Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total consumption.An increase of 3.3%in consumption is predicted from 2024 to 2032.Tomatoes are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose levels.Thus,tomatoes are globally important due to their widespread usage and nutritional value.To face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize them.Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit.This leads to financial losses and affects the livelihood of farmers.Therefore,automatic disease detection at any stage of the tomato plant is a critical issue.Deep learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters.Also,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region.Moreover,the existing techniques lack in recognizing multiple diseases on the same leaf.To address these concerns,we introduce a customized deep learning-based convolution vision transformer model.Themodel achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories.It requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,respectively.Its training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned models.Thus,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato crops.The periodicmonitoring promotes timely action to prevent the spread of diseases and reduce crop loss.

Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

Tuning of Optical Properties via Annealing of Bismuth Ferrite (BiFeO3) Thin Films

In this study we are reporting annealing induced optical properties of bismuth ferrite (BiFeO3) thin films deposited on glass substrate via spin coating at 5000 rpm. The structural, optical and surface morphology of BiFeO3 (BFO) thin films have been studied via X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Optical absorption (UV-Vis) and Photoluminescence (PL) spectroscopy. XRD spectra confirm annealing induced phase formation of BiFeO3 possessing a rhombohedral R3c structure. The films are dense and without cracks, although the presence of porosity in BFO/glass was observed. Moreover, optical absorption spectra indicate annealing induced effect on the energy band structure in comparison to pristine BiFeO3. It is observed that annealing effect shows an intense shift in the UV-Vis spectra as diffuse absorption together with the variation in the optical band gap. The evaluated optical band gap values are approximately equal to the bulk band gap value of BiFeO3.

基于元启发式算法的船舶动力装置随机建模与性能优化

For the successful operation of any industry or plant continuous availability of power supply is essential.Many of the large-scale plants established their power generation units.Marine power plant having two generators is also fall in this category.In this study,an effort is made to derive and optimize the availability of a marine power plant having two generators,one switch board and distribution switchboards.For this purpose,a mathematical model is proposed using Markov birth death process by considering exponentially distributed failure and repair rates of all the subsystems.The availability expression of marine power plant is derived.Metaheuristic algorithms namely dragonfly algorithm(DA),bat algorithm(BA)and whale optimization(WOA)are employed to optimize the availability of marine power plant.It is revealed that whale optimization algorithm outperforms over dragonfly algorithm(DA),and bat algorithm(BA)in optimum availability prediction and parameter estimation.The numerical values of the availability and estimated parameters are appended as numerical results.The derived results can be utilized in development of maintenance strategies of marine power plants and to carry out design modifications.

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

A Decisive Study on Dielectric Response of Bi2O3/Polystyrene &Bi2O3/PVDF Composite as Flexible Electrodes for Energy Storage

In this manuscript a comparative study on Bi2O3/polystyrene and Bi2O3/PVDF composites has been executed via analysis of structural, bonding, surface morphology and dielectric response of composites for energy storage. The composites have been synthesized using solution cast method by varying concentrations of Bi2O3 (BO = 1 - 5 mw%) into polystyrene (PS) and polyvinylidene fluoride (PVDF) polymers respectively. X-ray diffraction confirms the generation of crystallinity, Fourier transform infrared (FT-IR) spectroscopy confirms bonding behavior and scanning electron microscopy (SEM) confirms uniform distribution of Bi2O3 (BO) in PS and PVDF polymers. Impedance spectroscopy has been employed for determination of dielectric response of the fabricated composites. The dielectric constant has been found to be increased as 1.4 times of pristine PS to BO5%PS95% composites and 1.8 times of pristine PVDF to BO5%PVDF95% composites respectively. These high dielectric composite electrodes are useful for flexible energy storage devices.

Analysis and formulation of spur gear stresses with different tip modifications

Spur gears are widely used in the power transmission mechanism of several machines.Due to the transmitted torque,spur gears experience high stresses which could cause gear tooth failure by surface pitting or root fracture.Tip relief and other gear profile modification have been considered for reducing the induced stresses in the gear tooth.In this work,the influence of tip relief on stresses on a pair of identical spur gear was analyzed using commercial FEA software ANSYS,and formulae for estimating contact and bending stresses were derived.Three cases of gear sets were analyzed;a non-modified pair and another two sets with linear and parabolic tip relief profiles.The non-modified gear set frictionless contact stress was validated against the calculated AGMA pitting resistance,Hertzian contact stress and a reported contact stress value in the literature.The four methods agreed well with each other.Similarly,bending stress was also compared with the AGMA bending strength and Lewis bending stress for validation.Then,friction coefficient was varied from 0.0 to 0.3 with increment of 0.1.The gear contact stress increased up to 11%relative to the frictionless case,whereas bending stress decreased by 6%.Linear tip relief modification was carried out for increasing normalised tip relief values of 0.25 to 1.0 with increment of 0.25.The gear frictionless contact and bending stresses decreased by a maximum of 4%and 2%,respectively.Frictional contact stress increased by up to 7.1%and the bending stress is almost identical with the frictionless case.Parabolic tip relief was also carried out with similar normalised tip relief values.Frictionless contact stress decreased by 5%while frictional contact stress increased by up to 11.5%and the bending stress is also almost identical with the frictionless case.Finally,four formulae were introduced for estimating the contact and bending stresses for a tip modified spur gear.

