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

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

Effect of electroslag remelting and homogenization on hydrogen flaking in AMS-4340 ultra-high-strength steels

Hydrogen flakes and elemental segregation are the main causes of steel rejection. To eliminate hydrogen flaking, the present study focuses on the manufacture of AMS-4340 ultra-high-strength steel through an alternate route. AMS-4340 was prepared using three different processing routes. The primary processing route consisted of melting in an electric arc furnace, refining in a ladle refining furnace, and vacuum degassing. After primary processing, the heat processes(D1, D2, and D3) were cast into cylindrical electrodes. For secondary processing, electroslag remelting(ESR) was carried out on the primary heats to obtain four secondary heats: E1, E2, E3, and E4. Homogenization of ingots E1, E2, E3, and E4 was carried out at 1220°C for 14, 12, 12, and 30 h, respectively, followed by an antiflaking treatment at 680°C and air cooling. In addition, the semi-finished ESR ingot E4 was again homogenized at 1220°C for 6–8 h and a second antiflaking treatment was performed at 680°C for 130 h followed by air cooling. The chemical segregation of each heat was monitored through a spectroscopy technique. The least segregation was observed for heat E4. Macrostructure examination revealed the presence of hydrogen flakes in heats E1, E2, and E3, whereas no hydrogen flakes were observed in heat E4. Ultrasonic testing revealed no internal defects in heat E4, whereas internal defects were observed in the other heats. A grain size investigation revealed a finer grain size for E4 compared with those for the other heats. Steel produced in heat E4 also exhibited superior mechanical properties. Therefore, the processing route used for heat E4 can be used to manufacture an AMS-4340 ultra-high-strength steel with superior properties compared with those of AMS-4340 prepared by the other investigated routes.

Effect of interaction of the various modes on the radial velocity curves of the polytropic models of rotationally and tidally distorted pulsating variable stars

In our previous work, we developed a model to study the effects of rotation and/or tidal distortions on anharmonic radial oscillations and hence on the radial velocity curves of the polytropic models of pulsating variable stars.We considered the first three modes(fundamental and the next two higher modes) for the polytropic models of index 1.5 and 3.0 in that work.In the present paper, we are further extending our previous work to study the effect of the interaction of various modes on anharmonic radial oscillations and hence on radial velocity curves of the rotationally and/or tidally distorted polytropic models of pulsating variable stars.For this purpose, we have considered the following cases:(i) fundamental mode(ii) fundamental and the first mode,(iii) fundamental and the next two modes and finally(iv) fundamental and the next three higher modes of pulsation in our study.The objective of this paper is also to investigate whether the interaction of various modes affects the results of our previous study or not.The results of this study show that the interaction of the fundamental mode with higher modes appreciably changes the shape of the radial velocity curve of rotationally distorted and rotationally and tidally distorted polytropic models of pulsating variable stars.

Screening of COVID-19 Patients Using Deep Learning and IoT Framework

In March 2020,the World Health Organization declared the coronavirus disease(COVID-19)outbreak as a pandemic due to its uncontrolled global spread.Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease.However,the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing.To handle COVID19 testing problems,we apply the Internet of Things and artificial intelligence to achieve self-adaptive,secure,and fast resource allocation,real-time tracking,remote screening,and patient monitoring.In addition,we implement a cloud platform for efficient spectrum utilization.Thus,we propose a cloudbased intelligent system for remote COVID-19 screening using cognitiveradio-based Internet of Things and deep learning.Specifically,a deep learning technique recognizes radiographic patterns in chest computed tomography(CT)scans.To this end,contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality.The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator.Then,a deep transfer learning model,VGG-16,is trained to diagnose a suspected CT scan as either COVID-19 positive or negative.Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy,sensitivity,and specificity.The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.

