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A Novel Framework for Harnessing AI for Evidence-Based Policymaking in E-Governance Using Smart Contracts
Harnessing AI for evidence-based policymaking in e-governance has the potential to revolutionize the way governments formulate and implement policies. By leveraging AI technologies, governments can analyze vast amounts of data, extract valuable insights, and make informed decisions based on evidence. This chapter explores the various ways in which AI can be employed in e-governance to facilitate evidence-based policymaking. It discusses the use of AI algorithms for data analysis and prediction, enabling governments to identify patterns, trends, and emerging issues from diverse data sources. Moreover, AI-powered tools can enhance citizen engagement and participation, by facilitating data-driven decision-making processes and providing personalized services. Additionally, AI can assist in policy evaluation and impact assessment, by automating the collection and analysis of data, thus enabling governments to measure the effectiveness of their policies in real-time. Furthermore, AI can contribute to enhancing transparency and accountability in e-governance, by automating processes such as fraud detection and risk assessment. Despite the immense potential, the adoption of AI in e-governance must address challenges such as data privacy, algorithmic bias, and ethical considerations. This chapter concludes by emphasizing the importance of building trust, ensuring fairness, and promoting responsible AI practices to maximize the benefits of AI in evidence-based policymaking for e-governance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
A Comparative Analysis of LSB & DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges
In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches'current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance. 2023 IEEE. -
Pandemic Pulse: Unveiling Insights with the Global Health Tracker Through AI and ML
The current study highlights the importance of data analysis by applying data visualization tools to help you understand the pandemic disease informational component, and how it can be converted into knowledge that might enhance decision-making processes. In Tableau, a software for displaying data, researchers have incorporated a pandemic disease informational component from Coursera to improve assessment and selection. After becoming familiar with the data and the data visualization technological advances, some of it will be expected to conduct an initial investigation to identify significant changes in the data that is under consideration, compile and present this pandemic disease informational component, and enhance the corporate decision-making process. This issue for inquiry highlights the significance of knowledge examination via the use of communication visualization applications to aid in your comprehension of the pandemic disease informational component as well as how it may be changed into knowledge that may enhance the process of arriving at decisions. The creators of the knowledge representation computation application scenario used data from Coursera to improve their studies and make decisions. One will need to conduct an exploratory inquiry to find notable trends within the data after familiarizing oneself with it by utilizing visualization programs to compile and distribute this data to improve the company's decision-making procedures. This specific software is designed to be utilized in an early administrative duties course, an undergraduate accounting data structure course, or a data analytics-only educational program as a basic introduction to an informative visualization computer application. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An enhanced framework to design intelligent course advisory systems using learning analytics
Education for a person plays an anchor role in shaping an individuals career. In order to achieve success in the academic path, care should be taken in choosing an appropriate course for the learners. This research work is based on the framework to design a course advisory system in an efficient way. The design approach is based on overlapping of learning analytics, academic analytics, and personalized systems. This approach provides an efficient way to build course advisory system. Also, mapping of course advisory systems into the reference model of learning analytics is discussed in this paper. Course advisory system is considered as enhanced personalized system. The challenges involved in the implementation of course advisory system is also elaborated in this paper. Springer Science+Business Media Singapore 2017. -
Phonon limited diffusion thermopower in phosphorene
A theoretical investigation of diffusion thermopower, Sd, of phosphorene employing Boltzmann transport formalism is presented. We assume carriers in phosphorene to be scattered by in-plane single and flexural two-phonon processes via deformation potential coupling. Our calculations of Sd in phosphorene show that, at low temperatures (T?< 20 K) Sd increases linearly with temperature and for the range of temperatures considered single phonon contribution to Sd dominates. As function of carrier concentration, ns, considered (1016?1018 m-2), at T = 300K, Sd decreases from 189?V/K to 9.9 ?V/K. 2017 Author(s). -
Classification of Hypothyroid Disorder using Optimized SVM Method
Hypothyroidism is an endocrine disorder where the thyroid organ doesn't provide the enough amount of thyroid hormones. It is one of the common diseases found in women. Detection of hypothyroidism needs suitable diagnostic tests to encourage prompt analysis and medication. Accurate and early detection of a disease is more important and compulsory in healthcare domain to facilitate correct and prompt diagnosis and timely treatment. The information generated in healthcare domain is on large scale, crucial and complex with multiple parameters. To interpret and understand such a huge data and retrieve the accurate and relevant information from it is a tedious as well as challenging task. However, there is a need and importance to facilitate the patients with better medical solutions. This will help to reduce the cost, time and give more relief to users by applying advanced and upgraded knowledge. It will also assist to prevent the further complications. The proposed study gains the knowledge from the hypothyroid dataset to predict the level of disease. To identify the level of hypothyroid disorder, we used four classification machine learning techniques, namely KNN (K-Nearest Neighbour), SVM (Support Vector Machines), LR (Logistic Regression) and NN (Artificial Neural Network). The Experimental results compared the classification accuracy of four methods. Logistic Regression method achieved 96.08% accuracy among other three classifiers. But, SVM is found the best classifier after standardizing the data and parameter tuning with accuracy of 99.08%. 2019 IEEE. -
