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Sensitive crop leaf disease prediction based on computer vision techniques with handcrafted features
Agricultural production is considered the primary source of the economy of many countries. Tomato and Potatoes are the most sensitive and consumable vegetables worldwide. However, during the growth of these crops, they suffer from many leaf diseases, which lead to loss of productivity and economy of the farmers. Many farmers detect and find plant diseases that are more time-consuming, expensive, and require expert decisions following the naked eye method. Therefore, early and accurate diagnosis of Tomato and Potato crops leaf diseases plays a vital role in sustainable agriculture. So, this research paper proposes an efficient leaf disease classification model based on computer vision techniques. The proposed Adaptive Deep Neural Network (ADNN) leaf disease classification method is a hybrid model which combines an optimized long short-term memory (OLSTM) and convolution neural network (CNN). The weight values supplied in the LSTM classifier are optimally selected using the Adaptive Raindrop Optimization algorithm. The handcrafted features are extracted from the segmented image and fused with the hybrid deep neural network to improve the classifier performance. The ADNN method consists of five steps: preprocessing, feature extraction, segmentation, handcrafted feature extraction, and classification. At first, the images are given to the preprocessing stage to remove the noise from leaf images. Then, the image-affected portion is segmented using an enhanced radial basis function neural network. After the segmentation process, the segmented image is given as an input to the adaptive deep neural network (ADNN) that classifies various types of diseases in the Potato and Tomato leaves. The efficiency of the ADNN model based on the OLSTM-CNN approach is determined concerning multiple metrics, namely Accuracy, Precision, Recall, F-measure, Specificity, and Sensitivity. The ADNN model achieved the best Accuracy of 98.02% for Tomatoes and 98% for Potatoes. The ADNN is compared with existing state-of-the-art CNN, LSTM, ResNet50, and MobileNet techniques. The performance analysis proved that the ADNN model improved efficiency in terms of all metrics and methods. 2023, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
A colorimetric chemosensor for distinct color change with (E)-2-(1-(3-aminophenyl)ethylideneamino)benzenethiol to detect Cu2+ in real water samples
The study reports the synthesis of chemosensor (E)-2-(1-(3-aminophenyl)ethylideneamino)benzenethiol (C1), a highly sensitive, colorimetric metal probe that shows distinct selectivity for the detection of Cu2+ ion in various real water samples. Upon complexation with Cu2+ in CH3OH/H2O (60:40 v/v) (aqueous methanol), the C1 demonstrate significant enhancement in the absorption at 250nm and 300nm with a color change from light yellow to brown which was visualized using naked-eye. Therefore, these properties make C1 as an effective candidate for on-site Cu2+ ions detection. The emission spectrum of C1 illustrated TURN-ON recognition of Cu2+ with a limit of detection (LOD) of 46nM. Furthermore, Density Functional Theory (DFT) calculations were performed to better understand the interactions between C1 and Cu2+. The obtained results suggested that the electron clouds present around the NH2 innitrogen and sulfur in SH play a pivotal role in the formation of a stable complex. The computational results were in good agreement with the experimental UVvisible spectrometry results. Graphical abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to The Japan Society for Analytical Chemistry. -
Liberalisation and cashew industry: evidence from India (1965 to 2018)
We examine the impact of liberalisation on production, import, export and area under cultivation of cashew industry in India during 1965 to 2018 period using regression method. We divide data into two sub-periods. The liberalisation and pre-liberalisation period is from 1965 to 1991 and the post-liberalisation period covers the period from 1992 to 2018. We find that cashew production is not influenced post trade liberalisation. This study also finds trade liberalisation has a significant and positive impact on export. Further, we reveal an insignificant impact of liberalisation on import. This study show that the area under cultivation is not changed after the trade liberalisation. 2024 Inderscience Publishers. All rights reserved. -
Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license. -
Lattice thermal conduction in suspended molybdenum disulfide monolayers with defects
In this study, we investigated the effect of lattice defects comprising vacancies and boundaries on the lattice thermal conductivity (LTC), ? p , of suspended molybdenum disulfide monolayers (MLs) over a wide temperature range (1 < T < 500 K). Using the phonon Boltzmann formalism, the acoustic phonons were considered to be scattered by the sample and grain boundaries, isotopic impurities, vacancies, and other phonons via Umklapp and normal (N-) processes. ? p was evaluated using a modified Callaway model by considering the in-plane longitudinal acoustic and transverse acoustic phonons, and out-of-plane flexural acoustic phonon modes. We demonstrated the need to include the often neglected non-resistive N-processes when evaluating the LTC. Numerical calculations of the temperature dependence of the LTC for crystalline and polycrystalline MoS 2 MLs showed the dominance of sample-dependent scattering mechanisms at low temperatures (T < 100 K) and of phonon-phonon scattering at higher temperatures, where the N-processes played an important role. The effects of vacancies and boundaries were to alter the behavior and suppress the magnitude of the LTC. The suppression due to vacancies was greater in crystalline MLs with specular surfaces and in polycrystalline MLs with larger grain sizes. The calculations compared well with recent thermal conductivity data obtained for polycrystalline samples. The need for further investigations is suggested. 2018 Elsevier Ltd -
