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Sustainable Development- Adopting a Balanced Approach between Development and Development Induced Changes
The emergence of globalization has raised serious concerns and promoted ever increasing dichotomy between development and climate change issues that are borne out of such trade development plans and strategies. The sustainable development goals embark on 17 broad categories that calls upon the nations to achieve them by 2030. In order to achieve these goals the need of the hour is to make a paradigm shift from traditional trade agreements and policies to such agreements that aim to prioritize the attainment of these goals. There has to be uniform environment laws to promote intergeneration and intra-generational equity among the nations and leaving no lacuna for geographical disparity. The Artificial Intelligence can play a pivotal role in achieving sustainability by cross fertilization of technology and sustainable development leading to smart states, effective utilization of resources, green investment policies, use of eff icacious renewable energy resources, analyzing agricultural needs and prior analyses of prospective disasters. The Electrochemical Society -
Enhancing Cybersecurity: Machine Learning Techniques for Phishing URL Detection
Phishing attacks exploit user vulnerabilities in cybersecurity awareness by tricking them to fake websites designed to steal confidential data. This study proposes a method for detecting phishing URLs using machine learning. The proposed method analyzes various URL characteristics, such as length, subdomain levels, and the presence of suspicious patterns, which are key indicators of phishing attempts. Gradient Boosting was selected due to its robustness in handling complex, non-linear relationships between features, making it particularly effective in distinguishing between legitimate and phishing URLs, by evaluating the Gradient Boosting classifier on a dataset with 10,000 entries and 50 features, the method achieves an accuracy of 99%.This approach has the potential to enhance web browsers with add-ons or middleware that alert users from potential phishing sites which will be based solely on URL. 2024 IEEE. -
Precision Corn Price Prediction with Advanced ML Techniques
In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE. -
Defluoridation of Drinking WaterFluoride Wars
Fluorine is also known as two-edged sword. At lower doses, it influences tooth by inhibiting tooth caries, while in high doses, it causes dental and skeletal fluorosis. It is known that some quantity of fluoride is important for the formation of tooth enamel and mineralization in tissues. The present work aims at providing safe and potable water to rural areas where this element has created a menace. This work also suggests the use of few adsorbents such as paddy husk and coir pith which are affordable and removes fluorine to greater extent. The study concludes that materials which are used as adsorbents and can be safely inculcated as fluorine removal adsorbents which help people to have safe potable water. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Identifying a Range of Important Issues to Improve Crop Production
Crop yield production value update has a beneficial practical impact on directing agricultural production and informing farmers of changes in crop market prices. The main objective of the suggested method is to put the crop selection technique into practise so that it may be used to address a variety of issues facing farmers and the agricultural industry. As a result, the yield rate of crop production is maximised, which benefits our Indian economy. land conditions of several kinds. So, using a ranking system, the quality of the crops are determined. This procedure also alerts farmers to the rate of crops of low and high quality. Due to the use of multiple classifiers, using an ensemble of classifiers paves the way for better prediction decisions. The decision-making process for selecting the output of the classifiers also incorporates a rating system. The price of a crop that will produce more is predicted using this method. 2023 IEEE. -
Smart Online Oxygen Supply Management though Internet of Things (IoT)
We are surrounded by oxygen in the air we We cannot even exist without the ability to breathe. The need for oxygen has increased during the COVID19 pandemic, and although there is enough oxygen in our country, the main issue is getting it to hospitals or those in need on time. This is simply due to a significant communication gap between suppliers and hospitals, so we plan to implement an idea that will close this gap using real-time tracking as we can track the movement of oxygen tankers by gathering the requirements. We are using an ESP32 Wi-Fi module, a MEMS pressure sensor that enables the combination of precise sensors, potential processing, and wireless communication, such as Wi-Fi, Bluetooth, IFTTT, and MQTT protocols, to implement it successfully. The pressure sensor publishes the value of oxygen remaining from the location to the MQTT broker. 2022 IEEE. -
Impact of Risk Perception on Use and Satisfaction with Online Pharmacies and Proposed Use of IoT to Minimize Risks
This study investigates consumer risk perceptions regarding online pharmacies and their impact on usage frequency and satisfaction. The growing popularity of online pharmacies offers benefits such as accessibility, cost savings, and privacy. However, significant risks, including the potential for counterfeit drugs and insufficient medical oversight, raise concerns. This study has measured consumer perceptions of risk, satisfaction, and usage frequency through a survey conducted in Northeast India, excluding Sikkim (online) and Sikkim (offline). The findings reveal that the fear of receiving counterfeit medications is a significant risk factor, negatively influencing both the frequency of use and consumer satisfaction. Despite this, the impact is relatively weak, suggesting that while risk perception is a concern, it does not significantly deter online pharmacy usage. The study suggests that integrating advanced technologies such as IoT, RFID, and blockchain can mitigate these risks by ensuring the authenticity of medications in the supply chain. 2024 IEEE. -
VNPR system using artificial neural network