Hybrid Machine Learning Model for Face Recognition Using SVM

Face recognition systems have enhanced human-computer interactions in the last ten years.However,the literature reveals that current techniques used for identifying or verifying faces are not immune to limitations.Principal Component Analysis-Support Vector Machine(PCA-SVM)and Principal Component Analysis-Artificial Neural Network(PCA-ANN)are among the relatively recent and powerful face analysis techniques.Compared to PCA-ANN,PCA-SVM has demonstrated generalization capabilities in many tasks,including the ability to recognize objects with small or large data samples.Apart from requiring a minimal number of parameters in face detection,PCA-SVM minimizes generalization errors and avoids overfitting problems better than PCA-ANN.PCA-SVM,however,is ineffective and inefficient in detecting human faces in cases in which there is poor lighting,long hair,or items covering the subject’s face.This study proposes a novel PCASVM-based model to overcome the recognition problem of PCA-ANN and enhance face detection.The experimental results indicate that the proposed model provides a better face recognition outcome than PCA-SVM.

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.

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Robotic manipulators are widely used in applications that require fast and precise motion.Such devices,however,are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part.To address these issues,the Linear Matrix Inequalities(LMIs)and Parallel Distributed Compensation(PDC)approaches are implemented in the Takagy–Sugeno Fuzzy Model(T-SFM).We propose the following methodology;initially,the state space equations of the nonlinear manipulator model are derived.Next,a Takagy–Sugeno Fuzzy Model(T-SFM)technique is used for linearizing the state space equations of the nonlinear manipulator.The T-SFM controller is developed using the Parallel Distributed Compensation(PDC)method.The prime concept of the designed controller is to compensate for all the fuzzy rules.Furthermore,the Linear Matrix Inequalities(LMIs)are applied to generate adequate cases to ensure stability and control.Convex programming methods are applied to solve the developed LMIs problems.Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.

Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

In the next generation of computing environment e-health care services depend on cloud services.The Cloud computing environment provides a real-time computing environment for e-health care applications.But these services generate a huge number of computational tasks,real-time computing and comes with a deadline,so conventional cloud optimizationmodels cannot fulfil the task in the least time and within the deadline.To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time.In order to overcome existing issues,an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications.In this work,two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier.The predictive model enhances the quality of service using performance metrics,makespan,least average task completion time,resource usages cost and utilization of the system.Fromresults as compared to existing algorithms the proposedANN-WHOalgorithms prove to improve the average start time by 29.3%,average finish time by 29.5%and utilization by 11%.

Status,structure and environmental variations in semi-arid mangroves of India

The semi-arid mangroves of the Gulf of Kachchh,the largest ecosystems on the west coast of India,are poorly studied in terms of vegetation structure and environmental parameters in spite of their conservation significance.Therefore,it is necessary to document the structural features of these mangroves in view of ongoing coastal industrial development.Mangrove forest structure in 10 locations on the northern and southern coasts of the Gulf of Kachchh were assessed using the line intercept transect method.Descriptions included density of young and mature age classes,tree heights,diameters at breast height(DBH)and aboveground biomass,along with seven significant environmental variables.Mature tree densities ranged from 350 to 1567 ind.ha-1,while average height and girth at breast height ranged from 1.0 to 6.8 m and 3.0 to 137.0 cm,respectively.The majority of trees(55.6%)were in B 1.8 m height class followed by a 1.9 to 2.4 m class(25.1%).DBH was most often in class 2 cm or lower than that.Among the canopy index classes,more trees were recorded in class ≤2 cm.The regeneration density was greater than the recruitment class.This study indicates that the poor structural attributes of Avicennia marina Vierth.var.acutissima Stapf and Mold dominated mangroves are largely due to aridity induced by scarce and erratic rainfall and high soil and water salinities.The results should be valuable in conserving and sustainably managing these mangroves in the face of developmental activities.