Effect of vacancies on thermopower of molybdenum disulfide monolayers
A detailed theoretical investigation of the effect of scattering of electrons and phonons by lattice vacancies in molybdenum disulfide (MoS2) monolayers (MLs) on diffusion, S d, and phonon-drag, S g, components of thermoelectric power (TEP), S, is presented over a wide-temperature range (1 < T < 300 K) using the Boltzmann transport formalism. The diffusion component is assumed to be influenced, not only by vacancies via short-range and Coulomb disorder scattering, but also by charged impurities (CIs) and acoustic and optical phonons. In the case of S g, the phonons are considered to be scattered, besides the vacancies, by sample boundaries, substitutional isotopic impurities, as well as other phonons via both N- and U-processes. Numerical calculations of S d and S g, as functions of temperature and vacancy defect density are presented for MoS2 MLs with n s = 1017 m-2 supported on SiO2/Si substrates. The role of carrier scatterings by mono-sulfur and mono-molybdenum vacancies in influencing the overall electron and phonon relaxation rates and in determining S d and S g are investigated. The behavior of S d and S g is found to be noticeably influenced by vacancy scattering. The influence on S d is seen to be more for mono-sulfur vacancies for densities lesser than 1%. The influence, is to enhance S d slightly for MLs with realizable CI concentrations. On the other hand, S g is found to depend sensitively on the vacancy disorder for T < 50 K; a S-vacancy density of 0.1% is found to suppress the characteristic peak of S g by almost 60%. The extent of reduction in the characteristic peak of S g, observable in low temperature measurements of S, can provide information about defect density. The calculations demonstrate that defect engineering of MoS2 ML systems can be used to tune their thermoelectric performance. A need for detailed experimental studies is suggested. 2018 IOP Publishing Ltd. -
Role of charged impurities in thermoelectric transport in molybdenum disulfide monolayers
A theoretical study of the electronic properties, namely, electrical conductivity (EC), electronic thermal conductivity (ETC) and thermoelectric power (TEP) in 2D MoS2 monolayers (MLs), over a wide range of temperatures (10 < T < 300 K), is presented employing Boltzmann transport formalism. Considering the electrons to be scattered by screened charged impurities and the acoustic, optical and remote phonons, the transport equation is solved using Ritz iterative method. Numerical calculations of EC, ETC and TEP presented for supported and free-standing MLs with high electron concentrations, as a function of temperature, bring out the relative importance of the various scattering mechanisms operative. The role of CIs, with regard to both concentration and separation from the substrate-ML interface, in determining the properties of supported MLs is demonstrated for the first time. Validity of Wiedemann-Franz law and Mott formula are examined for supported and free standing MLs. Calculations are in consonance with recent experimental data on mobility and TEP of exfoliated SiO2-supported MoS2 ML samples. In the case of TEP it is found that though the diffusion contribution is dominant the inclusion of the drag component, incorporating contributions from all relevant phonon scattering mechanisms, is needed to obtain good agreement with the data. 2017 IOP Publishing Ltd. -
Parkinson's Disease Progression Prediction Using Longitudinal Imaging Data and Grey Wolf Optimizer-Based Feature Selection
This work uses longitudinal imaging data and a feature selection method based on the Grey Wolf Optimizer (GWO) to create a novel method for forecasting the course of Parkinson's disease.Magnetic resonance imaging (MRI) and positron emission tomography (PET) longitudinal imaging data offer important insights into the structural and functional changes in the brain over time. However, because of its great dimensionality, analysing this complicated data might be difficult. We suggest using the GWO-based feature selection method to identify the most informative imaging features related to illness development in order to solve this problem.The Grey Wolf Optimizer is an algorithm that draws inspiration from nature and imitates the way that grey wolves hunt. By effectively locating an ideal subset of features that maximise classification or regression performance, it has demonstrated promising results in feature selection challenges. GWO will be used in our investigation to choose the most pertinent imaging features from the longitudinal data, lowering dimensionality and improving the model's ability to predict outcomes.Using machine learning strategies, we will build a predictive model that includes the chosen features and longitudinal imaging data. We hope to equip clinicians with a tool to forecast the course of each patient's Parkinson's disease by utilising this model. By assisting in early diagnosis, treatment planning, and disease progression monitoring, this predictive skill can ultimately improve the overall management of Parkinson's disease and the quality of life for those who are affected. Our method has great promise for expanding the fields of neurodegenerative disease prediction and personalised therapy because it integrates longitudinal imaging data and the Grey Wolf Optimizer-based feature selection method in a novel way. 2024, Ismail Saritas. All rights reserved. -
