期5卷 91-101页

作者： Sachin Dhawan Hemanta kumar Bhuyan Subhendu kumar Pani Vinayakumar Ravi Rashmi Gupta Arun Rana Alanoud Al Mazroa Dorset College King George BlvdSurreyBritish ColumbiaCanada Department of Information Technology Vignan’s Foundation for ScienceTechnology and Research(Deemed to be University)Andhra PradeshIndia krupajal Computer Academy Biju Patnaik University of Technology(BPUT)Rourkela 751002India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia NSUT East Campus(Formerly AIACTRDelhi 110031India(Affiliated to Guru Gobind Singh Inderaprastha University))Dwarka 110078India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida 201310Uttar PradeshIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia

The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as ***,this progress has also led to security concerns related to the transmission of confidential ***,safeguarding these data during communication through insecure channels is crucial for obvious *** emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation *** to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification *** simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual *** the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.

关键词： Image enhancement Image processing Steganography deep learning Fuzzy neural combination