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A data/document management system and method /
Patent Number: 202111047326, Applicant: Anil Kumar.
A data/document management system and method comprising a user device (101); internet (102); a server (103); an authentication module (104); a code generation module (105); record management system (106); a processing module (107); a database (108). The method comprises following steps of A) Data /document storing mode and B) data / document fetching mode. The invention provides a high efficient and low, user friendly data / document management system and method. The invention provides a record management system (106) which is connected to the database system (108). -
A decade of climate change concern in India: Determinants of personal and societal climate concern
Scientists have called for a culturally relevant investigation of factors impacting public climate concern to devise relevant behavioural and policy interventions. Although India will be adversely affected by climate change, there is a shortage of models that track changes in Indian climate concern across time. The study tracked the growth of climate concern from 2006 to 2020 and identifies determinants of personal and societal climate concern. Secondary analyses of survey data from the International Science Survey and World Values Survey (2006-2020, N = 9254), were conducted to predict climate concern across the year, environmental protection versus economic growth preferences, and socio-demographic variables. Within responses from 2020 (N = 3176), the predictive role of anthropogenic climate change beliefs, trust in scientists, adequate government action, collective efficacy, environmental protection preferences, and sociodemographic variables were evaluated to understand personal and societal climate concern. Binary logistic regression found that climate concern increased significantly from 2006 (2.6%) to 2020 (89.5%) and was predicted by education and preferences for environmental protection. Multiple regression results identified personal climate concern as predicted by education, anthropogenic climate change beliefs, trust in scientists, and environmental protection preferences; while government action beliefs and favouring left-wing affiliation predicted societal climate concern. There was mixed support for the political polarization of climate concern. The study shows an increase in Indian climate change concern over the past decade, with personal and societal climate concern being influenced by different psychological characteristics. Important implications for future climate communication research and social policy development are discussed. 2024 by author(s). -
A decade survey on internet of things in agriculture
The Internet of Things (IoT) is a united system comprising of physical devices, mechanical and digital machines, and different hardware components like sensors, actuators, cameras etc., monitored and operated by the software. The combination of devices and systems connected over the internet opens the pathway for development of various applications beneficial in terms of economic growth of a nation. IoT has evolved as a potentially emerging computer technology solving various real-life problems and issues. IoT covers vast group of applications, from warfare to surveillance, from habitat monitoring to energy harnessing, predictive analytics and personalized health care, and so on. Among various fields, agriculture is one important field having maximum scope of implementation and investment. The main aim of this book chapter is to furnish all the details related to applications of IoT in the field of agriculture. This includes the details related to data collection, types of sensors used, deployment details, data access through cloud. It also covers details related to various communication technologies used in IoT such as Bluetooth, LoRaWAN, LTE, 6LowPAN, NFC, RFID etc. And above all, the chapter focuses on the significance of IoT on agronomics, agricultural engineering, crop production and livestock production. This chapter is a decade survey conducted to study the contribution of IoT in the field of agriculture. Around 40 research papers for the duration 2008-2018 are collected from peer reviewed journals and conferences. The collected articles are analyzed to provide relevant information required for the various end users. Springer Nature Switzerland AG 2020. -
A Deep Assessment of ML Based Procedure used as a Classifiers in the Clinical Field
In the unexpectedly evolving panorama of healthcare technology, the mixing of data mining and machine mastering gives exceptional possibilities for the advancement of sickness prediction fashions. This research paper introduces a unique Machine Learning Smart Health Procedure designed to harness the predictive energy of those era for forecasting illnesses. By meticulously reading ancient healthcare facts, which includes affected individual signs and symptoms and effects, this system leverages cutting-edge algorithms which includes Nae Bayes, Support Vector Machines (SVM), and neural networks to expect capacity health problems with accelerated accuracy. This method now not best pursuits to facilitate early and specific evaluation but also strives to noticeably enhance affected individual care and treatment consequences. Through the strategic utility of statistics mining and prediction analysis in the healthcare area, our proposed machine demonstrates the capacity to revolutionize conventional diagnostic techniques, developing a proactive and predictive healthcare model more plausible and effective than ever earlier than. 2024 IEEE. -
A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data
Several economists and social scientists have held a longstanding fascination with the practice of stock market prediction. As the stock market is essentially uncontrollable chaos, many experts believe that trying to predict it is futile. Due to the complexity of the numerous factors, accurate stock price predictions are notoriously difficult to achieve. While the market behaves more like a scale than a voting machine over the long run, its behavior may be predicted with some certainty. Information from Twitter is used into the algorithm. In this proposed method, a convolutional extreme learning machine model with kernel support was introduced (CKELM). To improve feature extraction and data classification, the CKELM model builds on the KELM's hidden layer by adding convolutional and subsampling layers. The convolutional layer and the subsampling layer do not employ the gradient technique to fine-tune their parameters because some designs worked well with random weights. When compared to popular models like CNN and KELM, The proposed model fares quite well, with an accuracy of around 98.3 percent. 2023 IEEE. -
A Deep Ensemble Framework for DDoS Attack Recognition and Mitigation in Cloud SDN Environment
