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Flexible and cost-effective cryptographic encryption algorithm for securing unencrypted database files at rest and in transit
To prevent unauthorized access to the databases and to ensure that the data of the databases is protected from intruders and insiders, the data is being encrypted at the storage locations. The same goal is achieved with Transparent Data Encryption, a feature that can be found in almost all database products. However, it has been observed that the non-datafiles are being ignored and there is no standard encryption for them like there is for datafiles. Moreover, there was no standard algorithm to encrypt them without relying on third-party tools. Therefore, This study provides a robust algorithm to perform the encryption. This presentation also describes the importance of non-datafiles encryption, and how some non-datafiles can pose a threat to data and infrastructure without encryption. The practical implementation of the non-data file encryption algorithm shows the authentic results. Further, unlike existing algorithms, the proposed algorithm gives the file owner full control over the encryption logic. In the encryption process, two levels of encryption logics are combined with a passcode lock, while the same combination of two levels of reversing encryption and passcode is used in the decryption process to convert encoded data back into text format. 2022 The Author(s) -
Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors. -
Experimental instigating a counter cultural film platform in Bangalore /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.189-297, ISSN No: 2045-6298. -
Mentha spicata assisted AgCuO nanocomposite enables anti-diabetic and vitamin-C sensing activities
Diabetes mellitus (DM), a multifactorial chronic health condition, affects a sizable portion of the global population, and more people are expected to contract it in the future, according to the World Health Organisation (WHO). Diabetes mellitus can be treated with conventional drugs, but most of the medications have a variety of side effects. The use of nanocomposites (NCs) to treat diabetes has been prioritized in this scenario. In this study, AgCuO NCs were synthesized using a green method using Mentha spicata leaf extract and their physicochemical properties were investigated with a variety of analytical techniques. According to an extensive in vivo and in vitro analysis of the biological activities of as-synthesized AgCuO NCs, AgCuO NCs possess effective antibacterial, anti-diabetic, and anti-hyperlipidemic characteristics. When AgCuO NCs are administered to STZ-induced animals in a concentration-based manner, the blood levels of inflammatory and liver marker enzymes are reduced and antioxidant enzyme levels are increased. Besides, AgCuO NCs exhibit excellent sensing activity with a limit of detection of 86 nM against Vitamin-C. This study reveals that AgCuO NCs derived from Mentha spicata may, therefore, prove to be a very successful anti-diabetic and biosensor candidate in the future. 2024 Elsevier B.V. -
Ethical imperatives and frameworks for responsible AI adoption in digital entrepreneurship
This study explores ethical dimensions in AI adoption for digital entrepreneurship. Thematic analysis highlights transparency, fairness, and accountability. Findings recommend comprehensive ethical guidelines, inclusive decision-making, and robust accountability mechanisms. Practical implications extend to digital ventures, policymakers, and educators. Future research may delve into industry-specific nuances, cross-cultural analyses, and the longitudinal impact of ethical frameworks, contributing significantly to responsible AI adoption discourse in digital entrepreneurship. 2024, IGI Global. All rights reserved. -
An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network
Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour. 2022 Elsevier Ltd -
The nutraceutical properties and health benefits of pseudocereals: a comprehensive treatise
This review article depicts the possible replacement of staple cereal sources with some pseudocereals like Chia, Quinoa, Buckwheat, and Amaranth, which not only provide recommended daily allowance of all nutrients but also help to reduce the chances of many non-communicable infections owing to the presence of several bioactive compounds. These pseudocereals are neglected plant seeds and should be added in our routine diet. Besides, they can serve as nutraceuticals in combating various diseases by improving the health status of the consumers. The bioactive compounds like rutin, quercetin, peptide chains, angiotensin I, and many other antioxidants present in these plant seeds help to reduce the oxidative stress in the body which leads toward better health of the consumers. All these pseudocereals have high quantity of soluble fiber which helps to regulate bowel movement, control hypercholesterolemia (presence of high plasma cholesterol levels), hypertension (high blood pressure), and cardiovascular diseases. The ultimate result of consumption of pseudocereals either as a whole or in combination with true cereals as staple food may help to retain the integrity of the human body which increases the life expectancy by slowing down the aging process. 2022 Taylor & Francis Group, LLC. -
Secure framework of authentication mechanism over cloud environment
Cloud computing offers a cost effective virtual infrastructure management along with storage and application-oriented services to its customers. This innovation quickly turns into a generally very widely accepted worldview for conveying administrations through web. In this way, this administration expert provider must be offer the trust and information security, on the grounds that there is a most vital and profitable and most delicate information in extremely secure using cryptographic techniques to secure the data in cloud. So for ensure the privacy of essential information, it must be secured utilizing encryptions algorithms and afterward transferring to cloud. This paper presents a novel technique for electronic distributed computing administrations utilizing two-variable validation (2FA) access control framework. The prime target of the projected framework is to guarantee a optimal security for all the actors involved in the component design of proposed authentication system. Furthermore, property based control in the framework likewise authorize cloud servers to maximum the access to those clients with the same arrangement of properties while saving client privacy. At long last, we additionally do a reproduction to show the practicability of our proposed framework. The assessment work is done by utilizing expense of communication, data transfer capacity and proficiency of the framework as an execution metric. Springer International Publishing AG 2017. -
