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Detection of Fraudulent Alteration of Bank Cheques Using Image Processing Techniques
In todays world illegal alteration and illegal modifications of authenticated financial documents is increasing rapidly as a fastest growing crimes around the world. The result of this kind of crimes may result in a huge financial loss. In this paper image processing and document image analysis techniques are used to examine such cases in order to identify the fraudulent bank cheques. However, it is very difficult to detect an alteration made on documents once the printing ink of alike color is employed. In this paper, alterations and modifications caused with handwritten ball point pen strokes are considered and proposed a technique for recognition of such types of corrections by employing standard techniques under Digital image processing and pattern recognition. The results are quite promising during the experiments conducted. 2021, Springer Nature Singapore Pte Ltd. -
Total syntheses of Prelactone V and Prelactone B
The total syntheses of natural products Prelactone-V and Prelactone-B have been accomplished by a novel Chiron approach starting from D-glucose. The synthesis involves isopropylidene acetal formation of D-glucose using Poly(4-vinylpyridine) supported iodine as a catalyst, Tebbe olefination, Grignard reaction, Wittig olefination, selective mono deprotection of acetal using PMA/SiO2, hydrogenation and anti-1,3-diol formation are as key steps. 2017 -
Performance evaluation of machinelearning techniques indiabetes prediction
Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. -
Performance evaluation of random forest with feature selection methods in prediction of diabetes
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Design And Development Of Artificial Intelligence Based Knowledge Management System For Managing Software Security Vulnerabilities
Software development practices play a signifcant role in building the world s future. It is the place where exciting technological evolution begins in the world. Exploration of critical challenges in the area of software development plays a signifcant role in fueling the pace of technological progression in the industry. This work focuses on exploring important areas of software development practices and problems faced by the industry. Understanding the critical parts of the software system development eco-system and the stakeholders associated with those will be important. Customers of software development teams, the software development industry and knowledge newlinesources, and the software development internal eco-system are the broad focus areas of study. Leveraging the data already spread across the eco-system and facilitating easy newlineaccess to practitioners as and when there is a need will be one of the primary focuses. newlineThe software development landscape module, customer landscape module, and industry landscape module are the key modules that will be explored in this work. The core aspiration of the work will be to integrate all the possible data across the industry newlineand process the same and make it easily accessible to the practitioners as and when they are needed. The process also makes the data smarter and more insightful over time. -
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A Real-Time Approach with Deep Learning for Pandemic Management
It has never been so critical to managing pandemic situations created by a virus like COVID-19, which has brought the world almost to a standstill, claiming millions of lives. Learning from all earlier viruses and building a quick tackling mechanism is a need of the hour. There is a greater need for technology to collaborate with healthcare and leverage each of the domains expertise. With less time in hand, this collaboration must happen in a short time. There is a need to study the exiting progression in technology and the healthcare landscape to bring them to a common path for practical solutions. In the chapter, an attempt was made to put together some thoughts in both fields to relate them to pandemic managements frequent subject. Caution is drawn towards some crucial aspects, such as security and transparency, that cannot be compromised in this journey. Artificial intelligence (AI), being at the forefront of the technology supporting lives, provides a greater hope in this direction. Some of the prominent approaches can be looked at from a pandemic management point of view, which can start a more in-depth discussion on AI and healthcare going hand in hand in managing this pandemic situation. Essential areas of pandemic management, such as building on the knowledge gathered over a period, plugging in the real-time data from the society, building efficient data management systems and building transparent and interpretable solutions are the focus areas of exploration in this chapter. 2022, Springer Nature Switzerland AG. -
Data set on impact of COVID-19 on mental health of internal migrant workers in India: Corona Virus Anxiety Scale (CAS) approach