Vehicle number plate recognition (VNPR) is a technique used to extract the license plate from a sequence of images. The extracted information in the database can be used in the applications like electronic payment systems such as toll payment, parking lots etc. An effective VNPR can be implemented based on the quality of the acquired images. It is used for real time application and it has to recognize the number plates of all types under different environmental conditions. Different algorithms has been used which depends on the features present in the images. It should be generalised to extract different types of license plate from the images. In this paper we propose a new method which is robust enough to recognize the characters from the number plates with help of artificial neural network. This algorithm is practical for the front view and rear view of orientation of the vehicle. 2016 IEEE. -
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. -
Social Characteristics and Its Relationship with Intent to Stay-with Reference to Financial Sectors
One of the challenging tasks of the HR management of the organization is to design the job in such a way that facilitates a good work culture/atmosphere for the employees to ensure their stay in the organization. The present study analyzed the role of social characteristics of the job with their intention to leave among the employees working in the finance sector. Primary data were collected from 250 employees working at all levels of management in the finance and banking sector in Indias southwest region through the Convenience sampling method. Morgeson and Humphrey (2006) developed the work design questionnaire which was adopted and used for data collection. Hierarchical multiple regression used for applied for data analysis. The results show that social characteristics cannot predict intent to stay. Also, age and gender do not have a significant role as mediating factors to social characteristics and intent to stay. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Synergy Unleashed: Smart Governance, Sustainable Tourism, and the Bioeconomy
This study investigates the transformational potential of smart Governance in the tourism sector to enhance the operational effectiveness, transparency, and efficacy of governmental actions. This research synthesises the body of knowledge regarding the use of technology and data-driven methods in Governance using a literature review methodology. A conceptual framework is suggested to highlight the complex effects of smart Governance on many stakeholders in the travel industry. The study uses a multidimensional paradigm that includes agile leadership, stakeholder alliances, network management, and adaptive Governance. It explains how these complementary components construct a revolutionary ecology that encourages creativity, adaptability, and inclusive growth. Organisations can acquire insights into visitor behaviours, preferences, and traffic patterns by utilising data analytics and digital platforms, which can improve resource allocation, infrastructure construction, and policy formation. Applications that use real-time data enable dynamic crowd control, traffic optimisation, and safety improvements. The report also highlights how local communities may be involved in smart Governance to promote inclusive decision-making. This framework helps promote deeper study into the actual application and outcomes of smart Governance, which has the potential to change the travel sector. This multidisciplinary approach fosters resilience, innovation, and responsible, inclusive development. This study promotes real-world applications that fully utilise this synergy to further the interconnected objectives of sustainable tourism, bioeconomic growth, and efficient Governance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Analysis of Market Behavior Using Popular Digital Design Technical Indicators and Neural Network
Forecasting the future price movements and the market trend with combinations of technical indicators and machine learning techniques has been a broad area of study and it is important to identify those models which produce results with accuracy. Technical analysis of stock movements considers the price and volume of stocks for prediction. Technical indicators such as Relative Strength Index (RSI), Stochastic Oscillator, Bollinger bands, and Moving Averages are used to find out the buy and sell signals along with the chart patterns which determine the price movements and trend of the market. In this article, the various technical indicator signals are considered as inputs and they are trained and tested through machine learning techniques to develop a model that predicts the movements accurately. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
This research intends to enhance ovarian cancer detection by the combination of state-of-the-art machine learning algorithms with extensive multi-modal datasets. The Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and VGG16 models were thoroughly assessed, displaying remarkable precision, recall, F1 scores, and overall accuracy. Notably, VGG16 emerged as a strong performance with a precision of 0.97, recall of 0.96, F1 score of 0.97, and accuracy reaching 98.65%. The addition of confusion matrices enables a thorough insight on each model's classification performance. Leveraging multiple datasets, spanning CT and MRI scans with demographic and biographical facts, promotes the holistic knowledge of ovarian cancer features. While the suggested Hybrid Evolutionary Deep Learning Model was not deployed in this work, the results underscore the potential for its development in future research. These discoveries signify a huge leap forward in early detection capabilities and individualized treatment techniques for ovarian cancer patients. As technology and medicine combine, this study tracks a road for breakthrough diagnostic approaches, empowering clinicians and encouraging favourable results in the continuing struggle against ovarian cancer. 2024 IEEE. -
Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE. -
Asset productivity in organisations at the intersection of big data analytics and supply chain management