Prediction of BRIC Stock Price Using ARIMA,SutteARIMA,and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide.The outbreak became a major epidemic,engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline.This paper identifies two different models to capture the trend of closing stock prices in Brazil(BVSP),Russia(IMOEX.ME),India(BSESN),and China(SSE),i.e.,(BRIC)countries.We predict the stock prices for three daily time periods,so appropriate preparations can be undertaken to solve these issues.First,we compared the ARIMA,SutteARIMA and Holt-Winters(H-W)methods to determine the most effective model for predicting data.The stock closing price of BRIC country data was obtained from Yahoo Finance.That data dates from 01 November 2019 to 11 December 2020,then divided into two categories-training data and test data.Training data covers 01 November 2019 to 02 December 2020.Seven days(03December 2020 to 11December 2020)of datawas tested to determine the accuracy of the models using training data as a reference.To measure the accuracy of the models,we obtained the means absolute percentage error(MAPE)and mean square error(MSE).Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price(BVSP)while MAPE(0.50)and MSE(579272.65)with Holt-Winters(smaller than ARIMA and SutteARIMA),model SutteARIMA was found most appropriate to predict the stock prices of Russia(IMOEX.ME),India(BSESN),and China(SSE)when compared to ARIMA and Holt-Winters.MAPE andMSE with SutteARIMA:Russia(MAPE:0.7;MSE:940.20),India(MAPE:0.90;MSE:207271.16),and China(MAPE:0.72;MSE:786.28).Finally,Holt-Winters predicted the daily forecast values for the Brazil stock price(BVSP)(12 December to 14 December 2020 i.e.,115757.6,116150.9 and 116544.1),while SutteARIMA predicted the daily forecast values of Russia stock prices(IMOEX.ME)(12 December to 14 December 2020 i.e.,3238.06,3241.54 and 3245.01),India stock price(BSESN)(12 December to 14 December 2020 i.e.,.45709.38,45828.71 and 45948.05),and China stock price(SSE)(11 December to 13 December 2020 i.e.,3397.56,3390.59 and 3383.61)for the three time periods.

IoT-Based Women Safety Gadgets (WSG): Vision, Architecture, and Design Trends

In recent years,the growth of female employees in the commercial market and industries has increased.As a result,some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety.The exponential increase in assaults and attacks on women,on the other hand,is posing a threat to women’s growth,development,and security.At the time of the attack,it appears the women were immobilized and needed immediate support.Only self-defense isn’t sufficient against abuse;a new technological solution is desired and can be used as quickly as hitting a switch or button.The proposed Women Safety Gadget(WSG)aims to design a wearable safety device model based on Internet-of-Things(IoT)and Cloud Technology.It is designed in three layers,namely layer-1,having an android app;layer-2,with messaging and location tracking system;and layer-3,which updates information in the cloud database.WSG can detect an unsafe condition by the pressure sensor of the finger on the artificial nail,consequently diffuses a pepper spray,and automatically notifies the saved closest contacts and police station through messaging and location settings.WSG has a response time of 1000 ms once the nail is pressed;the average time for pulse rate measure is 0.475 s,and diffusing the pepper spray is 0.2–0.5 s.The average activation time is 2.079 s.

Effect of cooling media on bead geometry,microstructure,and mechanical properties of wire arc additive manufactured IN718 alloy

This work aims to present and explore thermal management techniques for the wire arc additive manufacturing(WAAM)of IN718 components.Excessive heat can be mitigated via air or water cooling.In this study,the material was deposited under four different heat-input conditions with air or water cooling.In air cooling,the layer is deposited in a normal atmospheric air environment,whereas with water cooling,the material is deposited inside a water tank by varying the water level.To validate the air and water cooling thermal management techniques,IN718 single-pass and multilayer linear walls were deposited using the bidirectional gas metal arc welding based WAAM setup under four different heat input conditions.During the deposition of single layers,the temperature profiles were recorded,and the geometric and microstructural features were explored.For multilayer wall structures,the mechanical properties(hardness,tensile strength,and elongation)were determined and assessed using the corresponding microstructural features explored through scanning electron microscopy(SEM),energy dispersive spectroscopy(EDS),and electron backscatter diffraction(EBSD)analyses.The microstructure observed through SEM analysis in the building direction was found to be nonhomogenous compared with that in the deposition direction.Moreover,water cooling was found to govern bead characteristics,such as wall width and height.The grain size and anisotropy of the mechanical properties also decreased in the water-cooled case.Hence,water cooling is an economical and efficient method to mitigate excessive heat accumulation in WAAM-deposited IN718.

Influence of Metal Oxides on the Arc Erosion Behaviour of Silver Metal Oxides Electrical Contact Materials

In the present work investigations have been made to see the role of metal oxides on the performance of the silver metal oxides electrical contact materials. Silver metal oxide materials of three different compositions Ag-10CdO, Ag-7.6SnO2-2.3ln2O3 and Ag-10ZnO were prepared by internal oxidation process under identical processing conditions. These materials were tested for electrical conductivity, hardness, and erosion loss. Performing an accelerated test on the actual contactor assessed the electrical performance, involving erosion loss and temperature rise of the processed materials. The arc-eroded surface was characterized under scanning electron microscope. The study of the eroded surfaces of contacts indicates that the thermal stability of metal oxides depends on nature of silver-metal oxide interface and their mode of erosion. An attempt is made to correlate the surface features of the eroded contacts with the thermal stability of metal oxides.