The article presents a unique dataset on mental health of internal Migrant workers in India. The dataset was constructed during the pandemic when the entire nation was the victim of stringent measures to curtail the spread of Corona Virus in the form of travel restrictions and lockdowns. We collected this data in our pursuit to submit a paper in response to call for paper in the Journal titled Migration and health. Non-availability of authentic data about the internal migrant workers triggered this effort to compile the data. We have recorded 1350 responses out of a 6897 Sample through snowball sampling method. Every respondent is said to be a referee for further driving of sample. The responses were collected between June 2 and August 30, 2020 through the telephonic interviews. Also, the consent of the respondents has been duly obtained for publication of the data without revealing their identity. The interview schedule was adopted by using Corona virus Anxiety Scale (CAS) which uses four dimension model namely Cognitive, Emotional, Behavioral and Psychological. The Interview schedule was originally designed in English but was later translated into three different languages after consulting the language experts. This article provides descriptive statistics of study variables along with socio economic factors. This dataset provides a significant platform for further research related to CAS and in assessment of mental health of vulnerable groups. 2021 -
Optimization of anti-corrosion performance of novel magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles on Al in simulated acidizing environment using RSM
The suitability of newly synthesized magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles (Ag@PANI-CS-Fe3O4) as a robust corrosion inhibitor for Aluminum (Al) in a 5 M HCl environment has been investigated via Weight Loss (WL), Alternating Current (AC)-Impedance Spectroscopy (IS), Potentiontiodynamic polarization (Tafel plots), and Scanning Electron Microscopy (SEM) techniques. The protection efficiency (PE) was mathematically modeled using the Response Surface Methodology (RSM) to fit an empirical relation in terms of temperature, nanocomposite concentration, and time using the face-centered central composite design. The model was accurate with a coefficient of determination (R2 = 99.27%). The negative Gibb's free energy of adsorption (?Gads) values confirmed the spontaneity of Freundlich adsorption isotherm process on Al in 5 M HCl solution. The optimization simulation yielded maximum protection efficiency (of 97.88%) at 5 mg/L nanocomposite concentration, 1 h time, and an intermediate temperature of 304.8 K. Furthermore, the sensitivity of PE was evaluated to find that the low temperature 303 K is favorable for PE, whereas higher temperature will act adversely on PE. The results obtained by the RSM model are in agreement with the experimental observations. 2021 Elsevier B.V. -
Study of 1s internal bremsstrahlung spectrum from 57Co
The internal bremsstrahlung contribution from the electron capture of 57Co has been measured in coincidence with K-X-ray of the residual atom. The end-point energy (EPE) is extracted from the data using the linearised Jauch plot. The transition energy obtained using the (EPE) is 842.7keV, which is close to the value given by Audi and Wapstra. The measured intensity and shape factor from 300 to 600keV are found to be in good agreement with the Glauber and Martin theory. 2002 Elsevier Science Ltd. All rights reserved. -
Impact of visual hierarchy on user experience in e-commerce websites
The primary aim of this book chapter is to propose a model that explains the influence of four elements of visual hierarchy-colour, size, alignment, and font/characters-on the user experience of e-commerce websites. The study's sample comprised 312 customers of e-commerce websites, with the four elements of visual hierarchy as independent variables, and the user experience of e-commerce websites as dependent variable. To analyze the data obtained from e-commerce website users, the researchers employed structural equation modeling to assess the relationships. The results of the analysis showed that the proposed model had acceptable fit indices and all the four elements of visual hierarchy had a positive impact on the user experience of e-commerce websites. While this book chapter examined the individual contributions of visual hierarchy elements, investigating how different combinations of visual hierarchy elements influence user experience could provide insights into optimal design strategies for e-commerce websites. 2024, IGI Global. All rights reserved. -
Enhancing academic credential verification through blockchain technology adoption in university academic management systems
Blockchain technology has emerged as promising solution in various sectors, including higher education. This research investigates the impact of usage of blockchain technology in student credential verification within university academic management system. This study employs a descriptive research through quantitative analysis of data collected from universities that have integrated or planning to integrate blockchain technology into their academic management systems. Key parameters examined include awareness and familiarity with blockchain, extent of blockchain usage, user experience and satisfaction, the perceived impact and benefits. The findings suggest that blockchain technology positively influences academic credential verification process, streamlining data sharing and reducing administrative burdens. As blockchain continues to transform the academic management landscape, this study offers timely guidance for stakeholders navigating the intersection of technology and education. 2024, IGI Global. All rights reserved. -
Transforming future startups through servant leadership and social entrepreneurship profitability
This book chapter delves into the influence of servant leadership on the profitability of social entrepreneurship, a phenomenon widely recognized by thriving startups worldwide. It generates hypothesis about the effect of seven facets of servant leadership viz., empowerment, conceptualization, followers influence, emotional convalescence, follower's growth and succession, value-creation for the community, and ethical behavior on social entrepreneurship profitability. The data was received through e-mail survey from 158 entrepreneurs located in Bengaluru. Hypothesis testing was done using multi-regression analysis technique. Results of the study specify that the seven dimensions of servant leadership except conceptualization have significant impact on the social entrepreneurship profitability. Further, the study continues with discussion of the inferences for practice by the social entrepreneurship, suggestions for future studies, and the continuous advancement of leadership tactics as well as styles that support upcoming social entrepreneurship ventures. 2024, IGI Global. All rights reserved. -
