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Digital Soil Texture Classification Using Machine Learning Approaches
The texture of the soil is an important factor to consider during cultivation. The water transmission property is being regulated by the texture of the soil. To determine sand, silt and clays percentage present in a soil sample, a conventional laboratory method is used, which consumes more time. Digitization in agriculture has given a new direction of innovative research in agriculture domain. In this paper, based on image processing an efficient model has been developed for soil texture classification. Eight different image preprocessing techniques were used for the image enhancement. Out of that, the linear contrast adjustment found to be best in image enhancement. A feature vector was calculated by extracting six different features from the enhanced image. The feature vector of an image is input to the machine learning classifier. The various classifiers used in this research work are SVM, KNN, ANN and PNN. The accuracy of the classifiers was SVM (0.98), KNN (0.89), ANN (0.89) and PNN (0.86). From the result, it is found SVM model has higher rate in classification of soil. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Digital Transaction Cyber-Attack Detection Using Particle Swarm Optimization
The cyber digital world is an essential variant in day-to-day life in advanced technology. There is a better change in the lifestyle as intelligent technology. In larger excite to increase the advanced technology which can be developed to humans in major dependent on network and internet users. Now, in modern times, the internet has changed the primary need in human lifestyle by giving access to everything in the world while sitting in one place knowing and updating the information and usage of online subscribers or Revolution. The world is moving in Rapid and Faster communications within a fraction of a second, at a lesser cost, and it has minimal paper-based processes and relies on the digitization document instead of a paperless environment. The data is handled by finch security practices, which are used in security worldwide to establish protected data management systems like digital lending, credits, mobile Banking, and mobile payment. Cryptocurrency and blockchain, B-trading, and banking as a service are included. At the same time, leveraging the new technologies is to resist hacking cyber-attacks. This article is also involved in artificial intelligence and machine learning (AI&ML) in different cyber-attacks. This article focuses on genetic algorithms to detect the cyber-attack. The main aim of the detection is future to prevent these cyber-attacks. The comparison will take two sample genetic algorithms. The first one is taken for Ant Colony Optimization (ACO), and the proposed model is taken for Particle Swarm Optimization. The average attack detection of ACO algorithm is 45 packets at the same time PSO algorithm will detect 50 packets. 2023 IEEE. -
Digital Water Dynamics: Analyzing VA Tech Wabag's Influence on India's Water Technology Landscape
This research delves into the transformative role of VA Tech Wabag in India's water technology landscape, amid the burgeoning challenges of water scarcity, pollution, and infrastructure inadequacies. Leveraging a comprehensive review of literature and fundamental analysis, the study underscores the global shift towards digitalization and sustainability in water management, situating VA Tech Wabag's initiatives at the forefront of this paradigm shift. Through innovative digital water solutions and large-scale infrastructure projects, the company has markedly enhanced water quality and availability across diverse urban and rural settings, underpinning its financial resilience and growth trajectory despite regulatory and fiscal hurdles. The discussion extrapolates the implications of these technological advancements, highlighting the company's commitment to environmental stewardship, community engagement, and the imperative for continuous innovation within a dynamic industry landscape. Conclusively, the paper affirms VA Tech Wabag's pivotal contributions to water security and resilience, advocating for future research on the scalability of digital water technologies and their long-term impacts on resource management. This study, enriched with specific data points and analyses, aims to offer a well-substantiated overview of VA Tech Wabag's influence on shaping a sustainable and efficient water technology ecosystem in India. 2024 IEEE. -
Digital Watermarking Techniques for Secure Image Distribution
In the contemporary era of digital advancements, it is of utmost importance to prioritize the establishment of robust security measures and traceability protocols for photos. This necessity arises from the inherent risk associated with the effortless diffusion of unlicensed information. Digital watermarking, which implants hidden data into digital photographs to verify their validity, is frequently used. This level emphasizes the need of safe photo distribution, digital platform problems, and unauthorized reproductions. The purpose of this research is to explain digital watermarking fundamentals. It emphasizes verification, IP protection, and digital watermarking monitoring. This research compares spatial and frequency domain watermarking approaches. Direct pixel manipulation in spatial domain techniques is vulnerable to attacks. Integrating watermarks with transform domains like Discrete Cosine Transform improves robustness in frequency domain techniques. The study also studies adaptive watermarking, which adjusts the watermark to the image's content to balance visibility and durability. The purpose of this research is to explore watermark identification methods. These methods use blind and non-blind watermarking. We discuss the security risks that might compromise watermarked photographs and the ways to reduce their likelihood. 2024 IEEE. -
Digitalization of Online Classes Among Higher Secondary Students in the Emerging Shift of Post Covid-19 (Second Wave)
