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Soil classification using active contour model for efficient texture feature extraction
Precision farming is a systematic approach in agriculture that aims in improving economic and environment status of the farmers. It is achieved by having prior knowledge on soil texture, nutrient, pH and other climatic conditions. Hence this paper proposes a soil classification for crop prediction approach that uses an active contour algorithm for band estimation in Fourier domain for efficient texture feature extraction. This approach initially segments the soil sample and extracts into the color and texture features. The approach proposes a texture feature extraction where the image is initially transformed to Fourier domain of a 2D-discrete Fourier transform. The image in the Fourier domain is classified into high and low-frequency bands. The cut off frequency is decided by final contour of active contour method, where initial circular contour is used for estimating final contours on Fourier coefficients. This leads to the estimation of an irregular-shaped cut off frequency along with the 2D Fourier coefficients, instead of using a circular-shaped cut off frequency. A local binary pattern (LBP) from the high-frequency band image extracts texture feature. The extracted texture and color features are trained using a fully connected network. Active contour-based proposed model was evaluated by metrics F1-score, accuracy, specificity, sensitivity, and precision on soil datasets of Kaggle and IRSID. The accuracy, F1-score, specificity, precision, and sensitivity of proposed approach active contour-based were estimated as 97.89%, 97.87%, 99.46%, 98.11 and 97.94% respectively when evaluated in the Kaggle dataset. The evaluation results of proposed active contour model based soil classification outperform other traditional approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP
Security of the software system is a prime focus area for software development teams. This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. Various approaches in this context are explored using data of insurance domain-based software development. These approaches will facilitate an easy understanding of the practical challenges associated with actual-world implementation. This paper also discusses the capabilities of language modeling and its role in the knowledge system. The source code is modeled to build a deep software security analysis model. The proposed model can help software engineers build secure software by assessing the software security during software development time. Extensive experiments show that the proposed models can efficiently explore the software language modeling capabilities to classify software systems' security vulnerabilities. 2021 Raghavendra Rao Althar et al. -
Software Quality Prediction by CatBoost: Feed-Forward Neural Network in Software Engineering
Software quality is the key aspect of every software organization. Multiple frameworks and algorithms are essential to ensure quality. However, multiple software failures occur uninvited. There are multiple aspects that skew a softwares efficiency. Now the software quality analysis framework mostly focuses on design flaws and test plans done during development. To overcome this problem of software failure, this research proposes a prediction for software efficiency analysis in software engineering using enhanced feed-forward neural network machine learning classification with CatBoost. This research also evaluates the parameters of efficiency of each software component before implementation. This proposed work also analyses the basic aspects that need to be ensured before the design phase of any software. 2024 Taylor & Francis Group, LLC. -
Software development with UML modelling and software testing techniques
This chapter focuses on software development principles and discusses each principle thoroughly with diagrammatic representation. It also includes the definition of UML (unified modeling language) modelling with an explanation regarding how UML modelling takes place and a detailed example. It also focuses on software testing methods, with each method definition and diagrams well explained. A simple case study situation is taken to discuss the example of UML model. This chapter's main objective is to focus on all key points of software development testing and model design techniques precisely. 2023, IGI Global. All rights reserved. -
Software bug in identification and prediction through software are metrics in object oriented protects :
In the software engineering, quality assurance plays an important role. newlineThe quality assurance as an activity, observes the execution of software project to ensure that the behavior of product is in accordance with the expectations. The testing is associated with quality assurance activities. The testing takes a lot of time and an effort of the tester to test the test newlinecases. Even after enough manual or automatic testing, bugs remain uncovered because of lack of time. So, a need arises to focus on this area to save the time and cost of the organizations. The software developer or newlinetester should be aware about the main reasons of software bugs so that they can focus on the right part of the code at the right time. Need of introducing product, process and project metrics is also very essential for newlinethe identification of major causes of bugs. Predictions will always be best if the history of project is taken into consideration. We can come up with accurate predictors with the help of root causes of the software bugs. Several bug prediction models can use bug indicators as the input of model to