Insights of Evolving Methods Towards Screening of AI-Enhanced Malware in IoT Environment
Internet-of-Things (IoT) has been encountering a series of potential form of threats since past half decades. Artificial Intelligence (AI), which is frequently seen to be adopted to solve various challenges in IoT operation, has now been adopted even by attackers for their malicious purposes. Of all forms of threats, AI-enhanced malwares are one of the most potential forms of threats which has its extensive effectiveness towards the complete operation of the entire IoT environment. Hence, this manuscript discusses existing detection and prevention approaches evolved in current literatures to understand various taxonomies of solution-based methodologies for circumventing such threats. The paper also contributes towards highlighting the potential open-ended issues that are yet to be addressed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Diagnosis of compromised accounts for online social performance profile network
Proliferation of internet technologies has changed the way content is created and exchanged through the Internet, prompting expansion of online networking applications and administrations. Online networking empower creation and exchanged the clients produced content and design of a scope of Internet-based applications. This development is fueled by more administrations as well as by the rate of their adoption by the users. While determined spammers misuse the built up trust connections between account proprietors and their companions to proficiently spread malignant spam, auspicious discovery of traded off records is quite challenge, because of the fixed trust association among the administration suppliers, account proprietors, and their companions. The proposed paper depicts a novel method to notice the cooperated user account in systems like Facebook and twitter. Our novel scheme consists of statistical method of modelling and detected to identity accounts that behaves a sudden change along with detected the compromised accounts. This paper gives validation of these behavioral elements by gathering and dissecting genuine client clickstreams to an OSN site. Taking into account our estimation study, further devise every client's social behavioral profile (SBP) by joining its separate behavioral element measurements. We assess the capacity of social behavioral profiles in recognizing distinctive OSN clients, and the simulation results demonstrate the social behavioral profiles precisely separate every OSN clients and distinguish traded off records. 2016 IEEE. -
Development challenges for agriculture in Maharashtra
Maharashtra is heralded as one of the economically advanced states, but this illusion crashed under the attack of COVID-19 virus and economic deterioration is expected to follow. It is argued here that the state policy dished out a raw deal to the agricultural sector and set the sector under severe stress. The path of this retrogression, reasons behind the trends and the possible policy platform for the last six decades are traced. Stagnation has gripped the agricultural sector, and it is losing cultivable land to other uses. This is accompanied by a sharp increase in small and marginal holdings. Surprisingly, the state has no agricultural policy document in place and the sector largely depends on only sporadic firefighting approaches with a policy paralysis. 2020 Economic and Political Weekly. All rights reserved. -
The broad-basing process in India and muslims
Muslims are recognised as numerically the most important among religious minorities in India. Broad-Basing has covered them, but the rate of catching up with the rest is not satisfactory. There has been a faster decline in poverty rate among Muslims than among the rest. The preponderance of the informal economy into which most of the Muslim workers are caught, their lower representation in higher education and gender biases are major stumbling blocks in their progress. The lower work participation of Muslim women is a significant factor in Muslims lagging behind others in employment. Most Muslim converts in India are believed to have come from the lower social groups, particularly artisans. Rural artisans suffered deprivation both during the colonial period due to cheaper imports of manufactured goods from England, and also subsequently after independence due to the rise of modern industry. Most of the artisans were reduced to the status of agricultural labourers. Thus the destiny of Muslims in India is tied up greatly with that of the informal sector. Their Broad-Basing can be promoted with the improvement in the status of the informal sector. 2020 selection and editorial matter, M. V. Nadkarni. -
Sex determination using finger print ridge density among the medical students of NIMS medical college, Jaipur
To determine the sex of an individual plays an important role among forensic pathologists and scientists particularly when the fingerprints recovered from the crime scene does not match any of the criminal record so in that case fingerprint ridge density plays an important role in determining the sex of an individual. The present study was done among the 100 medical students of NIMS Medical college (50 males and 50 females) shobha nagar, Jaipur. Finger ridge density was counted on the radial border of each print. Result of the study shows that females have higher number of finger ridge density count as compared to males. Application of Bayes theorem suggests that finger print ridge density count <14ridges/25mm2 is more likely to be male while finger print ridge density count >14ridges/25mm2 is more likely to be female. 2016, World Informations Syndicate. All rights reserved. -
Hotel Recommendation System Based on Customer's Reviews Content Based Filtering Approach