The second wave of COVID-19 in India has left higher secondary school students befuddled, unhappy, and unsure about their future. During the second wave of the COVID-19 epidemic, a number of factors influence the effectiveness of online learning. Hence, the main objective of this research paper is focused on understanding the factors influencing online learning among higher secondary students. Researchers identified variables such as attitude, tools and technology, and quality of teaching and social support through extensive literature review. The research study adopted snowball sampling technique and used a survey-based online questionnaire for collecting the data; responses were obtained from 394 respondents from the state of Kerala in India. PLS-SEM was used to test the proposed hypotheses. The results of the study indicate that quality of teaching is the only factor that impacts the effectiveness of online classes among higher secondary students. Attitude, technology and tools, and social support are observed to have insignificant impact on online learning effectiveness. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Digitization of Monuments An Impact on the Tourist Experience with Special Reference to Hampi
The cultural heritage of India offers a deep examination of the country's political and historical evolution. Historical structures and monuments are among a nation's most valuable assets and a source of pride for Indian civilization. Monuments hold significant historical importance and exert a profound emotional influence on the community. Given the deterioration of culturally significant heritage monuments caused by factors such as weather, climate change, and human activity, as well as the threats these elements pose to numerous heritage sites of national and international significance, it is imperative to prioritize the recording, preservation, and conservation of these monuments. Events of cultural significance require comprehensive digital documentation and proper recording. As demonstrated by various programs and initiatives led by Prime Minister Narendra Modi, the government is committed to enhancing the visitor experience at monuments and museums. The primary aim of the current study is to better understand how cultural heritage sites are digitized and to assess the implications of this process for enhancing the tourist experience. To address the research objectives, a survey was conducted to analyze digital requirements. The digitization of significant cultural heritage sites is vital for the long-term sustainability of the tourism industry. Many methods will be adapted as resources permit, ensuring the industry's steady growth over time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Dimensionality reduction based on the classifier models: Performance Issues in the prediction of Lung cancer
Dimensionality reduction is an essential feature to reduce the complexity of the computations in the large data set environment. When handling large quantum of medical data set, as in the case like, Lung cancer prediction, based on symptoms and Risk factors, number of attributes/ dimensions pose a major challenge. Here in this study an attempt is made to compare the performance of the attribute selection models prior and after applying the classifier models. A total of 16 classifier models are chosen, which are based on statistical, rule based, logic based and artificial Neural network approaches. Feature set selection and ranking of attributes are done based on individual models. Confusion matrix of the models before and after dimensionality reduction is computed. Based on the confusion matrix result the models are compared and based on the performance optimal model is chosen. It is found that Multi-layer perceptron based artificial neural network model gives better performance compared to other approaches. 2012 IEEE. -
Disaster resilience of flood in Kerala, India
Kerala, the southern state in the Indian peninsula, has been affected by floods for the last three consecutive years. Changing weather patterns leading to heavy monsoon and development without considering the ecological vulnerabilities of the region has been pointed out as the reasons for flooding. Displaced communities, the destruction of agricultural and industrial enterprises, and health concerns have made disaster management a challenge for communities and governments alike. Even though there were lots of difficulties, the way Keralites came out of all these miseries and their adaptation was really inexplicable and always provided scope for research in that area. This paper focuses on examining the flooding pattern and impact of floods in Kerala, India and assessing the resilience capacity of the affected community. Self-developed questionnaires were used to gather data from the flood-affected population in the most flood-affected districts in Kerala. To gauge the respondents' opinions, the questionnaire used a five-point variable Likert scale. When all was said and done, 260 valid questionnaires were successfully retrieved. The study found that communities show resilience to flood with partnership and decentralised management of disasters. The study could help recognise the strategies for building resilient communities through policy intervention and civil society participation. Published under licence by IOP Publishing Ltd. -
Discriminative Gait Features Based on Signal Properties of Silhouette Centroids
Among the biometric recognition systems, gait recognition plays an important role due to its attractive advantages over other biometric systems. One of the crucial tasks in gait recognition research is the extraction of discriminative features. In this paper, a novel and efficient discriminative feature vector using the signal characteristics of motion of centroids across video frames is proposed. These centroid based features are obtained from the upper and lower regions of the gait silhouette frames in a gait cycle. Since gait cycle contains the sequence of motion pattern and this pattern possesses uniqueness over individuals, extracting the centroid features can better represent the dynamic variations. These variations can be viewed as a signal and therefore the signal properties obtained from the centroid features contains more discriminant information of an individual. Experiments are carried out with CASIA gait dataset B and the proposed feature achieves 97.3% of accuracy using SVM classifier. 2019, Springer Nature Singapore Pte Ltd. -
Diseased Leaf Identification Using Bag-of-Features and Sigmoidal Spider Monkey Optimization
Agricultural products decide the economy of a country like India. The agricultural business has the involvement of a large population. The quality and quantity of agricultural products highly depend on environmental conditions and facilities provided to farmers. Timely and efficient detection of diseases in plants and crops is one of the most critical issues that affect crop production. Therefore, it is highly desirable to develop some cheap and easy-to-handle automated plant disease detection systems for the timely treatment of plants. Leaves are considered a primary source of information about the health of plants. In the case of plants, the disease may be easily visualized and identified by observing its effect on leaves. Therefore, this paper introduces a bag-of-features in sigmoidal spider monkey optimization to identify a diseased leaf, separating the diseased leaf from a healthy leaf. The investigational outcomes show the superiority of the anticipated technique in contrast to other meta-heuristic-based systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Distance based properties of the semi splitting block graph of graph
