Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various disciplines.The most sticky topic in organizationalfields was MANET protection.MANET is helpless against various threats that affect its usability and accessibility.The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious node.The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node.Ad-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center.We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits.The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology.We implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation models.Furthermore,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Cardiovascular disease is among the top five fatal diseases that affect lives worldwide.Therefore,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier stages.Machine learning(ML)techniques are used to assist healthcare providers in better diagnosing heart disease.This study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart disease.The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository.Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics.Specifically,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method.Results were recorded before and after the data preprocessing techniques were applied.Out of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed model.The proposed model yielded better results with gradient boosting in terms of precision,recall,and f1-score.It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

Assessment of groundwater quality with special emphasis on nitrate contamination in parts of Gautam Budh Nagar district,Uttar Pradesh, India

Progressive developments in industrial and agricultural activities are causing a critical stress on groundwater quality in developing countries.The objective of this paper is to assess and evaluate the contamination level of groundwater caused by leachate in 11 villages of the Gautam Budh Nagar district in Uttar Pradesh,India.We systematically sampled 22 groundwater samples and 13 leachate samples to ascertain the source of pollution on groundwater quality.The standard analytical methods given by the American Public Health Association(APHA)(Standard methods for examination of water and wastewater,23rd edn.APHA,AWWA,WPCF,Washington,2017)were used for quantitative estimation of hydrochemical parameters of collected samples.The results of the analysis of groundwater samples indicate that pH values range from 7.31 to 8.97.The mean magnesium concentration in groundwater samples is 58.93±21.44 mg/L.Out of the groundwater samples taken,approximately 41%and 73%of samples analysis results have been found beyond the acceptable limit with respect to the parameters of turbidity and total dissolved solids,respectively,according to the Bureau of Indian Standards(Indian standard specification for drinking water(IS:10500).BIS,Manak Bhawan,New Delhi,2012)for drinking water.Around 95.4%of groundwater samples and 92.3%of leachate samples have high nitrate concentrations above the standard limit of BIS(45 mg/L),respectively.The Piper plot shows that 50%of the samples belong to the Ca^2+-Mg^2+-HCO3^-type.Ternary and Durov's diagrams indicate that the mean concentrations of ions are in the order of Na^+>Mg^2+>Ca^2+>K^+(for cations)and HCO3^->NO3^->C1^->SO42^->CO32^->F-(for anions)in groundwater of the study area.The spatial variation of the hydrochemical parameters shows that groundwater is heavily contaminated with respect to nitrate.Analytical results indicate that the groundwater of villages Achheja,Bisrakh road,Dujana,Badalpur and Sadopur is not suitable for drinking.

Optimal Confidential Mechanisms in Smart City Healthcare

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely manner.The system also concedes the freedom of a patient.IoT is a part of this system and it helps in providing care to the patients.IoTbased healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate care.Existing methodologies exhibit few shortcomings in terms of computational complexity,cost and data security.Hence,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT cipher.This procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam optimization.The key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box models.The study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the establishments.The methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing threats.Healthcare service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare service.The proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access policies.The model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum range.Then,the level of the model and throughput were analyzed by maximum value i.e.,50Mbps/sec and 95.56%for PRESENT-Authorization cipher to achieve smart city security.The proposed model achieved better results than the existing methodologies.

Modified approximation and error estimation for King’s type(p,q)-BBH operators

In this paper,the King’s type modification of(p,q)-Bleimann-Butzer and Hahn operators is defined.Some results based on Korovkin’s approximation theorem for these new operators are studied.With the help of modulus of continuity and the Lipschitz type maximal functions,the rate of convergence for these new operators are obtained.It is shown that the King’s type modification have better rate of convergence,flexibility than classical(p,q)-BBH operators on some subintervals.Further,for comparisons of the operators,we presented some graphical examples and the error estimation in the form of tables through MATLAB(R2015a)

灰度模糊算法优化Al-SiC-Gr混合金属基复合材料的加工参数(英文)

石墨颗粒增强金属基复合材料能够提供更好的切削加工性能和摩擦性能。用灰度模糊算法优化Al-SiC-Gr混合金属基复合材料的加工参数,以获得到具有优秀综合性能的材料。当混合金属基复合材料中SiC-Gr的质量分数分别为5%、7.5%和10%时,对应的拉伸强度分别为170、210和204 MPa。另外,与另外2种材料相比,Al-10%(SiC-Gr)复合材料具有更好的切削加工性能。与其他的灰度技术相比,灰度模糊逻辑算法在输出方面提高了推理的合理性,降低了不确定性。实验结果表明,在设置的相同加工参数下,与其他的灰度技术相比,灰度模糊逻辑算法的推理合理性从0.619提高到0.891,且同时保证材料具有更好的综合性能。

Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks

In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthcare WBANs are the black hole and sink hole attacks.Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path.Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness.This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score(PCS)and an MK-Means algorithm,which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory issues.First,the gathered training data feature count is reduced through data pre-processing in the PCS.Second,the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification.Third,certain attack detection measures given by the intrusion detection system,such as the number of data packages trans-received,are identified by the MK-Means algorithm.This study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate,low communication overhead,and reduced end-to-end delay in the network and improves the accuracy of biomedical data.