Much research has been done in the recent past on the absolute shift of Internet infrastructure in order to make it more significantly programmable, configurable and make it more conveniently feasible. Software Defined Networking (SDN) forms the basis for this absolute shift in Internet infrastructure. When you look at the benefits of an SDN-based cloud environment they are monumental. Namely, network traffic control and elastic resource management. The SDN-based cloud environment becomes susceptible to cyber threats, especially like that of Distributed Denial of Service (DDoS) attacks and other cyber-attacks that perturb the SDN-based cloud environment. Hence, automated Machine Learning (ML) models are an efficient way to protect against these cyber-attacks. This research will develop a deep learning-based ensemble model for DDoS attack detection and classification (DLEM-DDoS) in a cloud environment. Long Short-Term Memory (LSTM), 1-D Convolutional Neural Networks (1D-CNN) and Gated Recurrent Unit (GRU) are the three DL models integrated into an ensemble model that classifies the incoming packet by majority voting classifiers. Network traffic data including source and destination IP addresses, packet and byte counts, packet and byte rates, flow duration, protocol types and port numbers are fed into the DLEM-DDoS model. This model preprocesses this data by converting categorical values (like protocol types) into numerical values and removing any missing values. Once collected and preprocessed, the data is fed into deep learning models (LSTM, 1D-CNN, GRU) within the framework for analysis. Finally, in this research using the DLEM-DDoS technique an efficient DDoS attack mitigation scheme in an SDN-based cloud environment is demonstrated. The report shows comprehensive stimulations as well as a superiority into the current approaches in terms of several measures. 2024 S. Annie Christila and R. Sivakumar. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
A deep learning approach in early prediction of lungs cancer from the 2d image scan with gini index
Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
A Deep Learning Method for Autism Spectrum Disorder
The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Deep Learning Method for Classification in Brain-Computer Interface
Neural activity is the controlling signal used in enabling BCI to have direct communication with a computer. An array of EEG signals aid in the selection of the neural signal. The feature extractors and classifiers have a specific pattern of EEG control for a given BCI protocol, which is tailor-made and limited to that specific signal. Although a single protocol is applied in the deep neural networks used in EEG-based brain-computer interfaces, which are being used in the feature extraction and classification of speech recognition and computer vision, it is unclear how these architectures find themselves generalized in other area and prototypes. The deep learning approach used in transferring knowledge acquired from the source tasks to the target tasks is called transfer learning. Conventional machine learning algorithms have been surpassed by deep neural networks while solving problems concerning the real world. However, the best deep neural networks were identified by considering the knowledge of the problem domain. A significant amount of time and computational resources have to be spent to validate this approach. This work presents a deep learning neural network architecture based on Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and inception network methods. Experimental results show that the proposed method achieves better performance than other methods. 2023 IEEE. -
A Deep Learning Methodology CNN-ADAM for the Prediction of PCOS from Text Report
Text categorization is a popular piece of work in natural language processing (NLP) and machine learning, and Convolutional Neural Networks (CNNs) can be used effectively for this purpose. Although CNNs are traditionally associated with computer vision tasks, they have been adapted and applied successfully to text classification problems. In the proposed study Convolutional Neural Networks (CNNs) with adam optimization algorithm plays a crucial role in detecting PCOS words from sonographic text reports. 2023 IEEE. -
A Deep Learning Model for Information Loss Prevention from Multi-Page Digital Documents
World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers' sensitive information of a reputed TI company securing it's one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization's information security monitoring team. 2013 IEEE. -
A Design of Agricultural Robotics for the use of Sowing and Planting
Agricultural robots is always getting better to deal with problems like population growth, fast urbanization, fierce competition for high-quality goods, worries about protecting the environment, and a lack of skilled workers. This in-depth study looks at the main uses of farming robotic systems, covering jobs like preparing the land, sowing, planting, treating plants, gathering, estimating yields, and phenotyping. Each robot is judged on how it moves, what it will be used for, whether it has sensors, a robotic arm, or a computer vision program, as well as its development stage and where it came from. The study finds trends, possible problems, and things that stop business growth by looking at these shared traits. It also shows which countries are putting money into studying and developing (R&D) for these products. The study points out four important areas - movement systems as a whole sensor, computer vision computer programs, and communication technologies - that need more research to make smart agriculture better. The results make it clear that spending money on farming robotic systems can pay off in the long run by helping with things like accurate yield estimates and short-term benefits like keeping an eye on the harvest. 2024 IEEE. -
A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
A device for caregiver wellbeing assessment and a method thereof /
Patent Number: 202111033343, Applicant: Dr. Ruchi Tyagi.A system and a method for wellbeing assessment to assess psychological and mental needs of caregivers. The method comprising the steps of identifying categories of psychological need, wherein said categories comprises competence, results doubting, self-esteem and fears of failures, criticism, and expectations; plotting category theme on the basis of the identified categories; determining factors affecting the psychological needs of caregivers in COVID 19 situation on the basis of the plotted category theme, where said factors comprise depression, anxiety and/or stress assessment. -
A diet and workout recommendstion system using improved conditional restricted boltzmann machines (CRBM) and method thereof /
Patent Number: 202141024082, Applicant: Dr. Vaishali M Deshmukh.