Clayey soil stabilization using alkali-activated volcanic ash and slag

Lime and Portland cement are the most widely used binders in soil stabilization projects.However,due to the high carbon emission in cement production,research on soil stabilization by the use of more environmentally-friendly binders with lower carbon footprint has attracted much attention in recent years.This research investigated the potential of using alkali-activated ground granulated blast furnace slag(GGBS)and volcanic ash(VA)as green binders in clayey soil stabilization projects,which has not been studied before.The effects of different combinations of VA with GGBS,various liquid/solid ratios,different curing conditions,and different curing periods(i.e.7 d,28 d and 90 d)were investigated.Compressive strength and durability of specimens against wet-dry and freeze-thaw cycles were then studied through the use of mechanical and microstructural tests.The results demonstrated that the coexistence of GGBS and VA in geopolymerization process was more effective due to the synergic formation of N-A-S-H and C-(A)-S-H gels.Moreover,although VA needs heat curing to become activated and develop strength,its partial replacement with GGBS made the binder suitable for application at ambient temperature and resulted in a remarkably superior resistance against wet-dry and freeze-thaw cycles.The carbon embodied of the mixtures was also evaluated,and the results confirmed the low carbon footprints of the alkali-activated mixtures.Finally,it was concluded that the alkali-activated GGBS/VA could be promisingly used in clayey soil stabilization projects instead of conventional binders.

Multi-Criteria Decision Making Based on Bipolar Picture Fuzzy Operators and New Distance Measures

This paper aims to introduce the novel concept of the bipolar picture fuzzy set(BPFS)as a hybrid structure of bipolar fuzzy set(BFS)and picture fuzzy set(PFS).BPFS is a new kind of fuzzy sets to deal with bipolarity(both positive and negative aspects)to each membership degree(belonging-ness),neutral membership(not decided),and non-membership degree(refusal).In this article,some basic properties of bipolar picture fuzzy sets(BPFSs)and their fundamental operations are introduced.The score function,accuracy function and certainty function are suggested to discuss the comparability of bipolar picture fuzzy numbers(BPFNs).Additionally,the concept of new distance measures of BPFSs is presented to discuss geometrical properties of BPFSs.In the context of BPFSs,certain aggregation operators(AOs)named as“bipolar picture fuzzy weighted geometric(BPFWG)operator,bipolar picture fuzzy ordered weighted geometric(BPFOWG)operator and bipolar picture fuzzy hybrid geometric(BPFHG)operator”are defined for information aggregation of BPFNs.Based on the proposed AOs,a new multicriteria decision-making(MCDM)approach is proposed to address uncertain real-life situations.Finally,a practical application of proposed methodology is also illustrated to discuss its feasibility and applicability.

A multiplicative Gauss-Newton minimization algorithm:Theory and application to exponential functions

Multiplicative calculus(MUC)measures the rate of change of function in terms of ratios,which makes the exponential functions significantly linear in the framework of MUC.Therefore,a generally non-linear optimization problem containing exponential functions becomes a linear problem in MUC.Taking this as motivation,this paper lays mathematical foundation of well-known classical Gauss-Newton minimization(CGNM)algorithm in the framework of MUC.This paper formulates the mathematical derivation of proposed method named as multiplicative Gauss-Newton minimization(MGNM)method along with its convergence properties.The proposed method is generalized for n number of variables,and all its theoretical concepts are authenticated by simulation results.Two case studies have been conducted incorporating multiplicatively-linear and non-linear exponential functions.From simulation results,it has been observed that proposed MGNM method converges for 12972 points,out of 19600 points considered while optimizing multiplicatively-linear exponential function,whereas CGNM and multiplicative Newton minimization methods converge for only 2111 and 9922 points,respectively.Furthermore,for a given set of initial value,the proposed MGNM converges only after 2 iterations as compared to 5 iterations taken by other methods.A similar pattern is observed for multiplicatively-non-linear exponential function.Therefore,it can be said that proposed method converges faster and for large range of initial values as compared to conventional methods.