PA1 cells containing a truncated DNA polymerase ? protein are more sensitive to gamma radiation
Purpose: DNA polymerase ? (Pol?) acts in the base excision repair (BER) pathway. Mutations in DNA polymerase ? (Pol?) are associated with different cancers. A variant of Pol? with a 97 amino acid de-letion (Pol??), in heterozygous conditions with wild-type Pol?, was identified in sporadic ovarian tumor samples. This study aims to evaluate the gamma radiation sensitivity of Pol?? for possible target therapy in ovarian cancer treatment. Materials and Methods: Pol?? cDNA was cloned in a GFP vector and transfected in PA1 cells. Stable cells (PA1Pol??) were treated with60Co sourced gamma-ray (015 Gy) to investigate their radiation sensitivity. The affinity of Pol?? with DNA evaluated by DNA protein in silico docking experiments. Results: The result showed a statistically significant (p < 0.05) higher sensitivity towards radiation at different doses (015 Gy) and time-point (4872 hours) for PA1Pol?? cells in comparison with nor-mal PA1 cells. Ten Gy of gamma radiation was found to be the optimal dose. Significantly more PA-1Pol?? cells were killed at this dose than PA1 cells after 48 hours of treatment via an apoptotic pathway. The in silico docking experiments revealed that Pol?? has more substantial binding potential towards the dsDNA than wild-type Pol?, suggesting a possible failure of BER pathway that results in cell death. Conclusion: Our study showed that the PA1Pol?? cells were more susceptible than PA1 cells to gamma radiation. In the future, the potentiality of ionizing radiation to treat this type of cancer will be checked in animal models. 2022 The Korean Society for Radiation Oncology. -
Moderating influence of critical psychological states on work engagement and personal outcomes in the telecom sector
Organizations want their employees to be engaged with their work, exhibiting proactive behavior, initiative, and responsibility for personal development. Existing literature has a dearth of studies that evaluate all the three key variables that lead to optimal employee performancecritical psychological states (CPSs), work engagement, and personal outcomes. The present study attempts to fill that gap by linking the variable CPSs (which measures experienced meaningfulness, responsibility, and knowledge of results) with the other two. The study surveyed 359 sales personnel in the Indian telecom industry and adopted standardized, valid, and reliable instruments to measure their work engagement, CPSs, and personal outcomes. Analysis was done using structural equation modeling (SEM). Findings indicated that CPSs significantly moderate the relationship between personal outcomes and work engagement. The Author(s) 2014. -
Managing workplace diversity: Issues and challenges
Diversity management is a process intended to create and maintain a positive work environment where the similarities and differences of individuals are valued. The literature on diversity management has mostly emphasized on organization culture; its impact on diversity openness; human resource management practices; institutional environments and organizational contexts to diversity-related pressures, expectations, requirements, and incentives; perceived practices and organizational outcomes related to managing employee diversity; and several other issues. The current study examines the potential barriers to workplace diversity and suggests strategies to enhance workplace diversity and inclusiveness. It is based on a survey of 300 IT employees. The study concludes that successfully managing diversity can lead to more committed, better satisfied, better performing employees and potentially better financial performance for an organization. The Author(s) 2012. -
Intention to Stay as a Moderator on Employee Job Satisfaction and Organizational Citizenship Behavior
International Journal of Management Studies, Statistics & Applied Economics, Vol-2 (2), pp. 65-74. ISSN-2250-0367 -
Commitment of Information Technology Employees in Relation to Perceived Organizational Justice
The IUP Journal of Organizational Behaviour Vol. XI, No. 3. pp 23-40, ISSN No. 0972-687X -
Socialization tactics and new entrants adjustments in the information technology context /
PES Business Review, Vol. 8, Issue 1, pp.19-28 ISSN No. 0973-919X -
Expression of dissatisfaction in relation to managerial leadership strategies and its impact in Iinformation technology organizations /
Skyline Business Journal, Vol.8, Issue 1, pp.29-35, ISSN: 1998-3425. -
Brain Tumor Classification Using an Ensemble of Deep Learning Techniques
The article reflects on the classification of brain tumors where several deep learning (DL) approaches are used. Both primary and secondary brain tumors reduce the patient's quality of life, and therefore, any sign of the tumor should be treated immediately for adequate response and survival rates. DL, especially in the diagnosis of brain tumors using MRI and CT scans, has applied its abilities to identify excellent patterns. The proposed ensemble framework begins with the image preprocessing of the brain MRI to enhance the quality of images. These images are then utilized to train seven DL models and all of these models recognize the features related to the tumor. There are four models which are General, Glioma, Meningioma, and Pituitary tumors or No Tumor model, which helps in reaching a joint profitable prediction and concentrating solely on the strength of the estimation and outcome. This is a significant improvement over all the individual models, attaining a 99. 43% accuracy. The data used in this research was gotten from Kaggle website and comprised of 7023 images belonging to four classes. Future work will focus on increasing the dataset size, investigating additional DL architectures, and enhancing real-Time detection to improve the accuracy of diagnostic scans and their overall relevance to clinical practice. 2013 IEEE. -