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.

COVID-19-associated mucormycosis and treatments

In the current pandemic,COVID-19 patients with predisposing factors are at an increased risk of mucormycosis,an uncommon angioinvasive infection that is caused by fungi with Mucor genus which is mainly found in plants and soil.Mucormycosis development in COVID-19 patient is related to various factors,such as diabetes,immunocompromise and neutropenia.Excessive use of glucocorticoids for the treatment of critically ill COVID-19 patients also leads to opportunistic infections,such as pulmonary aspergillosis.COVID-19 patients with mucormycosis have a very high mortality rate.This review describes the pathogenesis and various treatment approaches for mucormycosis in COVID-19 patients,including medicinal plants,conventional therapies,adjunct and combination therapies.

Behaviour of Earth Dam under Seismic Load Considering Nonlinearity of the Soil

The stability and safety are very important issues for the dam structure which are built in seismic regions. The dam body consists of soil materials that behave nonlinearly modelled with finite elements. The numerical investigation employs a fully nonlinear finite element analysis considering linear and elastic-plastic constitutive model to describe the material properties of the soil. In this paper, seismic analysis of an earthen dam is carried out using Geo-Studio software based on finite element method. Initially, the in-situ stress state analysis has been done before the earthquake established, and then its results are used in the seismic analysis as a parent analysis. A complete parametric study is carried out to identify the effects of input motion characteristics, soil behaviour and strength of the shell and core materials on the dynamic response of earthen dams. The real earthquake record is used as input motions. The analysis gives the overall pattern of the dam behaviour in terms of contours of displacements and stresses.

Ba_(0.85)Ca_(0.15)Zr_(0.1)Ti_(0.88)Sn_(0.02)O_3-水泥复合材料的电热行为（英文）

寻找用于土木工程结构的多功能材料的努力引起全球研究人员的兴趣。在不同Ba_(0.85)Ca_(0.15)Zr_(0.1)Ti_(0.88)Sn_(0.02)O_3(BCZT-Sn)与水泥配比条件下,制备适合于电热应用的水泥基BCZT-Sn复合材料。水泥基复合材料的磁滞回线表现出一定的饱和特征,纯BCZT-Sn样品的磁滞回线则由于其铁电性质而饱和。此外,首次探索这种复合材料在固态制冷技术中的应用,即其电热效应(ECE)。在0～29 kV/cm的电场作用下,纯BCZT-Sn、含10%水泥及15%水泥的复合材料的热力学温度变化分别为0.71、0.64和0.50K,等温熵变分别为0.86、0.80和0.65 J/(kg·K)。水泥基复合材料的热力学温度变化与BCZT-Sn铁电陶瓷的相当。此外,具有不同陶瓷含量的复合材料的室温介电常数(er)数随着复合材料中BCZT-Sn含量的增加而增大。由于其优异的性能,这种水泥基BCZT-Sn复合材料有望用于固态制冷技术中。

A Multi-Agent Framework for Operation of a Smart Grid

This paper presents the operation of a Multi-agent system (MAS) for the control of a smart grid. The proposed Multi-agent system consists of seven types of agents: Single Smart Grid Controller (SGC), Load Agents (LAGs), a Wind Turbine Agent (WTAG), Photo-Voltaic Agents (PVAGs), a Micro-Hydro Turbine Agent (MHTAG), Diesel Agents (DGAGs) and a Battery Agent (BAG). In a smart grid LAGs act as consumers or buyers, WTAG, PVAGs, MHTAG & DGAGs acts as producers or sellers and BAG act as producer/consumer or seller/buyer. The paper demonstrates the use of a Multi-agent system to control the smart grid in a simulated environment. In order to validate the performance of the proposed system, it has been applied to a simple model system with different time zone i.e. day time and night time and when power is available from the grid and when there is power shedding. Simulation results show that the proposed Multi-agent system can perform the operation of the smart grid efficiently.