The system includes, but not limited to, one or more processor provided in a computer network; and a memory disposed in communication with each of the processor and storing processor executable instructions, the instructions comprising instructions to: process varied datasets of Food items and various nutrient parameters of Food items with respect to their ratings by various food takers and while recommending for a patient or a target user. -
A diet and workout recommendstion system using improved conditional restricted boltzmann machines (CRBM) and method thereof /
Patent Number: 202141024082, Applicant: Dr. Vaishali M Deshmukh.
The system includes, but not limited to, one or more processor provided in a computer network; and a memory disposed in communication with each of the processor and storing processor executable instructions, the instructions comprising instructions to: process varied datasets of Food items and various nutrient parameters of Food items with respect to their ratings by various food takers and while recommending for a patient or a target user. -
A Different Humour: A Quantitative and Qualitative Analysis of the Nature of Participation of Select Indian Female Stand-Up Comedians on YouTube
In recent times, Stand-up Comedy space in India has been registering itself as an alternative public sphere. As comedy has always been understood to be a masculine domain in any given society, it becomes imperative to examine if this aspect of the ????implied??? public sphere of the Stand-up Comedy space changes the dimension of comedy. This dissertation studied the nature of participation of both the male and female stand-up comics on YouTube using descriptive surveys which reported the default nature of the stand-up comedy space in India. Furthermore, the thesis studied the implications of the performances by select few female comics with specific reference to the audience reception of their comedic routines. In addition, this dissertation studied how female stand-up comics negotiate their citizenship and gender in a stand-up comedy space in India. Thematic and Critical Discourse Analysis were used to examine the theme, style, and nature of humour pervasive throughout their comedic routines. The style of humour presentation by the select female comics included subversion which was articulated in varied ways, often by marking it explicitly through themes, narratives and humour, and in other times, using it covertly. The thesis explored the dimensions of ???unladylike??? or ???unfunny??? which were used as markers to identify their routines by both themselves and their YouTube audience/commentators, the thesis also attempted to explain how these comedians found a balance between ????doing gender??? and ????undoing gender??? in these comedy spaces. The thesis concluded with an argument that the stand-up comedy space as negotiated by the select comics provides us a glimpse of an emergent feminist public sphere. -
A discourse analysis on spiritual text of the weekly supplement "The Speaking Tree" by the Times of India /
This research looks at the creative ways by the Times Group in reviving the dying medium ‘print’ through its weekly supplement ‘the speaking tree’. The goal is to show the relevance of spirituality in the modern society and to educate them on necessity of spirituality in the contemporary times. The researcher has analysed some articles by the spirituality experts published in the supplement vis-a-vis discourse analysis and symbols vis-a-vis semiotics. -
A Discrete Kumaraswamy Marshall-Olkin Exponential Distribution
Finding new families of distributions has become a popular tool in statistical research. In this article, we introduce a new flexible four-parameter discrete model based on the Marshall-Olkin approach, namely, the discrete Kumaraswamy MarshallOlkin exponential distribution. The proposed distribution can be viewed as another generalization of the geometric distribution and enfolds some important distributions as special cases. Some properties of the new distribution are derived. The model parameters are estimated by the maximum likelihood method, with validation through a complete simulation study. The usefulness of the new model is illustrated via counttype real data sets. 2022. Journal of the Iranian Statistical Society. All Rights Reserved. -
A distinctive symmetric analyzation of improving air quality using multi-criteria decision making method under uncertainty conditions
This world has a wide range of technologies and possibilities that are available to control air pollution. Still, finding the best solution to control the contamination of the air without having any impact on humans is a complicated task. This proposal helps to improve the air quality using the multi-criteria decision making method. The decision to improve air quality is a challenging problem with todays technology and environmental development level. The multi-criteria decision making method is quite often faced with conditions of uncertainty, which can be tackled by employing fuzzy set theory. In this paper, based on an objective weighting method (CCSD), we explore the improved fuzzy MULTIMOORA approach. We use the classical Interval-Valued Triangular Fuzzy Numbers (IVTFNs), viz. the symmetric lower and upper triangular numbers, as the basis. The triangular fuzzy number is identified by the triplets; the lowest, the most promising, and the highest possible values, symmetric with respect to the most promising value. When the lower and upper membership functions are equated to one, we get the normalized interval-valued triangular fuzzy numbers, which consist of symmetric intervals. We evaluate five alternatives among the four criteria using an improved MULTIMOORA method and select the best method for improving air quality in Tamil Nadu, India. Finally, a numerical example is illustrated to show the efficiency of the proposed method. 2020, MDPI AG. All rights reserved.