THE GENERALIZED HYPERSTABILITY OF GENERAL LINEAR EQUATION IN QUASI-2-BANACH SPACE

In this paper,we study the hyperstability for the general linear equation f(ax+by)=Af(x)+Bf(y)in the setting of complete quasi-2-Banach spaces.We first extend the main fixed point result of Brzdek and Ciepliński(Acta Mathematica Scientia,2018,38 B(2):377-390)to quasi-2-Banach spaces by defining an equivalent quasi-2-Banach space.Then we use this result to generalize the main results on the hyperstability for the general linear equation in quasi-2-Banach spaces.Our results improve and generalize many results of literature.

Despeckling of Ultrasound Images Using Modified Local Statistics Mean Variance Filter

This article presents an improved method of despeckling the ultrasound medical images.In this paper a modified local statistics mean variance filter method has been proposed.In the proposed method,more consideration is given to local statistics since local statistical features are more important rather than global features.Various parameters like mean square error,peak signal to noise ratio,quality index,and structural similarity index measure are calculated to analyze the quality of the despeckled image.

Obtaining Crisp Priorities for Triangular and Trapezoidal Fuzzy Judgments

This paper proposes anoptimal fuzzy-based model for obtaining crisp priorities for Fuzzy-AHP comparison matrices.Crisp judgments cannot be given for real-life situations,as most of these include some level of fuzziness and com-plexity.In these situations,judgments are represented by the set of fuzzy numbers.Most of the fuzzy optimization models derive crisp priorities for judgments repre-sented with Triangular Fuzzy Numbers(TFNs)only.They do not work for other types of Triangular Shaped Fuzzy Numbers(TSFNs)and Trapezoidal Fuzzy Numbers(TrFNs).To overcome this problem,a sum of squared error(SSE)based optimization model is proposed.Unlike some other methods,the proposed model derives crisp weights from all of the above-mentioned fuzzy judgments.A fuzzy number is simulated using the Monte Carlo method.A threshold-based constraint is also applied to minimize the deviation from the initial judgments.Genetic Algorithm(GA)is used to solve the optimization model.We have also conducted casestudiesto show the proposed approach’s advantages over the existingmethods.Results show that the proposed model outperforms other models to minimize SSE and deviation from initial judgments.Thus,the proposed model can be applied in various real time scenarios as it can reduce the SSE value upto 29%compared to the existing studies.

Optimizing Bidders Selection of Multi-Round Procurement Problem in Software Project Management Using Parallel Max-Min Ant System Algorithm

This paper presents a Game-theoretic optimization via Parallel Min-Max Ant System(PMMAS)algorithm is used in practice to determine the Nash equilibrium value to resolve the confusion in choosing appropriate bidders of multi-round procurement problem in software project management.To this end,we introduce an approach that proposes:(i)A Game-theoretic model of multiround procurement problem(ii)A Nash equilibrium strategy corresponds to multi-round strategy bid(iii)An application of PSO for the determination of global Nash equilibrium.The balance point in Nash Equilibrium can help to maintain a sustainable structure not only in terms of project management but also in terms of future cooperation.As an alternative of procuring entities subjectively,a methodology to support decision making has been studied using Nash equilibrium to create a balance point on benefit in procurement where buyers and suppliers need multiple rounds of bidding.Our goal focus on the balance point in Nash Equilibrium to optimizing bidder selection in multi-round procurement which is the most beneficial for both investors and selected tenderers.Our PMMAS algorithm is implemented based on MPI(message passing interface)to find the approximate optimal solution for the question of how to choose bidders and ensure a path for a win-win relationship of all participants in the procurement process.We also evaluate the speedup ratio and parallel efficiency between our algorithm and other proposed algorithms.As the experiment results,the high feasibility and effectiveness of the PMMAS algorithm are verified.

CHFS: Complex hesitant fuzzy sets‐their applications to decision making with different and innovative distance measures

The objective of the authors is to establish an innovative concept of the complex hesitant fuzzy set(CHFS),which is the combination of the hesitant fuzzy set and the complex fuzzy set to manage complex and awkward information in the real‐decision theory.The structure and the basic properties of the proposed set are studied in detail.Based on the internal structure of the set and to find the degree of the discrimination between the pairs of the CHFSs,the generalized distance measures and modified generalized distance measures are defined.Several properties and their relationship between them are derived in detail.Also,several cases of the proposed measures are exposed which reduce them to the existing studies.Furthermore,based on these proposed measures,a decision‐making approach is established under the uncertain environment and several numerical examples are given to examine the feasibility and validity of the explored measures.Finally,the credibility of the modified and parameterized distance measures based on CHFSs is verified by comparing them with some existing measures.