Recommendation systems are fantastic tools for remembering people's ideas in order to gain knowledge more efficiently and selectively. Recently, booking and searching for hotels online has become more common. As it takes more time, online hotel research is growing more quickly. In addition, the amount of knowledge accessible online is continuously expanding. User preferences have a big impact on hotel recommendations. The most effective recommendations may be made by recommendation systems by utilising historical user preference data. To solve this problem, recommender systems have suggested content-based filtering methods. Product recommendations, recommendations for websites, news articles, restaurants, and TV series are all examples of applications for content-based recommender systems. The dataset for this project includes client evaluations of the offered Kaggle profile. Word embedding, word2vec, and TF-IDF natural language processing methods were used for feature extraction. The algorithm shows the user the top 10 suggested hotels based on the user's past knowledge of the hotel's location. 2022 IEEE. -
Continuance Intention of ChatGPT Use by Students
ChatGPT, an AI language model, has gained significant attention for its potential to enhance educational experiences and foster interactive learning environments. The potential of student interaction via ChatGPT has engendered significant debate around educational technology. It is apparent that the current literature has yet to fully explore the role of ChatGPT in management education. Amidst the increasing integration of ChatGPT into educational contexts, the concept of continuance intention takes center stage. This research paper delves into the nuanced landscape of students continuance intention regarding the use of ChatGPT in educational settings. We ground our study in Technology Continuance Theory and Theory of Planned Behavior to examine students continuance intention to use ChatGPT. By investigating the determinants that shape this intention, we aim to provide insights that inform educators and educational technology designers in optimizing the integration of AI-driven tools like ChatGPT. This study contributes to the growing body of research at the intersection of AI and education, offering valuable implications for both theory and practice. 2024, IFIP International Federation for Information Processing. -
Is ChatGPT Enhancing Youths Learning, Engagement and Satisfaction?
Integration of artificial intelligence (AI) in educational practices necessitates the understanding of the influence of tools such as ChatGPT. Self-determination Theory (SDT) has been used to examine the impact of ChatGPT usage by students for the improvement of perceived learning, engagement and satisfaction. The moderating role of students AI literacy between ChatGPT and the antecedents of intrinsic motivation, autonomy, competence and relatedness. The data was collected through questionnaire from 481 students and structural equation modeling was used to analyze the data. The findings of the study shows that ChatGPT usage impacts students perceived autonomy, competence, and relatedness, enhancing intrinsic motivation. Also, there is a moderation of AI literacy between ChatGPT usage and these psychological needs. This study extends SDT to student interactions with ChatGPT and underscores the pivotal role of AI literacy. The findings contribute to the discourse on AI and education, offering valuable perspectives on students use of ChatGPT and its effect on their academic experience. 2024 International Association for Computer Information Systems. -
Digital Gender Gap, Gender Equality and National Institutional Freedom: A Dynamic Panel Analysis
While digital gender gap is a growing field of research in Information Systems (IS), there remains a dearth of research focusing on it. The objective of this study is to investigate the relationship between the digital gender gap in mobile and internet usage and gender equality. Additionally, this study also examines the impact of national institutional freedoms on the aforementioned relationship. Utilizing the theoretical framework of intersecting inequalities and building upon existing literature on the gender digital divide, this study aims to explore the associations between disparities in mobile and internet usage, gender equality, and the extent of national institutional freedoms encompassing economic, political, and media domains. In pursuit of this objective, we undertake a dynamic panel data analysis using publicly accessible archival data at the country level. The results indicate that national institutions have a significant impact on the relationship between the digital gender gap in internet and mobile phone usage and gender equality. The discussion encompasses the significance of our findings for both study and practice. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A study on causes of job stress in the IT sector of Bangalore
International Journal of Research in Commerce, IT & Management Vol.2, Issue 2, pp. 126-128 ISSN No. 2231-5756 -
A New Facile Iodine-Promoted One-Pot Synthesis of Dihydroquinazolinone Compounds
A one-pot iodine catalyzed reaction has been developed for the preparation of dihydroquinazolinones from isatoic anhydride, enaminones, and amines in modest to good yields. The reaction has been screened in various catalysts and solvents and a gram scale experiment has been performed based on the optimum conditions. A possible mechanism has been proposed based on the control experiments. The reaction has been checked with broad range of substrates. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
Offline Handwritten Character and Numeral Recognition: A Kernel-Based Approach
Automatic character recognition for the handwritten Indic script is a challenging area for research in the field of pattern recognition. Although a great amount of research work has been reported, all the state-of-the-art methods are limited with optimal features. This article aims to suggest a well-defined recognition model which harnessed upon handwritten Odia characters and numerals by implementing a novel process of decomposition in terms of 3rd level fast discrete curvelet transform (FDCT) to get higher dimension feature vector. After that, kernel-principal component analysis (K-PCA) is considered to obtain optimal features from FDCT feature. Finally, the classification is performed by using probabilistic neural network (PNN) on a handwritten Odia character and numeral dataset from both NIT Rourkela and IIT Bhubaneswar. The outcome of the proposed scheme performs better compared to existing models with optimized Gaussian kernel-based feature sets. Copyright 2022, IGI Global.