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 -
Sensitivity and tolerance analysis of 2D Profilometer for TMT primary mirror segments
The primary mirror (M1) of Thirty Meter Telescope (TMT) consists of 492 segments of which, 86 are ground and polished by India-TMT. These segments are off-Axis and aspheric in nature and one of the effective methods to polish such segments is through Stressed Mirror Polishing (SMP). During SMP, consistent in-situ metrology of the surface is needed to achieve the required profile. A 2D Profilometer (2DP) will be used by India-TMT for the low frequency profile metrology. The 2DP is a contact-Approach metrology, consisting of probes positioned in a spiral pattern, measuring the sag of segment surface. Initial section of this paper deals with the sensitivity and tolerance analysis of the 2DP. This is followed by the study on position and rotational errors of the 2DP as a whole. Simulation of these analysis is carried out initially on a sphere and then on different segments of the TMT, in order to study the induced measurement errors. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
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. -
An Intelligent System to Forecast COVID-19 Pandemic using Hybrid Neural Network
A current outbreak known as COVID-19 has been discovered from the coronavirus was informed by WHO. COVID-19 is a universal pandemic that has brought out the best and the worst of humanity. Due to an increase in the cases daily, COVID-19 is creating a menace to public health and establishes a disruption of the social and economic development of the countries. The problem is the hospitals are not able to provide proper facilities and treatments on time due to the lack of facilities in India. The purpose of this project to build an efficient hybrid deep learning model for forecasting the COVID-19 pandemic with multiple features that are responsible for the spread of COVID-19 in the top five states in India. In particular, a hybrid model that incorporates Auto-Regressive Integrated Moving Average and Long-term Short Memory is been used to forecast confirmed cases. The linear and non-linear dependencies in the dataset is been dealt with by an ARIMA-LSTM hybrid model. As a result, when compared to the outcomes of ARIMA, LSTM models independently, the hybrid model was giving better results and was performing well in forecasting COVID-19 cases. Through this, the policymakers will get prior information on COVID-19 cases in states which will help the government and healthcare departments to take prominent measures to prevent it. 2021 IEEE. -
Harnessing Machine Learning for Mental Health: A Study on Classifying Depression-Related Social Media Posts
This study is of particular relevance in the way it identifies depression-related content on social media using a machine learning model to classify posts and comments. This dataset, encompassing around 6500 entries from various platforms including Facebook, was rigorously annotated by four proficient English-speaking undergraduate students together with the final label which is established via majority voting. Data Preprocessing, initial cleaning, normalization and TF-IDF feature creation through vectorization for the output of POS tags. The different machine learning models that were trained and tested are Logistic Regression, Random Forest, SVM (Support Vector Machine), Naive Bayes Gradient Boosting Algorithm K-NN (K nearest Neighbors) AdaBoost Decision Tree. Authors evaluated the models and measured their accuracy, precision score, recall rate (also known as sensitivity) in addition to F1-score. Gradient Boost, Random Forest, and SVM were top performers among which Gradient boosting was found to be an overall best one with almost 98.5%. They show that machine learning model can successfully predict the label of social media posts, as a way for accurately identifying depression from text data. This detailed model performance evaluation is useful in understanding what each approach does well and poorly, shedding light into whether they are / would be actually suitable for real-world applications. This study not only developed discriminative classifiers, but also included detailed analysis of their performance which should hopefully guide future work and help in practical implementations for real-time mental health monitoring. Through this work, this study aim to facilitate timely identification of depression-related posts, ultimately supporting mental health awareness and intervention efforts on social media platforms. 2024 IEEE. -
Optimal location and parameters of GUPFC for transmission loss minimization using PSO algorithm
Transmission losses are one of the major losses faced by our power system. Reduction of transmission losses will benefit us by saving a large amount of power. The transmission losses can be reduced by placing FACTS devices in the power system. Among all the FACTS devices Unified Power Flow Controller (UPFC) and Generalized Unified Power Flow Controller (GUPFC) are the best. Incorporating the GUPFC in to the power system and placing it to the optimal location and setting its output to optimal values can reduce the transmission losses. This paper explains the way to locate the optimal location of GUPFC and finding the optimal setting using PSO algorithm to reduce the total transmission losses. Voltage variation is taken as the criteria for finding the location and PSO is used for finding the settings of GUPFC. This study is conducted on an IEEE 14-bus system using MATLAB software. 2017 IEEE. -
Protection Against SIM Swap Attacks on OTP System
One-time password-based authentication stands out to be the most effective in the cluster of password-less authentication systems. It is possible to use it as an authentication factor for login rather than an account recovery mechanism. Recent studies show that attacks like SIM swap and device theft raise a significant threat for the system. In this paper, a new security system is proposed to prevent attacks like SIM swap on OTP systems, the system contains a risk engine made up of supervised and unsupervised machine learning model blocks trained using genuine user data space, and the final decision of the system is subject to a decision block that works on the principles of voting and logic of an AND gate. The proposed system performed well in detecting fraud users, proving the systems significance in solving the problems faced by an OTP system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cryptocurrency Security and Privacy Issues: A Research Perspective