predict the number of bugs. newlinePrediction attempts to provide quantitative measures to help the software testers and developers. With more number of bug indicators, a step can be taken towards wider horizon of bug prediction thus enabling higher devotion to improve quality of software products. Therefore, identification of several reasons of software bugs and implementation of effective bug prediction models are needed to widen the scope of bug newlineprediction approaches and to improve the software quality. After estimating the future bugs using prediction models, awareness of bug severity is also required to avoid the expected harms to software products. newlineIntroduction of Artificial Neural Network (ANN) was needed to improve the prediction potential. In this work an attempt has been made to associate different levels and types of inheritance through neural network newlineby establishing a correlation framework with diverse types of bug severitie. -
Soft grafting of DNA over hexagonal copper sulfide for low-power memristor switching
Green electronics, where functional organic/bio-materials that are biocompatible and easily disposable are implemented in electronic devices, have gained profound interest. DNA is the best biomolecule in existence that shows data storage capacity, in virtue of the sequential arrangement of AT and GC base pairs, analogous to the coding of binary numbers in computers. In the present work, a robust, uniform and repeatable room-temperature resistive switching in a Cu/Cu2S/DNA/Au heterojunction is demonstrated. The DNA nanostructures were anchored on the densely packed hexagonal Cu2S structures by simple electrochemical deposition. This heterostructure presents outstanding memristor behavior; the device exhibits resistive switching at a very low threshold voltage of 0.2 V and has a relatively high ON/OFF ratio of more than 102 with a good cycling stability of ?1000 cycles and a negligible amount of variation. The justification for such a switching mechanism is also given on the basis of the energy-band diagram of the Cu2S-DNA interface. Based on the studies herein, the resistive switching is attributed to the reversible doping of DNA by Cu+ ions, leading to intrinsic trap states. Further, the switching is modeled with the help of different transport mechanisms, like Schottky-barrier emission, Poole-Frenkel emission and Fowler-Nordheim tunneling. 2023 The Author(s). -
Soft excess in AGN with relativistic X-ray reflection
The soft X-ray excess, emission below (Formula presented.) 2keV over the X-ray power-law, is a marked spectral component in the X-ray spectra of many Seyfert1 galaxies. We investigate if the observed soft X-ray excess in a sample of Seyfert1s is in accordance with the prediction of the relativistic reflection model by analyzing the XMM-Newton and NuSTAR spectra. The fractional difference in the soft excess (SE) obtained from the blurred reflection emission predicted (from NuSTAR) and the observed (from XMM-Newton) luminosities show that the reflection model underestimates the SE emission in our sample. The results point to alternative models (for example, warm Comptonization) to explain the soft X-ray excess in AGN. 2023 Wiley-VCH GmbH. -
Soft computing techniques for hub sequence analysis /
Bioinformatics, the combination of Biology and Information Technology has become a pioneer industry booming worldwide. One of the grand challenges in biology is to understand organizing principles of bimolecular networks. There seems to be a deliberate effort towards uncovering new laws of biological complexity. One of the most pressing needs in this area is the understanding of protein-protein interaction networks and their complexity. Hub proteins- network elements with high connectivity- literally ??hold the networks together. Though several experimental methods have been developed to identify hub proteins, it is important to supplement procedures for pattern recognition to classify/predict hub protein sequences. This research work aims at the classification and prediction of hub proteins of two model organisms- Homo sapiens and Escherichia coli using different computational approaches of pattern recognition such as Principal Component Analysis (PCA), Artificial Neural Network (ANN) and Linear Discriminant Analysis using (i) Class Dependent Approach (LDACD), (ii) Class Independent Approach (LDACIND), and (iii) Normal Bayes Classification (LDANB). -
Soft Computing Approaches for Maximum Power Point Tracking of Solar PV System
Solar power changes according to irradiance and temperature in a day. A Maximum Power Point Tracking (MPPT) algorithm is actually necessary to obtain the maximum power from the photovoltaic (PV) arrangement. In this paper, in order to optimize power and improve the efficiency of PV module with regulated output voltage, soft computing MPPT techniques, flying squirrel search optimization and artificial bee colony methods are implemented on cascaded double voltage lift boost converter. The PV module is subjected to both with and without constraints to analyze the performance of the DC/DC converter, and the comparative outcomes are evaluated for resistive and different types of battery loads at various temperature conditions in MATLAB/Simulink platform. The optimized power is achieved by using artificial bee colony technique with less ripple in the output waveforms at constant 25 C temperature irrespective of the changes in irradiation with the battery load and this can be used for charging of the battery system. 2023 Praise Worthy Prize S.r.l.-All rights reserved. -
Soft Computing Approach for Student Dropouts in Education System