DWT-SVD Based Image Steganography Using Threshold Value Encryption Method

Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual system.This paper presents an image scrambling method that is very useful for grayscale secret images.In this method,the secret image decomposes in three parts based on the pixel’s threshold value.The division of the color image into three parts is very easy based on the color channel but in the grayscale image,it is difficult to implement.The proposed image scrambling method is implemented in image steganography using discrete wavelet transform(DWT),singular value decomposition(SVD),and sorting function.There is no visual difference between the stego image and the cover image.The extracted secret image is also similar to the original secret image.The proposed algorithm outcome is compared with the existed image steganography techniques.The comparative results show the strength of the proposed technique.

Image Segmentation Based on Block Level and Hybrid Directional Local Extrema

In the recent decade,the digitalization of various tasks has added great flexibility to human lifestyle and has changed daily routine activities of communities.Image segmentation is a key step in digitalization.Segmentation plays a key role in almost all areas of image processing,and various approaches have been proposed for image segmentation.In this paper,a novel approach is proposed for image segmentation using a nonuniform adaptive strategy.Region-based image segmentation along with a directional binary pattern generated a better segmented image.An adaptive mask of 8×8 was circulated over the pixels whose bit value was 1 in the generated directional binary pattern.Segmentation was performed in three phases:first,an image was divided into sub-images or image chunks;next,the image patches were taken as input,and an adaptive threshold was generated;and finally the image chunks were processed separately by convolving the adaptive mask on the image chunks.Gradient and Laplacian of Gaussian algorithms along with directional extrema patterns provided a double check for boundary pixels.The proposed approach was tested on chunks of varying sizes,and after multiple iterations,it was found that a block size of 8×8 performs better than other chunks or block sizes.The accuracy of the segmentation technique was measured in terms of the count of ill regions,which were extracted after the segmentation process.

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.

Average L-Shell Fluorescence Yields of Some Rare Earth Elements

Average L-shell fluorescence yields of some rare earth elements were determined using HPGe detector employing reflection geometry set up. Target atoms were excited using 59.5 keV gamma rays emerging from Am-241 source of strength 300 mCi. Background radiation and multiple scattering effects were minimized by properly shielding the detector. The elemental foils of uniform thickness and 99.9% purity were used in the present investigation. The fluorescent spectra were recorded in a 16 K multichannel - analyzer. The data were carefully analyzed and average L-shell fluorescence yields were calculated. The resulting yield values are compared with the available experimental and theoretical values.

人类未来的传统“希望”（英文）

在新时代文化的背景下，跨文化交际已然成为人类沟通交流不可或缺的一部分。本文认为，各国的文化在跨文化交际中起到了重要的作用，扮演了双重身份：一是对于各国经验的普及有所助益，二是去帮助解决不同文化在相互沟通与指正时所存在的偏差与误解。通过对文化创新的分析，本文指出，每一种传统文化需要的不仅仅是内部的创新与转型，外部的借鉴与再生也是必不可少的。除此之外，本文还将从研究“希望”在传统文化中的价值作用入手，分别从现代西方特有的科技的发展的观念与根深蒂固存在于印度泰米尔入灵魂中传统的运气的观念两方面出发，从而为各国间未来的文化发展往来做出贡献。

Spatial Transformations and Urban Conservation of Religious-Historic Towns: A Case of Vrindavan, India

The spatial transformations can be observed at different religious-historic towns of India due to urbanization. Research is based upon fact that there is substantial change in the built environment because of spatial transformations at the religious-historic towns. The process of modernization in the functions and spatial layout is unavoidable at any historic town. The study attempts to focus on various urban historic conservation components, including the look of historic buildings, their earlier uses, and its immediate surroundings to improve the built environment of historic towns. A theoretical framework for the urban conservation of ancient towns is the main objective of study. How to modernize the historic conservation function while preserving the space’s texture and integrity. The research started with the investigation of the morphological growth of Mathura district, India through satellite images and in-depth study of the evolution process of street network in Vrindavan town, which is one of the main temple towns of Mathura district. There is a significant difference in the layout & architectural character of old part and the newly developed Vrindavan. Due to increased accessibility and movement, the spatial structure of traditional religious precincts, which were once local integration centres, has significantly changed. Increasing & changing mode of transportation and further increase in the religious tourism might be the cause or a big reason for the spatial transformations and correspondingly there is a challenge to conserve & preserve the religious precincts of historic towns. The study tries to analyze spatial transformations with the help of Historical GIS at different scales of urban form. Suggestive measures to conserve the environmental ambience of religious-historic towns are the outcome of the research.

Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging effects.That is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical industry.In this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd.The controller is used to control Mobile Robot System(MRS)at the required set point.The FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system response.The FF is touted as providing an easy,reliable,and efficient tuning technique for PID controllers.The most suitable ideal performance is accomplished with FF-PID,according to the display in the time response.Further,the observed response is compared to those received by applying GA and conventional off-line tuning techniques.The comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.