Cryptocurrency has developed as a new mode of money exchange since it has become easier, faster and safer. The first cryptocurrency was introduced in 2009 and since then, the growth rate of cryptocurrency has been increasing drastically. As of 2020, the cryptocurrency exchange all over the world has exceeded 300%. The researchers face many challenges during their research on the various cryptocurrencies. For example, most of the high-tech companies still do not support bitcoin on mobile platforms. High-tech companies like Google and Apple are also thinking into banning the bitcoin wallet from their app stores. The work provides a review of cryptocurrency and its types, scope on the investment plans and its advantages also discussed. The growth and comparison between bitcoins and gold is also discussed. The challenges researchers face and the security issues concerning it. This review provides an overview of how the different forms of cryptocurrency are increasing from over a decade. It explains the different types and the year in which they were invented. It also gives a brief comparison with respect to bitcoin, which is one of the most used cryptocurrency. Furthermore, it gives a brief explanation on investments, and schemes for those who are new in the cryptomarket. Later emphasizes on the security issues faced by this technology. It talks about proof of work and the different data attacks the software faced and how the issues were overcome. In the end, it talks about the challenges researchers face while researching cryptocurrency. 2021 IEEE. -
Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review
Nuclear atypia identification is an important stage in pathology procedures for breast cancer diagnosis and prognosis. The introduction of image processing techniques to automate nuclear atypia identification has made the very tedious, error-prone, and time-consuming procedure of manually observing stained histopathological slides much easier. In the last decade, several solutions for resolving this problem have emerged in the literature, and they have shown positive incremental advancements in this fieldof study. The nuclear atypia count is an important measure to consider when assessing breast cancer. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and future prospects for this critical undertaking, which will aid humanity in the fight against cancer. In this study, we examine the various techniques applied in detecting nuclear atypiain breast cancer as well as the major hurdles that must be overcome and the use of benchmark datasets in this domain. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and prospects for this critical undertaking, which will aid humanity in the fight against cancer. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Revisiting psychotherapeutic practices in Karnataka, India: Lessons from indigenous healing methods
Psychotherapeutic practices in India observes a paradigm shift with the current focus on the indigenous movement which has hit the discipline of Psychology like any other stream in Social Sciences and Humanities. The professional challenges and issues faced by the mental health professionals in this country has always revolved around on the 'uncanny' realm of myths, beliefs and religions as far as mental illness is concerned (Prasadarao & Sudhir, 2001). Efforts have been initiated in exploring the cultural and social roots of the health-illness constructs as well as debating on the possibility of 'integration' of these different philosophies. This paper is designed to understand the various therapeutic forms and processes in indigenous healing practices and to analyse the negotiation between indigenous healing practices and psychotherapy with special reference to Karnataka, one of the States situated in the Southern part of India. The study approaches the cultural landscape of Karnataka state based on a qualitative research design wherein in-depth unstructured interview of healers and mental health practitioners and systematic observation of some indigenous healing forms are adopted as methods of data collection. The paper concludes by looking at the challenges of constructing ethnospecific interventions in psychotherapy and the need to develop more cultural-specific theories taking into account the cultural history of the community. -
A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models
Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE. -
The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset
With the advent of data communication the increased usage of the technologies results in network intrusions and associated attacks. Consequently, the data violation rates are increased abundantly and that sacrifices Confidentiality, Integrity and Availability. This article focused on the network Intrusion Detection System (IDS) that detects various attacks and types. Machine learning (ML) has the potential to spot known-experience and Zero-day attacks. Consequently, the article has considered ML and ensembled models for the various attack classification. The major contributions of the current article are 3-fold. Initially, to understand the relevance and sufficiency of the dataset through exploratory data analysis. Second, the comprehensive understanding of the various attacks, its nature, various types and classifications and finally, the empirical analysis of the dataset through the potential of various ML models. The article utilized various discriminative models for the execution and all of the models have shown better accuracy. The tree-based ensemble model, Random Forest has outperformed the rest of the models with higher accuracy in the training and testing samples of 99.997 % and 99.969 % respectively. 2023 IEEE. -
Design of Reconfigurable Filter Structure Based on FRM for Wideband Channelizer?
A reconfigurable FIR bank of filters are essential for digital channelizer in wideband system. FRM is a extensively used method to generate a sharp transition width sub-bands or channels for digital channelizer. The aim of this work is to design multiple non-uniform sharp transition width FIR bank of filters with reduced number of multipliers and group delay for wideband channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on FRM filters and exponential modulation (EM) technique. The performance of the proposed structure is illustrated with the help of an example. Result shows that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE. -
Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks
A low computational complexity FIR bank of filters are essential for spectrum sensing in wireless networks. FRM is a widely used method to generate a sharp transition width sub-bands or channels. The intention of this work is to design multiple non-uniform sharp transition width FIR bank of filter with low computational complexity for spectrum sensing in cognitive radio for IoT networks. The design parameters of the proposed structure are calculated in an efficient way. The proposed structure is designed based on the FRM filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is illustrated with the help of an example. Result indicates that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE.