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A Study on Critical Success Factors for Successful ERP Implementation at Indian SMEs
ERP (Enterprise Resource Planning ) comprises of a commercial software package that promises the seamless integration of all the information flowing through the company??financial, accounting, human resources, supply chain and customer information (Davenport, 1998). Much has been written on implementation of Enterprise Resource Planning (ERP) in organizations of various sizes. The literature is replete with many cases studies of both successful and unsuccessful ERP implementations. Research on the implementation of ERP in certain European countries shows that, the job of implementing an ERP is a riskier business for Small and medium-size enterprises (SMEs) than for Large Enterprises (LEs), still SMEs have been receiving lesser focus from the software vendors and consultants than LEs (Shanks et al.,2000). There have been very few empirical studies that attempt to delineate the critical success and failure factors that drive the success and failure of ERP implementation at Indian SMEs. Much of the time, ERP software vendors and consultants are the targets for blame when anticipated results do not materialize. Are the ERP vendors and consultants that sold the software the real culprits for the lack of business performance improvement? (Rao, 2000).The failure rates of ERP implementations have been publicized widely but, this has not distracted companies from investing large sums of money on ERP implementation. Many companies in developing countries have implemented ERP to capture its benefits still there is a lack of examining Critical Factors (CFs) that contribute in the success and failure of ERP implementation at Indian SMEs(Ranganathan and Kannabiran, 2004). In this dissertation, a framework has been adopted to cover both the national (Indian) and the organization size (SMEs) aspects to identify and rank the CFs that contribute in the success and failure of ERP implementation at Indian SMEs. Four models (ERP model, ERP Implementation Success Model, ERP Implementation Failure Model and ERP Gap (Strategic ERP) Model) were developed to explore and rank the thirty Critical Success Factors (CSFs) along with the twenty Critical Failure Factors (CFFs) that contribute in the success and failure of ERP implementation at Indian SMEs. Key Critical Success Factors (KCSFs) and Key Critical Failure Factors (KCFFs) were identified by ranking of these CSFs and CFFs according to their importance to decide their priorities during the ERP implementation at Indian SMEs. Quantitative survey based method was used to explore what are the possible critical success and failure factors that contribute in the success and failure of ERP implementation at India SMEs .Three close ended questionnaire were used to collect the data from the 500 Indian ERP consultants those who are having experience of ERP implementation in India for almost all types of Indian industries including Indian SMEs. Sample was drawn from ten national and international well known IT (ERP) sector companies which are involve in world wide ERP implementation including Indian SMEs. The Indian ERP consultants have been selected for the data collection using non probabilistic sampling method. The data collected were analyzed using statistical techniques such as descriptive statistics, reliability tests, validity tests, exploratory factor analysis and non parametric tests. In order to explore thirty CSFs and twenty CFFs along with the KCSFs and KCFFs, three close ended questionnaires were customized with the help of literature reviews and experts opinions. Later on it has been standardized for this research with the help of Cronbachs Alpha readability and validity test (Guilfords formula) supported by exploratory factor analysis. Based on the Indian ERP consultants perceptions, literature review, and secondary data review it was found that an ERP implementation at Indian SMEs is not exactly same from the ERP implementations found in the existing literature for the worldwide Large Enterprises (LEs). When discussing the CSFs and CFFs for an ERP implementation at Indian SMEs, it was found that although the factors are more or less same but the importance of factors in term of their priorities (importance) are defiantly different from the ERP implementation of the LEs. -
CRM Practices in Private Commercial Banks, Influencing Long Term Relationship and Customer Centric Holistic Approach
Purpose: The exigent purpose of the research is to find out whether socio-economic characteristics impress the study on CRM in private banks and to study CRM practices, factors sway long term relationship between customer and banks, and to know CRM as a customer central holistic approach. CRM gaining more attention as it is attracting and retaining the customers. CRM technology is used to organize, mechanized and integration of sales, marketing, support service and technical support (Robertshaw, 1999). There is a tremendous changes in market, innovation of technology, regional integration increasing competition and especially moderating customers. Approach: A well structure questionnaire was recommended for data collection in order to avoid delay, non-response and incompleteness. Respondents were met while they approached the bank. Either before or after their work respondents were appealed to provide the suitable consumer. A total of 220 questionnaires were in the hand and out of this 200 were usable and this forming 91% success rate. Findings: There is a significant variation in socio economic uniqueness except the demography account at different bank branches and all the factors shows high relationship sums account at different banks. The CRM practices ranked by respondents in the rank-wise are providing security of funds, providing greater value for money and transparency in banking services. Factors like customer satisfaction, well developed privacy policy and quick service are influencing better forever relationship between private sector banks and customers. The measurement of CRM a customer centric approach reveals that CRM protects data privacy, establishes and maintains strong relationship and CRM anticipates anticipates needs of customers. Further factors like data privacy, retention of existing customers and establish and maintain strong relationships are the impressing factors of customer centric approach. 2024, Collegium Basilea. All rights reserved. -
Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Deep learning approaches to understanding psychological impacts on vulnerable populations
This chapter investigates the psychological effects on vulnerable groups, with a particular emphasis on the relationship between deep learning techniques and the impact of climate. Vulnerable groups confront particular problems, which might lead to negative psychological results. Investigating this complexity is critical to designing effective intervention techniques. Using sophisticated deep learning techniques, this study seeks to find subtle patterns and correlations in a variety of datasets, including psychological markers, socioeconomic characteristics, and climatic variables. The work employs a comprehensive technique that includes deep learning models, feature extraction, and interpretability analysis to untangle complicated relationships. Preliminary findings imply that deep learning approaches might uncover previously unknown links between climate change and psychological effects on vulnerable groups. This insight adds to a more comprehensive understanding of the difficulties. This understanding contributes to a more holistic grasp of the challenges faced by these groups. By including climate-related factors into the deep learning framework, this study hopes to close the gap between environmental impacts and psychological 2024, IGI Global. All rights reserved. -
Enhancing Movie Genre Classification through Emotional Intensity Detection: An Improvised Machine Learning Approach
Movie Genre Classification through Emotion Intensity is a computer vision technique used to identify facial emotion through a sequential neural network model and to get the genre of the movie with it. This paper delves into latest advancements in Emotion Detection, particularly emphasizing neural network models and leveraging face image analysis algorithms for emotion recognition. Grenze Scientific Society, 2024. -
How Are We Surviving the Pandemic, COVID-19?: Perspectives from Hospitality Industry Workers in Australia
The COVID-19 pandemic has been disastrous and has affected the hospitality industry worldwide, and the people working in the sector were impacted immensely. The purpose of this study is to understand the viewpoints of hospitality workers in Australia on how lockdowns have impacted professional and personal well-being. The case study methodology is adopted for this study. Viewpoints from Australian hospitality workers were collected through semi-structured interviews. With the pandemic taking surprising turns with the rise of new infections and in turn new pandemic waves, the industry is facing a constant lurking fear of lockdowns. Changing variants of COVID-19 creates a profound effect on the psychological and personal well-being of the people employed in the hospitality sector. This chapter would reflect upon the viewpoints of hospitality workers in Australia after two years of the COVID-19 crisis. A real-time assessment is required to understand the vulnerability of hospitality industry workers in a developed country. 2023 Priyakrushna Mohanty, Anukrati Sharma, James Kennell and Azizul Hassan. -
Transition to an Empty Nest: A Phenomenological Exploration of Homemaker Mothers of Out-of-State College Students
This phenomenological study explores the lived experiences of Indian homemaker mothers transitioning to an empty nest, focusing on middle-aged mothers emotional, psychological, and cultural dimensions. Five homemaker mothers, aged between 46 and 50, with at least one child attending an out-of-state college in Bengaluru, participated in semi-structured interviews. Data was analysed using Colaizzis descriptive phenomenological method, which revealed 95 minor themes organised into 9 major themes and 4 overarching categories. The findings highlight a complex emotional intersectionality where feelings of pride and joy in their childrens achievements coexisted with sadness, loneliness, and loss. Cultural expectations surrounding motherhood in India, which emphasises maternal self-sacrifice, further emphasised the emotional challenges the participants face. Coping strategies such as spirituality, social support, and technology emerged as key elements in navigating the transition, with participants often using prayer and digital communication to maintain emotional bonds with their children. The study also found that the empty nest transition triggered a redefinition of parental roles and personal identity. Participants with higher education levels were better equipped to embrace this phase as an opportunity for self-growth, while others struggled with their diminished caregiving role. Technology played a dual roleoffering emotional comfort through digital connection and fostering dependency and frustration. Overall, the empty nest experience for Indian homemaker mothers is emotionally challenging and a potential period of personal rediscovery, shaped by cultural norms and the evolving role of family dynamics. This research provides a culturally specific perspective on a largely Western-studied phenomenon, offering insights for further investigation into the changing maternal identity in India. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al. -
Nonlinear analysis of the effect of viscoelasticity on ferroconvection
Thispaper concerns a nonlinear analysis of the effects of viscoelasticity on convection in ferroliquids. We consider the Oldroyd model for the constitutive equation of the liquid. The linear stability analysis yields the critical value of the Rayleigh number for the onset of oscillatory convection in Maxwell and Jeffrey ferroliquids. The use of a minimal mode double Fourier series in the nonlinear perturbation equations yields a KhayatLorenz model for the ferromagnetic liquid, and that is scaled further to get the classical Lorenz model as a limiting case. The scaled KhayatLorenz model thus obtained is solved numerically and the solution is used to compute the time-dependent Nusselt number, which quantifies the heat transport. The results are analyzed for the dependence of the time-averaged Nusselt number on different parameters. 2021 Wiley Periodicals LLC -
Brain Tumor Detectin Using Deep Learning Model
Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient's quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively. 2023 IEEE. -
Development and characterization of carbon fiber reinforcement in Aluminium metal matrix composites
Carbon fibers (CF) possess exceptional mechanical properties and the highest degree of chemical stability. However, carbon reinforcement in metal matrix composites is extremely scarce due to production difficulties, particularly in obtaining a uniform distribution. Carbon fiber reinforced composites are typically made using high temperature processing processes. However, the fibers must be coated with Ni or Cu in order to achieve effective particle dispersion; otherwise, there is a larger likelihood of intermetallic compound formation, which reduces the chances for enhanced properties. In this work, the metallurgical, mechanical, and tribological characteristics of the carbon fiber reinforcement in AA 7050 are examined. Uncoated carbon fibers are reinforced into the Aluminium matrix using a low temperature processing technique known as powder metallurgy. The AA 7050 matrix reinforced with carbon fibers at various weight percentages between 0 and 1.5. The samples undergone mechanical and metallurgical testing in accordance with ASTM guidelines. The findings indicate that the 0.25 weight percent carbon fiber reinforcement in the matrix increased the material's hardness by 30% over the monolithic alloy, making it an excellent alternative for structural applications. Published under licence by IOP Publishing Ltd. -
Experimental Investigation of Nano Hexagonal Boron Nitride Reinforcement in Aluminum Alloys Through Casting Method