The bounds on the radius and diameter of the semi splitting block graph (SB(G)) of graphs are investigated. The diametral paths and self-centeredness of semi splitting block graph of any connected graph are analyzed. The graphs where the diameter of G and SB(G) are the same are characterized and the number of blocks in the diametral path of such graphs is analyzed. 2023 Author(s). -
Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms
The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool. 2021 IEEE. -
District Level Analytical Study of Infant Malnutrition in Madhya Pradesh
One of the main causes for Indias high infant mortality rate is malnutrition. It can be addressed using three broad groups of conditions: stunting, wasting, and underweight. Other factors such as sanitation, poverty, breastfeeding also contribute to the prevalence of malnutrition. Understanding the contribution of these factors and thus, eliminating them, to reduce malnutrition, is the purpose of this study. In this chapter, the district-level data obtained through NFHS-4 is used for analytical study for infant malnutrition, in Madhya Pradesh. Hierarchical Agglomerative clustering is used to group the districts based on the factors such as exclusively breastfeeding, inoculation, breastfeeding within one hour, no inoculation. The proposed model presents the effect of each factor, on infant malnutrition. It will help decision-makers and the government to shortlist the most appropriate districts contributing to malnutrition and to take curative action to reduce the rate of infant malnutrition. It is a generic model which can be utilized by other states to study infant malnutrition. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
DKMI: Diversification ofWeb Image Search Using Knowledge Centric Machine Intelligence
Web Image Recommendation is quite important in the present-day owing to the large scale of the multimedia content on the World Wide Web (WWW) specifically images. Recommendation of the images that are highly pertinent to the query with diversified yet relevant query results is a challenge. In this paper the DKMI framework for web image recommendation has been proposed which is mainly focused on ontology alignment and knowledge pool derivation using standard crowd-sourced knowledge stores like Wikipedia and DBpedia. Apart from this the DKMI model encompasses differential classification of the same dataset using the GRU and SVM, which are two distinct differential classifiers at two different levels. GRU being a Deep Learning classifier and the SVM being a Machine Learning classifier, enhances the heterogeneity and diversity in the results. Semantic similarity computation using Cosine Similarity, PMI and SOC-PMI at several phases ensures strong relevance computation in the model. The DKMI model yields overall Precision of 97.62% with an accuracy of 98.36% along with the lowest FDR score of 0.03 and is much better than the other models that are considered to be the baseline models. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
DNA based cryptography to improve usability of authenticated access of electronic health records
The quality of health care has been drastically improved with the evolution of Internet. Electronic health records play a major role in interoperability and accessibility of patients data which helps in effective and timely treatment irrespective of the demographic area. The proposed model is to ensure and monitor maternal health during pregnancy and to create awareness alerts (options include messages, voice alerts or flash the system) based on the individual health record. The system aims to prevent maternal death due to medical negligence and helps to make recommendations to prevent future mortality based on medical history and take appropriate action. Authentication is a critical aspect considering the trade-off between usability and security whereas data breach and related cybercrime are major concerns in health care. The proposed model uses DNA based authentication techniques to ensure usability and confidentiality of electronic data, Aadhaar to prevent unauthorized access to patients data in case of emergency without affecting availability. 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. -
DNA for information security: A Survey on DNA computing and a pseudo DNA method based on central dogma of molecular biology
Biology is a life science which has high significance on the quality of life and information security is that aspect for social edification, which human beings will never compromise. Both are subjects of high relevance and inevitable for mankind. So, an amalgamation of these subjects definitely turns up as utility technology, either for security or data storage and is known as Bio computing. The secure transfer of information was a major concern from ancient civilizations. Various techniques have been proposed to maintain security of data so that only intended recipient should be able to receive the message other than the sender. These practices became more significant with the introduction of the Internet. Information varies from big data to a particular word, but every piece of information requires proper storage and protection which is a major concern. Cryptography is an art or science of secrecy which protects information from unauthorized access. Various techniques evolved through years for information protection, including Ciphers, Cryptography, Steganography, Biometrics and recent DNA for security.DNA cryptography was a major breakthrough in the field of security which uses Bio-molecular concepts and gives us a new hope of unbreakable algorithms. This paper discusses various DNA based Cryptographic methods proposed till now. It also proposes a DNA symmetric algorithm based on the Pseudo DNA Cryptography and Central dogma of molecular biology. The suggested algorithm uses splicing and padding techniques along with complementary rules which make the algorithm more secure as it is an additional layer of security than conventional cryptographic techniques. 2014 IEEE. -
Do Millennial Exhibit Environmentally Responsive Consumption BehaviorsA Study on Determinants of Green Purchase Decision?