A close investigation is required on the fundamental instruments of an establishments big data analytics usage. This research paper mainly addresses how is an organizations value creation affected due to big data analytics usage, what is big data analytics, and what are its key antecedents in an organization to understand the aspects that influence the actual usage of big data analytics. Hence, the technology, organization, and environment framework are used. The review data collected from Indian founded corporations confirm that: organizational value creation is significantly affected by big data analytics usage in that organization; organizational BDA usage is indirectly influenced by environmental factors, technological factors, and organizational factors through top management support. Collectively, this research study will guide the business managers on the usage of big data analytics, and a theory-based comprehensive analysis of big data analytics usage and its key antecedents. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
Kidney Abnormalities Detection and Classification Using CNN-based Feature Extraction
The presents of noises degrade the quality of ultrasound images and diminishes the disease diagnosis accuracy. Thus, an effective automatic stone and cyst detection system is beneficial to both the medical practitioners and patients. In this paper, an automatic detection and classification system for kidney stone and cyst image is proposed. The Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) techniques are applied to improve the quality of the images. In the next step, segmentation has been done based on the entropy of the image. The gamma correction technique has been applied to improve the overall brightness and an optimal global threshold value is selected to extract the region. The CNN model has attained much attention in medical image recognition and classification. In this paper, the pre-trained model ResNet-50 is utilized as a feature-extractor and Support Vector Machine as classifier to categorize the normal, cyst and stone images. The CNN model is analyzed with various other classification models such as k-nearest neighbor, decision tree and Nae Bayes. The results demonstrate that the ResNet-50 with supervised classification algorithm SVM is an optimal solution for analyzing kidney diseases. 2022 IEEE. -
Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review
Ultrasonography is the most accepted and widely used imaging technique due to its non-invasive and radiation-free nature. The heterogeneous structure of kidney makes the disease detection a difficult task. Hence, more efficient models and methods are required to assist radiologists in making precise decisions. Since ultrasound imaging is considered to be the initial step in the diagnosis, more efficient processing techniques are needed in the interpretation of images. The presence of speckle noise is a challenge task in image processing. It diminishes the clarity of the images. In this article, an in-depth review has been performed on various machine learning and deep learning techniques, which are helping to improve the quality of images. The pre-processing, segmentation, feature extraction, and classification are described in detail using kidney cyst, stone, tumor, and normal kidney images. Deep learning techniques are enhancing the quality of the images with better accuracy. The remaining challenges and directions for future research are also explored. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Dielectric performance of solid dielectric immersed in vegetable oil with antioxidant
Transformers are the most vital part of the power transmission and distribution system. Protecting them from all possible abnormalities is of very high priority. The insulation levels in the transformers need to be of very high grade as the power and voltage levels of a transformer are very high. Transformers are generally filled with petroleum based mineral oil as an insulator and also as a coolant inside them. These oils are highly inflammable and also highly toxic. They are also non-biodegradable, causing major harm to the environment. Vegetable oils which are abundant in nature unlike the mineral oil is being studied as a suitable substitute for mineral oils as transformer oil. The availability of vegetable oils differ from place to place. The work here focusses on the commercially available vegetable oils in India. Seven different samples of oil are tested for their dielectric properties and viscosity and the best one among them is tested with a solid dielectric (epoxy) immersed within it in order to simulate more appropriate conditions of a practical transformer. The tests are conducted based on Indian Standards (IS6792). 2016 IEEE. -
Predicting Employee Attrition Using Machine Learning Algorithms
Employees are considered the foundation of any organization. Due to their importance, the Human resources department implements various policies to sustain them. Yet the attrition rate in any organization is increasing yearly. The attrition rate signifies the number of employees who leaves a firm without being replaced. It is regarded as a well-known issue that requires the administration to make the best choices to retain highly competent staff. It is interesting to note that artificial intelligence is frequently used as a successful technique for foreseeing such an issue. This review paper aims to study the different machine learning approaches that predict employee attrition and factors influencing an employee to attrite from an organization. A Hybrid model comprising the various ensemble models is proposed to predict attrition at its earliest. The forecasted attrition model aids in not only taking preventive action but also in improving recruiting choices and rewarding top performers who contribute to the company's success. 2022 IEEE. -
Spectroscopic analysis of lead borate systems
Oxide glass systems are interesting because of their bonding like bridging and non-bridging oxygens. Depending on the modifier, the B2O3 glass system can have various Boron-Oxygen network. It is found that, PbO modifies the borate network and increases the formation of penta and diborate groups. In this work, we investigated optical properties of Lead Borate glass systems (x PbO: (1-x) B2O3) with x varying from 30-85 mol % using UV-VIS Spectra and the corresponding band gap was estimated using Tauc relation and these systems behave like direct allowed band gap systems. These results show that, Eg decreases with the addition of lead content. Further the refractive index measurements also have been carried out at various wavelengths. Many correlation is found between the band gap and refractive index for different compositions. Using different theoretical models a best fit has been tried and Ravindra's relation is found to match with our experimental results. 2018 Author(s).