Problems and perspectives in inventory management of fruits and vegetables at HOPCOMS, Bangalore
Increase in demand for Fruit and Vegetables has augmented over the years. Being perishable they are restricted to a limited life span. The time dependency on perishable commodities acts as a barrier to retain the freshness and quality in fruits and vegetables for a longer period. Therefore, Inventory management is vital to manage perishable commodities as it brings in transparency to the actual demand from the customers. The retailer being the key element in the supply chain to come in contact with the customer should follow up with techniques to manage overstocking and stock out situation. The present study focuses on bringing in inventory management in HOPCOMS, a cooperative society in Karnataka. In the road to satisfy the customers, it is necessary for the society to come up with different strategies to manage the inventories. Different inventory evaluation methods are studied in relation to perishable commodities and the factors affecting the same. FIFO (first in first out) an inventory evaluation method was found to be more efficient and must be considered practically by the retailers to manage inventories during the sales. However, with efficient infrastructural facilities, interference of state government to bring in cold storage facilities and, creating awareness regarding the actual demand for a commodity in the market, the retailer would be able to balance overstocking and stock out situation in the future. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Role of AI in the inventory management of agri-fresh produce at HOPCOMS
Inventory management is vital for maintaining the efficiency of supply chain management. Fruits and vegetables being perishable in nature should involve inventory management to avoid wastage and loss in terms of over stocking and stock out situations. The present study focuses on the role of artificial intelligence (AI)-powered inventory management of fruits and vegetables at HOPCOMS, a cooperative society founded in Bangalore. In the road to satisfy the customers, it is necessary for the society to come up with different strategies to manage the inventories in which a retailer confronts overstock and stock out situation, affecting the profit of the society. Therefore, a study was conducted with the help of structured questionnaire among 122 retailers of HOPCOMS outlets in Bangalore. The results obtained from the study suggest that inventory valuation method positively influences AI-powered demand forecasting and customer order fulfillment, and AI-powered demand forecasting is positively related to customer order fulfillment. 2023, IGI Global. All rights reserved. -
Riding the waves of culture: An empirical study on acclimatization of expatriates in IT industry /
Problems And Perspectives In Management, Vol.16, Issue 3, pp.432-442 -
Implementation challenges of Total Quality Management (TQM) in dairy sector /
Smart Journal of Business Management Studies, Vol.15, Issue 1, pp.1-9, ISSN No: 2321-2012. -
Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm
Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications. 2024 Elsevier B.V. -
A Comparative Assessment of Cascaded Double Voltage Lift Boost Converter
In several power conversion applications, dc-dc boost converters with voltage boost techniques are extensively used in order to meet the growing power demand. The main drawback of conventional dc-dc boost converter is obtaining high DC voltages, when operated at high duty ratio which causes switching losses and decreases overall efficiency because of the switch being used to be in 'ON' state for long time and voltage stresses across switch increases. The main objective of proposed converter is to obtain high voltage without extreme duty ratio. When input voltage of 15V DC is given, 201.1V DC output voltage is attained at duty ratio of 0.4 by the cascaded double voltage lift boost converter. To validate the performance of proposed converter, simulation is carried out in LTspice XVII and a comparative assessment of proposed converter with other converters at different duty ratio are realized. 2020 IEEE. -
A Review on DC-DC Converters with Photovoltaic System in DC Micro Grid
Photovoltaic system is the low-cost source of electrical power in high solar energy regions. The benefits of PV system are like nonpolluting and minimum maintenance. Solar energy changes as per irradiance and temperature and also one factor which reduces the power output is the partial shading in the cells. Hence f o r th, various algo rith ms a r e p u t fo rth to obta in t h e maximum power f r o m t h e PV arrangement and dc-dc converters intend to regulate the supply. The concept of micro grid is emerging as an excellent solution for inter connecting renewable energy sources and loads. DC micro grid is a necessity in today's world. There is wide increase in usage of DC systems in commercial, residential and industrial systems. DC micro grids are dominant in reliability, control and efficiency. Direct current architectures will be used in demand in the future electrical distribution systems. This paper reviews on all above concepts to be used in DC micro grid for future DC applications. Published under licence by IOP Publishing Ltd.