The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sodium alginate/bismuth (III) oxide composites for ?-ray shielding applications
In the present work, we have explored the efficacy of bismuth (III) oxide (Bi2O3) loaded, calcium ion cross-linked solution cast sodium alginate composite films for radioprotective applications. Calcium ion cross-linking increased the water and chemical resistance, which further improved on introduction of Bi2O3 into the composites. The 40 wt% Bi2O3 loaded films showed good heat resistance with the peak degradation temperature reaching as high as 251C. The Bi2O3 loaded composites showed enhanced tensile strength (TS) and Youngs modulus (YM). Compared to high-modulus polymers like epoxy, high-density polyethylene (HDPE) and poly (vinyl chloride) (PVC), these exhibit relatively greater extent of stretching before breaking. The ?-ray attenuation experiments showed that mass attenuation coefficients of the composites at various ?-ray energies increased with filler loading. These composites are effective in shielding ?-rays from radioactive sources like 137Cs, 22Na, 133Ba, and 60Co that are widely employed in several medical and industrial applications. The overall enhancement in thermal, mechanical, and radiation shielding characteristics of the composites may be attributed to the uniform distribution of the fillers in alginate matrix. These nontoxic sodium alginate/Bi2O3 composites can be used as soft and biodegradable radiation shields, which may be processed to wearable forms. 2020 Wiley Periodicals LLC. -
Socioeconomic determinants of COVID-19 in Asian countries: An empirical analysis
The spread of coronavirus disease, 2019, has affected several countries in the world including Asian countries. The occurrences of COVID infections are uneven across countries and the same is determined by socioeconomic situations prevailing in the countries besides the preparedness and management. The paper is an attempt to empirically examine the socioeconomic determinants of the occurrence of COVID in Asian countries considering the data as of June 18, 2020, for 42 Asian countries. A multiple regression analysis in a cross-sectional framework is specified and ordinary least square (OLS) technique with heteroscedasticity corrected robust standard error is employed to obtain regression coefficients. Explanatory variables that are highly collinear have been dropped from the analysis. The findings of the study show a positive significant association of per capita gross national income and net migration with the incidence of total COVID-19 cases and daily new cases. The size of net migration emerged to be a potential factor and positive in determining the total and new cases of COVID. Social capital as measured by voters' turnout ratio (VTR) in order to indicate the people's participation is found to be significant and negative for daily new cases per million population. People's participation has played a very important role in checking the incidence of COVID cases and its spread. In alternate models, countries having high incidence of poverty are also having higher cases of COVID. Though the countries having higher percentage of aged populations are more prone to be affected by the spread of virus, but the sign of the coefficient of this variable for Asian country is not in the expected line. Previous year health expenditure and diabetic prevalence rate are not significant in the analysis. Therefore, people-centric plan and making people more participatory and responsive in adhering to the social distancing norms in public and workplace and adopting preventive measures need to be focused on COVID management strategies. The countries having larger net migration and poverty ratio need to evolve comprehensive and inclusive strategies for testing, tracing, and massive awareness for sanitary practices, social distancing, and following government regulation for management of COVID-19, besides appropriate food security measures and free provision of sanitary kits for vulnerable section. 2020 John Wiley & Sons Ltd -
Sociocultural aspects of the medicalisation of infertility: a comparative reading of two illness narratives
This paper is a comparative reading of variations in the medicalisation of infertility caused by sociocultural aspects, in two illness narratives by patients: Elizabeth Katkins Conceivability (2018), a story of navigating a fertility industry with polycystic ovarian syndrome and antiphospholipid syndrome in America and Rohini Rajagopals Whats a Lemon Squeezer Doing in My Vagina (2021), a discussion from India of a growing awareness of medicalisation in treatment of unexplained infertility. For this purpose, it first charts scholarship on illness narratives and medicalisation, noting a historical association. Following this, it shows how infertility, a physiological symptom of reproductive incapacity or failure to show clinical pregnancy, is generally medicalised. This paper reads the texts as showing hitherto unaddressed sociocultural aspects of infertilitys medicalisation. At the same time, drawing from existing sociological and anthropological scholarship, it shows how a reading of sociocultural aspects in medicalised infertility nuances understanding of its medicalisation. This comparative reading attends to sociocultural values and norms within the texts, including pronatalism, fetal personhood, kinship organisation, purity/pollution, individual reliance, sacred duty and so forth. It draws from scholarship on embodiment, rhetorical strategies and the language of medicine. It also shows how a patients non-medicalised, affective history ofdeep sickness caused by the biographical disruption of infertility is not that of apoor historian. In laying out the particularisation of such sociocultural values and norms across America and India, medicalisations migration from its origins to the margins reveals subjectivised, stratified reproduction in infertility illness narratives. This paper is part of a turn in scholarship away from understanding the medicalisation of infertility as naturalised and decontextualised. Author(s) (or their employer(s)) 2024. -