Drivers of Rural Non-farm Sector Employment in India, 19832019
Using the national-level employment and unemployment surveys (NSS and PLFS) and the macro-level data for the period 20052019, this article explores the trends and recent growth patterns of rural non-farm sector employment in India. It also examines the micro-level factors determining individuals preference towards non-farm sector jobs and the macro-level factors responsible for the growth of non-farm sector employment in rural India. The main findings of the study suggest that although rural non-farm sector employment is rising in absolute terms, its growth rate has slackened in recent years. While the level of education and skill training, market wage rates and socio-cultural setups are among the key micro-level factors determining farmnon-farm employment choices of rural folks, at the macro-level, the growth of investment in capital goods, the number of factories, investment in infrastructure development and the growth of the manufacturing sector are crucial for the growth of non-farm sector jobs in India. Based on these findings, it is argued that the improvement of human capabilities through increased investment in education and skill, and the growth of non-farm sector employment through the development of rural infrastructure and industrialization measures, are necessary to sustain the structural transformation and to harness the demographic dividend in India. JEL Codes: J01, J21, J43, J64 2024 Research and Information System for Developing Countries & Institute of Policy Studies of Sri Lanka. -
Feminization of hunger in climate change: linking rural womens health and wellbeing in India
The links between climate change, food security and womens wellbeing remain an under-investigated area. This paper contributes to this area through a thorough examination of how women experience food insecurity in farming households in rural India. The households are located in four agro-climatic regions in India. These regions experience varied climatic pressures, and this diversity allows us to explore a wider variety of womens experiences in their attempts to maintain household food security as the climate changes. The study finds that women, even in comparatively more food-secure households, suffer from food insecurity. One of the reasons for this is that womens food habits and mealtimes have altered in recent years due to the increase in their work pressures. The worst effects are to be found in drought-prone areas, and there are greater vulnerabilities among women-headed households, indicating that the impacts of climate change are exacerbated by cultural norms that further hinder the role of women in farm activities. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Heat transport of nano-micropolar fluid with an exponential heat source on a convectively heated elongated plate using numerical computation
Purpose: The study of novel exponential heat source phenomena across a flowing fluid with a suspension of microparticles and nanoparticles towards a convectively heated plate has been an open question. Therefore, the impact of the exponential heat source in the transport of nano micropolar fluid in the existence of magnetic dipole, Joule heating, viscous heating and convective condition effects has been analytically investigated. Influence of chemical reaction has also been exhibited in this discussion. Design/methodology/approach: The leading equations are constructed via conservation equations of transport, micro-rotation, energy and solute under the non-transient state situation. Suitable stretching transformations are used to transform the system of partial differential equations to ordinary. The transformed ODEs admit numerical solution via RungeKutta fourth order method along with shooting technique. Findings: The effects of pertinent physical parameters characterizing the flow phenomena are presented through graphs and discussed. The inclusion of microparticles and nanoparticles greatly affects the flow phenomena. The impact of the exponential heat source (EHS) advances the heat transfer characteristics significantly compared to usual thermal-based heat source (THS). The thermal performance can be improved through the effects of a magnetic dipole, viscous heating, Joule heating and convective condition. Originality/value: The effectiveness of EHS phenomena in the dynamics of nano micropolar fluid past an elongated plate which is convectively heated with regression analysis is for the first time investigated. 2019, Emerald Publishing Limited. -
Influence of HRM practices on organizational commitment: A study among software professionals in India
Although organizational commitment has been discussed frequently in organizational psychology for almost four decades, few studies have involved software professionals. A study in India reveals that HRM practices such as employee-friendly work environment, career development, developmentoriented appraisal, and comprehensive training show a significant positive relationship with organizational commitment. The study's results emphasize the role of such HRD variables as inculcating and enhancing organizational commitment, and suggest that HRD practitioners and researchers should further develop commitment-oriented organization policies. Copyright 2004 Wiley Periodicals, Inc.