Aluminum metal matrix composites (AlMMCs) have a significant impact on a variety of industries that seek for innovation, efficiency, and sustainability. AlMMCs are substantial because of the special combination of properties that make them an essential part of contemporary production and design. Custom made properties of the AlMMCs can be obtained by the reinforcing different ceramic particles. Among the reinforcements, nano hexagonal boron nitride were rarely used. Hexagonal boron nitride particles have self-lubrication properties and it is one of the promising substitutes of graphite. The incorporation of hexagonal boron nitride (hBN) as a reinforcement material in aluminum alloys has garnered significant attention in recent years. This paper provides an overview of the reinforcement of nano hBN in aluminum alloys through casting method and highlights the mechanical and thermal properties of these alloys. The results show that the wear rate of the composite at 2wt.% is 9.91% lower for a load of 40 N when compared to unreinforced composite. Furthermore, the impact of hBN content, dispersion, and processing parameters on the properties of the composites is analyzed. The unique structural and thermal properties of hBN, along with excellent lubricating abilities, make it a promising candidate for reinforcing aluminum composites. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Processing of nanoreinforced aluminium hybrid metal matrix composites and the effect of post-heat treatment: a review
The demand for cutting-edge materials with a high strength-to-weight ratio and economic considerations is steadily increasing. Lightweight materials such as aluminium (Al) and its alloys are attractive, but some properties such as low thermal stability and high wear rate limit the application of aluminium alloys (AA) to some extent. Many researchers have developed various composites to get around these restrictions and increase the performance of aluminium and its alloy. Metal matrix composites (MMCs) with nanoparticles have revealed greater mechanical and tribological properties compared with micron-sized reinforcements. Most engineering applications require materials with excellent multidimensional properties, which are difficult to achieve using single reinforced MMCs. Hybrid metal matrix composites (HMMCs) with superior properties are the latest trends in composite technology. The choice of reinforcement selection has a vibrant role in the manufacturing of hybrid metal matrix composites. Researchers face a major challenge in finding optimum reinforcement combinations and their corresponding concentrations. The manufacturing of nanocomposites is difficult due to their high surface area and energy. To determine the most effective reinforcement combinations for hybrid composites, this article addresses several nanoreinforcements, their effects, and the appropriate processing methods for aluminium and its alloys. Researchers have paid less attention to the impact of precipitation hardening in aluminium and its alloys; thus, this paper also considers the effect of post-heat treatment ofaluminium composites. 2022, King Abdulaziz City for Science and Technology. -
Characterization and Analysis of Carbon Fiber and Nano hBN Reinforced Hybrid Aluminium Metal Matrix Composites by Conventional Sintering
As mono composites focus solely on improving one property at a time, the significance of hybrid composites grows day by day. The incorporation of carbon fiber (CF) and nano hexagonal boron nitride (hBN) particles as reinforcement for Aluminium has gained significant popularity because of their superior properties. In this work, the AA 7050 is reinforced with carbon fiber and nano hBN and fabricated by powder metallurgy method. To achieve an effective nanoparticle distribution in the matrix, premixing of the particles was done. The reinforcements were effectively dispersed by ball milling, and the composite was created using a traditional powder metallurgy. Dispersion of the particles in the matrix was analyzed by optical microscope. The effect of adding reinforcement to the matrix was investigated using properties such as micro hardness and compression test, and wear characterization. A significant increase in mechanical and wear properties was achieved for the combination of 0.25 wt. % carbon fiber and 0.5 wt. % hBN addition due to the uniform dispersion of nano particles along with the carbon fiber presence. The microhardness, compressive strength and wear rate is improved by 33%, 66% and 54 % respectively than the bare alloy. This study provides insight into the importance of hybrid Aluminium nano composites for high strength applications. Author(s) 2024. -
Effect of Premixing Process on the Uniform Distribution of Nano hBN and Carbon Fiber Reinforcements in AA7050 Matrix
In Aluminium metal matrix composites, achieving a homogeneous dispersion of reinforcements remains a significant challenge, especially when mixing fibrous and nanoscale reinforcements. The effect of the premixing procedure on the homogeneous dispersion of carbon fiber (CF) and nano hexagonal boron nitride (hBN) reinforcements in AA7050 matrix is studied in this work. Before composites are prepared, a multi-stage process for premixing is used, which consists of ultra-sonication, magnetic stirring, and mechanical mixing in order to minimize particle clustering. This also improves the wetting between the reinforcement and the matrix. Field emission scanning electron microscope (FESEM) was used to characterize the premixed powders to assess agglomeration behaviour, interfacial integrity, and dispersion uniformity. Due to the premixing process, better densification of nearly 95.2% and enhancement of 33.3% of micro-hardness are reported for 0.25 wt.% CF and 0.5 wt.% hBN addition. The results reveal that after the premixing process, particle dispersion was improved, leading to high-quality composites in the subsequent sintering process. The premixing process offers a better way to disperse the nano reinforcement particles in the production of aluminium metal matrix nanocomposites, which directly influences the properties of the composites. -