The purchase behavior of green products is largely affected by the intention-action gap and skepticism present among consumers. The purpose of this study was to analyze the various factors that affect the purchase behavior of green products among millennials. The practical benefit of this research is that it will assist in the convergence of green marketing and environmental consumer behavior theories. The theory used in the study is the theory of planned behavior. It helps to understand the specific behaviors of consumers as a possibility of a particular behavioral intention. For this purpose, we identified five constructs, namely, Environmental Concerns and Belief (ECB), Eco-Labelling (EL), Green Packaging and Branding (GPB), Green Product, Premium, and Pricing (GPPP), and Consumers Beliefs Towards the Environment (CBTE). These constructs have helped in identifying and analyzing the various factors that affect the purchase behavior of green products among millennials. We analyzed the purchase behavior of green products using a questionnaire approach. For this descriptive study, there were 251 millennials as our respondents who were chosen using the convenience sampling technique. The data was collected through a structured questionnaire via Google form and was analyzed using regression analysis, correlation. It was found that the key factors of green marketing such as Environmental Concerns and Beliefs (ECB), Green Packaging and Branding (GPB), and Green Product, Premium and Pricing (GPPP) have a positive influence on Consumers Beliefs Towards the Environment (CBTE). It implies that by increasing the spending on green packaging and branding there will be a positive effect on consumers environmental beliefs. On the other hand, Eco-Labelling (EL) has a negative influence on Consumers Beliefs Towards the Environment (CBTE) and this is caused by skepticism present among millennials. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Document Classification for Recommender Systems Using Graph Convolutional Networks
Graph based recommender systems have time and time again proven their efficacy in the recommendation of scientific articles. But it is not without its challenges, one of the major ones being that these models consider the network for recommending while the class and domain of the article go unnoticed. The networks that embed the metadata and the network have highly scalable issues. Hence the identification of an architecture that is scalable and which operates directly on the graph structure is crucial to its amelioration. This study analyses the accuracy and efficiency of the Graph Convolutional Networks (GCN) on Cora Dataset in classifying the articles based on the citations and class of the article. It aims to show that GCN based networks provide a remarkable accuracy in classifying the articles. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Does Packaging Elements Affects Consumers Preference During The Purchase Of Chocolate?
Chocolate is one of the highest consumed products and the packaging of such a product is important. The primary goal of the study is to understand if the packaging of chocolate has an impact on the consumer's preference during the purchase of chocolate. The researcher concentrates on the elements of packaging which are the color of the packaging, shape, and size of the packaging, labeling information on the packaging, and the material of packaging. The study helps the producer to understand what factors on the packaging impacts the customer during the purchase of chocolate. The researcher concentrates on how these elements of packaging play a role in affecting the consumer at the point of purchase of chocolate. Through this study one will be able to deliver the product i.e., chocolate more efficiently and effectively way to the consumers or the buyers. The Electrochemical Society -
Domain-Driven Summarization: Models for Diverse Content Realms
In todays information-rich landscape, automatic text summarization systems are pivotal in condensing extensive textual content into concise and informative summaries. The current study ventures into domain-agnostic summarization, delving into advanced models spanning various domains, such as business, entertainment, sports, politics, and technology. The study aims to uncover domain-specific enhancements, assess resource efficiency, and explore the boundaries of applicability. This study covers nine cutting-edge models, including Google Pegasus-Large, Facebook BART-Base, SSHLEIFER DistilBART-CNN-6-6, Facebook BART-Large, T5-Large, T5-Base, Facebook BART-Large-CNN, Facebook BART-Large-Xsum, and SSHLEIFER DistilBART-Xsum-12-1. Each model undergoes rigorous evaluation, revealing its efficacy within various domains. Google Pegasus-Large emerges as a standout choice for cross-domain summarization, while Facebook BART-Base demonstrates remarkable stability. Models like SSHLEIFER DistilBART-CNN-6-6, T5 variants, and others contribute to the evolving landscape of summarization. This study endeavors to establish a robust foundation for enhancing the efficiency and effectiveness of summarization techniques within various domains, thereby contributing valuable insights to the broader literature on text summarization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.