Socio-economic development of Darjeeling Himalayas: Categorical principal component analysis (CATPCA) and ordinal logistic regression (OLR)
The measurement of regional development plays a crucial role in improving the quality of life of local communities. However, the process of analyzing the regional progress was challenging as regional development was presented as a multidimensional concept. Nonetheless, the study's primary objective was to understand the indicators that genuinely reflect the development process's various dimensions in the northernmost district of West Bengal, Darjeeling Himalayas. Seven dimensions of development, namely psychological well-being, health, education, governance, safety and crime, energy and environment and standard of living were identified for analyzing the socio-economic development of the Darjeeling Himalaya. A questionnaire was framed and circulated in the region for the collection of data. By applying Categorical Principal Component Analysis (CATPCA), the data collected was aggregated into the above mentioned seven dimensions of development and analyzed the relationship between these development indicators through the Ordinal Logistic Regression model (OLR). The results showed that education and governance indicators had a significant impact on psychological wellbeing. Governance was affected by psychological wellbeing, while the standard of living was affected by psychological wellbeing and health indicators in the region. 2021 The Society of Economics and Development, except certain content provided by third parties. -
Socio-economic Development and Value Creation Through Corporate Social Responsibility: A Case Study of Bosch India Foundation
In recent years, it is mandatory for profitable organizations in India to work toward Corporate Social Responsibility (CSR). Many thinkers in the industry have appreciated the move of the Indian Government by mandating profitable businesses to take responsibility for society by sharing certain portions of the profit made by these organizations. This study focuses on various initiatives taken by BOSCH India Foundation (BIF) for socio-economic development and value creation through its CSR activities. The primary data are collected by conducting interviews with the seniorlevel managers working in the CSR department of the Bidadi plant. The data are also collected by visiting the field of action, discussing with various stakeholders and observing their initiatives. The secondary data are collected from published sources and official records of the company. This case study shows that BOSCH India Foundation is focusing on the development of the villages in Bidadi. Their CSR initiatives focus on education, agriculture and livestock development, health and hygiene, environment, women empowerment, youth development and access to potable water. This study analyzes the economic and social impacts it has created in the society. The case provides new insight for researchers and students about the CSR approaches and best practices which can be a model for companies working on CSR projects. 2024 by World Scientific Publishing Co. Pte. Ltd. -
Socially responsible universities and student satisfaction: Case analysis
Universities play the dual role of providing new knowledge and inculcating a sense of social responsibility in student citizens to contribute to community development. Higher Education Institutions (HEIs) are often expected to be socially responsible. The principal focus of this chapter is to determine the dimensions of University Social Responsibility (USR) and examine its impact on student satisfaction. A case study research was conducted with 299 students from a private university in India. Exploratory and Confirmatory Factor Analysis were used to identify the dimensions of USR. A structural equation model was used to analyze the impact of USR on student satisfaction, with gender and volunteerism in USR activities as moderators. The results show that student satisfaction is influenced by their perception of USR activities undertaken by the university. Findings indicate that the degree of influence of USR on satisfaction is more among female than male students. Contrastingly, the degree of influence of USR on satisfaction remained the same for volunteers and non-volunteers, indicating that the university is transparent in its USR activities. The findings highlight the importance of USR actions and how these activities lead to increased student satisfaction. The study also discusses the model adopted by the university to achieve higher standards of USR that other HEIs can adapt. 2024 Nova Science Publishers, Inc. -
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 -
Social, Medical, and Educational Applications of IoT to Assist Visually Impaired People
General daily tasks have always been a problem for visually impaired people. Identification of daily objects becomes a hectic task. Traditional methods such as a walking stick and a guide dog have been helpful to the visually impaired for basic navigation. Such, methods have a lot of limitations and often fail under varied situations. Technologies such as Computer Vision and Pattern Recognition (CVPR), Image Processing (IP) Internet of Things (IoT), etc. have made a major contribution to overcoming the limitations. IoT brings a lot of technical and automated solutions to assist the visually impaired people. Data science and analytics are a major part of the process. Data accumulated via various sensors can be processed and used to identify obstacles and enhance basic navigation using haptic and voice feedback. Raw data goes through a series of analysis and refinement. This is then processed into a form which is understandable to the system and can be directly interpreted to perform various components of an application. These applications involve education, navigation, entertainment, security, consumer, etc. These applications are across various verticals of technologies differing in terms of hardware, software, and protocols. Various economically feasible and accurate solutions are now available. While, optimization remains an issue. These devices have generally been very helpful to ease the lives of visually impaired people. The main aim of this article is to provide essential details related to real-world applications of IoT in the field of education, healthcare, entertainment, security, navigation, and solutions to address the daily challenges faced by visually impaired people. The structure of the article includes introduction to IoT, applications of IoT in modern era is dealt in detail in Sect.10.1. Followed by hardware device and communication technologies in Sect.10.2. Section10.3 deals with state of art which focus majorly on research contributions related to applications of IoT and smart devices benefiting the lives of visually impaired. Section10.4 incorporates the future scope and concludes with a summary in Sect.10.5. The article covers more than 30 research contributions in the pastten years which includes journal papers, conference papers and patents which provide a detailed and clear view on the research being carried out in the field of IoT to help the visually impaired. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Social-cognitive Skills Training on Interpersonal Understanding of Social Norms During Adolescence
Background: Social-cognitive skills training (SCST) in a therapeutic setup can result in more positive outcomes when incorporated with psychotherapy, especially among adolescents with minor social-cognitive impairments in their social interactions. It may result in multifarious benefits to mitigate their social-cognitive dysfunction. This study aimed to identify the effects of SCST on interpersonal understanding of social norms in adolescents with low social cognition. Methods: In this quasi-experimental research, 80 adolescents (1019 years) with low social cognition, no previous experience of skills training, and absence of any psychological disorders, especially those that affect their social-cognitive functioning, with assent from the participants and written informed consent from the parents/guardian and a score below 58 on the Need For Social-Cognition Scale, were included. They were randomly allocated into SCST or waitlist control group. SCST consists of 20 sessions with indoor activities, games, and discussions, and it has been arranged for 1 hour per 3 days a week for 3 months. Edinburgh social cognition test (ESCoT) was used to assess the degree of interpersonal understanding of social norms among adolescents as part of pre and posttests. Results: The Wilcoxon Sign Ranked Test showed that the interpersonal understanding of social norms after SCST is significantly higher than the interpersonal understanding of social norms SCST with a large effect size. The mean (standard deviation) scores in the ESCoT test improved significantly (P < 0.001) following [W = 0.001, P <.001, r = 1.000]. Conclusion: SCST effectively improves the interpersonal understanding of social norms, an essential developmental milestone during adolescence. It highlights the importance of focusing on mental health as a developmental asset that can influence social-cognitive development in the future. 2024 The Author(s). -
Social Work Intervention Research in Child Sponsorship Programs: Enhancing Psychological Well-being of Marginalized Adolescents
The Child Sponsorship Program (CSP) is critical to enhancing the objective and subjective well-being of enrollees. Meanwhile, social work interventions emphasize scientific approaches aimed at empowering marginalized populations. This intervention research (IR) was focused on raising the psychological well-being (PWB) of adolescents in a prominent CSP located in Kochi, Kerala. Preliminary findings from a pilot study underscored the need for intervention, and subsequent Delphi survey results guided the formulation of an intervention strategy. Capitalizing on the transformative power of peer groups, IR implemented a social group work intervention to enhance adolescent PWB in CSP. Using a nonequivalent comparison group interrupted time-series design, the PWB of participants in the intervention group (IG, N = 20) and comparison group (CG, N = 20) was measured and compared. Ryffs PWB scale with 42 items served as the assessment instrument. Descriptive statistics confirmed the normal distribution of baseline data for all participants (N = 40), while repeated measures ANOVA in SPSS 25 validated the alternative hypothesis, indicating significant differences in PWB measures over time within IG and between IG and CG. Additionally, along with statistical evidence of intervention effectiveness, this study used a qualitative design for ongoing evaluation of the intervention process, providing insights for program refinement and demonstrating intervention outcomes. By defining a model for group work intervention among CSP adolescents to improve PWB, this study underscores the important role of social work interventions in empowering marginalized populations. The Author(s) 2024.