MR Brain Tumor Classification and Segmentation Via Wavelets
Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique. 2018 IEEE. -
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved. -
The Psychosocial Experiences of Family Caregivers of Cancer Patients
Introduction: This study explores the psychosocial experiences of family caregivers of cancer patients, focusing on changes in their lives postdiagnosis, motivations for caregiving, challenges faced, coping mechanisms, and perceptions of caregiving. Understanding these experiences is crucial for enhancing support services for caregivers. Methods: A qualitative, exploratory study employing interpretative phenomenological analysis was conducted with 10 family caregivers. In?depth interviews were used to gather data on their lived experiences, which were analyzed to identify key themes. Results: Findings revealed significant emotional shifts, lifestyle adjustments, and changes in caregivers perceptions of the patient following the cancer diagnosis. Motivations for caregiving stemmed from external influences and personal attributes. Caregivers encountered multiple challenges, including lifestyle disruptions, emotional strain, financial difficulties, and a lack of support. Coping strategies involved prioritization, reliance on personal strengths, spirituality, family support, and rationalization, highlighting resilience and adaptability. Conclusion: Despite the difficulties, caregivers viewed their role as noble and transformative, maintaining a positive outlook and a deep concern for their loved ones well?being. These insights emphasize the need for healthcare providers to develop targeted support interventions to assist family caregivers in managing their responsibilities effectively 2025 Indian Journal of Social Psychiatry. -
Psychological capital in positive ageing :
Positive ageing is feeling good and maintaining a positive attitude, keeping healthy and being fully involved in life. Older adults add value to family and society by sharing of wisdom, gratitude,spirituality, resilience, optimism, hope and confidence (PsyCap). These are the mental resources that developed through their life experiences when things went well and when faced with challenges. The aim was to understand the process of development of psychological capital in positive ageing. The participants were chosen purposively, older adults 70-80 years, men and women, retired, tenth standard, middle socio-economic status, spouses have expired and living with family. They were interviewed with a validated semi structured interview schedule. Themes were analyzed using Interpretative Phenomenological Analysis, substantiated by verbatim from participant interviews and connections with existing theories and literature. Three super ordinate themes emerged, Factors that promote the development of PsyCap varies , Personal trauma and inadequacies as learning opportunities and Spiritual and philosophical ways of adaptation . Results indicated that support from family and friends and their internal strength helped them face adversity and aided in the development of optimism, hope, gratitude, confidence and self-belief. Challenges, lack of adequate resources and retirement were opportunities for learning as they facilitated the growth of PsyCap. Participants were grateful for effectual social support in time of grief. Their resilient attitude kept them positive and helped to prioritize goals effectively. Religion and spirituality provided solace and meaning to their lives, reflection led to the evolving of a philosophy that left them feeling fulfilled as they reached out to those in need. The study has implications for promoting a positive and healthy attitude towards older adults and sensitising family, caregivers and policy makers. -
Islanding detection technique of distribution generation system
Islanding is a condition in which the micro grid is disconnected from the main grid which consists of loads and distribution generation. Islanding is required whenever there is a fault and whenever the maintenance is required. Under normal condition or stable condition, the system works under constant current control mode. After islanding the system switched to voltage controlled mode. There are different methods that can be used to detect islanding situation such as active and passive methods. In this paper DQ-PLL detection technique used for detecting islanding condition is carried out. This paper also explains in detail the advantages of DQ-PLL method for islanding detection The implementation is validated by using MATLAB/SIMULINK software. 2016 IEEE.