Optimal Reordering Trace Files for Improving Software Testing Suitcase

An invariant can be described as an essential relationship between program variables.The invariants are very useful in software checking and verification.The tools that are used to detect invariants are invariant detectors.There are two types of invariant detectors:dynamic invariant detectors and static invariant detectors.Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm.Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical analysis.This method has some drawbacks.One of its biggest drawbacks is its overwhelming time order.It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace files.It is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this approach.Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

Cubic q-Rung Orthopair Fuzzy Linguistic Set and Their Application to Multiattribute Decision-making with Muirhead Mean Operator

The objective of this paper is to present a new concept,named cubic q-rung orthopair fuzzy linguistic set(Cq-ROFLS),to quantify the uncertainty in the information.The proposed Cq-ROFLS is a qualitative form of cubic q-rung orthopair fuzzy set,where membership degrees and nonmembership degrees are represented in terms of linguistic variables.The basic notions of Cq-ROFLS have been introduced and study their basic operations and properties.Furthermore,to aggregate the different pairs of preferences,we introduce the Cq-ROFL Muirhead mean-(MM),weighted MM-,dual MM-based operators.The major advantage of considering the MM is that it considers the interrelationship between more than two arguments at a time.On the other hand,the Cq-ROFLS has the ability to describe the qualitative information in terms of linguistic variables.Several properties and relation of the derived operators are argued.In addition,we also investigate multiattribute decision-making problems under the Cq-ROFLS environment and illustrate with a numerical example.Finally,the effectiveness and advantages of the work are established by comparing with other methods.

Preformation probability and kinematics of cluster emission yielding Pb-daughters

In the present study,the newly established preformation formula is applied for the first time to study the kinematics of the cluster emission from various radioactive nuclei,especially those that decay to the double shell closure^(208)Pb nucleus and its neighbors as daughters.The recently proposed universal cluster preformation formula has been established based on the concepts that underscore the influence of mass and charge asymmetry(η_(A)andη_(Z)),cluster mass A_(c),and the Q-value,paving the way to quantify the energy contribution during preformation as well as during the tunneling process separately.The cluster-daughter interaction potential is obtained by folding the relativistic mean-field(RMF)densities with the recently developed microscopic R3Y using the NL 3^(*)and the phenomenological M3Y NN potentials to compare their adaptability.The penetration probabilities are calculated from the WKB approximation.With the inclusion of the new preformation probability P_(0),the predicted half-lives of the R3Y and M3Y interactions are in good agreement with the experimental data.Furthermore,a careful inspection reflects slight differences in the decay half-lives,which arise from their respective barrier properties.The P_(0)for systems with double magic shell closure^(208)Pb daughter are found to be an order of≈10^(2)higher than those with neighboring Pb daughter nuclei.By exploring the contributions of the decay energy,the recoil effect of the daughter nucleus is evaluated,in contrast to several other conjectures.Thus,the centrality of the Q-value in the decay process is demonstrated and redefined within the preformed cluster-decay model.Additionally,we have introduced a simple and intuitive set of criteria that governs the estimation of recoil energy in the cluster radioactivity.

Regression analysis and features of negative activation energy for MHD nanofluid flow model:A comparative study

This article elucidates the impact of activation energy on magnetohydrodynamic(MHD)stagnation point nanofluid flow over a slippery surface in a porous regime with thermophoretic and Brownian diffusions.Negative activation energy is scarce in practice,but the impact of negative activation energy could not be neglected as it is noticed in chemical processes.The rate of some Arrhenius-compliant reactions is retarded by increasing the temperature and is therefore associated with negative activation energies,such as exothermic binding of urea or water.In some processes,the temperature dependence of the pressure-induced unfolding and the urea-induced unfolding of proteins at ambient pressure give negative activation energies.The present mathematical model is solved with successive linearization method(a spectral technique).A comparison of results is made for negative and positive values of activation energy.Apart from it,the quadratic multiple regression model is discussed briefly and explained with bar diagrams.It is observed that with rise in unsteadiness parameter from 0 to 1(taking positive activation energy),skin friction and Sherwood number are increased by 9.36%and 19%respectively,and Nusselt number is decreased by 26%.However,for negative activation energy,9.36%and 112%enhancement is observed in skin friction and Sherwood number,respectively.