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

Impact of polyherbal formulations MEF-4 and MEF-8 on high fat diet induced obesity in SD rats

As a traditional healing system of India,herbal medicines have been used as therapeutics over a long period of time.In the present study,polyherbal formulations MEF-4 and MEF-8 from two pharmacologically important plants,Camelia Sinensis(CS)and Citrus Aurantium(CA),were studied in vivofor their potential in reducing obesity and hyperlipidemia.These twoformulations are different from each other in the manner that they contain two different polymers and that MEF-4 was prepared through cold extraction of CS and Cl and MEF-8 was prepared through hot extraction method.Hyperlidemia leads to many complications like hypertension,stroke and heart attack.It also creates complications to the patients suffering from chronic disorders like diabetes and stress.The aim of the present investigation is to assess the effect of these twoformulations on the possibility of reducing obesity.The rodents enlisted in hyperlipidemia had taken a high-fat diet for about 14 d.The hyperlipidemic rodents of 250 g were taken in the study,treated with standard eating regimen and high-fat eating regimen orally for 30 d.After treatment for 30 d,blood tests from the MEF-4/MEF-8/standard/vehicle were gathered and an impact on body weight,hematology and lipid profile was investigated.Statistical analysis showed there was a significant increase in cholesterol(CHL),low density lipoprotein(LDL),triglyceride(TG),phospholipids(PHL)change in the body weight of group Ⅰ(HFD)and Ⅱ(STD)as compared to group Ⅲ(MEF-4)and group IV(MEF-8).The decline in triglyceride(P<0.01)was also noted for MEF-8 when compared to control and standard.

Robust Digital Audio Watermarking Based on SVD and Modified Firefly Algorithm

Digital Watermarking is a technology, to facilitate the authentication, copyright protection and Security of digital media. The objective of developing a robust watermarking technique is to incorporate the maximum possible robustness without compromising with the transparency. Singular Value Decomposition (SVD) using Firefly Algorithm provides this objective of an optimal robust watermarking technique. Multiple scaling factors are used to embed the watermark image into the host by multiplying these scaling factors with the Singular Values (SV) of the host audio. Firefly Algorithm is used to optimise the modified host audio to achieve the highest possible robustness and transparency. This approach can significantly increase the quality of watermarked audio and provide more robustness to the embedded watermark against various attacks such as noise, resampling, filtering attacks etc.

An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.

Genome tuning through HLA and KIR gene clusters impact susceptibility to dengue

Dengue is amongst the most prevalent viral diseases which globally affects millions of individuals annually and renders billions at risk,particularly in tropical and sub-tropical nations.WHO estimated 100-400 mil-lion infections each year and reported 4.2 million active cases in 2019 worldwide.The infection is caused by arthropod-transmitted dengue virus which is known to have 5 serotypes(DENV1-5).Most of the cases show mild clinical symptoms;though others may develop severe forms viz;dengue hemorrhagic fever and dengue shock syndrome.Though limited literature suggests the population-specific genetic influence on susceptibility and the clinical course of dengue;the genetic propensity of dengue is largely unknown in most ethnicities.In this context,the human leukocyte antigen(HLA)system represents the most polymorphic region of the human genome and is crucial for the initiation of an appropriate immune response.In most of the genome-wide association studies,the HLA complex is the most significantly linked genetic region with susceptibility or protection towards vari-ous infectious and noninfectious diseases.Killer immunoglobulin-like receptors represent another highly variable system present on the surface of natural killer(NK)cells which regulate the activity of NK cells through inter-actions with their cognate HLA ligands.It is conceivable that the interaction of HLA-Killer immunoglobulin-like receptors systems influences the host susceptibility towards dengue infection as well the disease outcome.Here we attempt to review these parameters in dengue infection and disease outcome.Further detailed investigations are warranted towards the identification of novel susceptibility markers and targeted therapeutic interventions.

Medicinal significance,pharmacological activities,and analytical aspects of ricinine:A concise report

Ricinine(3-cyano-4-methoxy-N-methyl-2-pyridone)is an alkaloid present in leaves and seeds of castor plant i.e.Ricinus communis.It can cause vomiting,convulsions,hypotension,liver and kidney damage and several other complications in human.Ricinine presents mainly in young plant and it is the only cyano-substituted pyridine compounds occurred naturally.Ricinine also found in some other plants such as Piper nigrum,Discocleidion rufescens,Aparisthmium cordatum and Nicotiana tabacum.Accidental and intended Ricinus communis intoxications in humans and animals have been known for centuries.In the present review,we summarize the information regarding its medicinal uses,pharmacological activities,analytical techniques and intended and unintended poisoning cases in humans and animals.This review will be beneficial for the researcher in the field of herbal medicine and other allied sciences.