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Forest Fire Prediction Using Machine Learning and Deep Learning Techniques
Forests are considered synonyms for abundance on our planet. They uphold the lifecycle of a diversity of creatures, including mankind. Destruction of such forests due to environmental hazards like forest fires is disastrous and leads to loss of economy, wildlife, property, and people. It endangers everything in its vicinity. Sadly, the presence of flora and fauna only increase the fire spread capability and speed. Early detection of these forest fires can help control the spread and protect the nearby areas from the damage caused. This research paper aims at predicting the occurrence of forest fires using machine learning and deep learning techniques. The idea is to apply multiple algorithms to the data and perform comparative analysis to find the best-performing model. The best performance is obtained by the decision tree model for this work. It gave an accuracy of 79.6% and a recall score of 0.90. This model was then implemented on front-end WebUI using the flask and pickle modules in Python. The front-end Website returns the probability that a forest fire occurs for a set of inputs given by the user. This implementation is done using the PyCharm IDE. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Forensic toxicological and analytical aspects of carbamate poisoning A review
Pesticides play a pivotal role in modern agricultural practices and effective domestic pest control. Despite their advantages, pesticides pose a great danger to humans and animals due to their toxicity. Pesticides, particularly carbamates, are extensively used all over the world in crop protection and domestic pest control, however, also causing morbidity and mortality on a larger scale, which is of great significance in both clinical and criminal justice management.Carbamates are derived from a carbamic acid (NH2COOH) that are commonly used as insecticides. Ethienocarb, Sevin, Carbaryl, Fenoxycarb, Furadan, Carbofuran, Aldicarb, and 2-(1-Methylpropyl) phenyl N-methylcarbamate are examples of insecticides that include the carbamate functional group. By reversibly inactivating the enzyme acetylcholinesterase, these insecticides can induce cholinesterase inhibition poisoning.Chromatographic methods, notably gas and liquid chromatography have traditionally been employed to analyse carbamate pesticides and their metabolites in various matrices. These approaches are employed due to their ability to separate the chemicals contained in a sample; as well as identify and quantify these compounds utilizing advanced detection systems. Aside from these GC and LC conventional methods, other detection and/or hyphenated techniques such as single-quadrupole, ion-trap, triple-quadrupole, or tandem mass spectrometry, have been used in carbamate analysis to provide quick results with excellent sensitivity, precision, and accuracy.The objective of this review is to describe various analytical techniques used to detect and determine carbamate pesticides in various matrices which include urine, blood, and tissues that are commonly encountered in emergency hospital laboratories and forensic science laboratories. 2022 Elsevier Ltd and Faculty of Forensic and Legal Medicine -
Forensic Investigation Approaches of DNA Analysis and Criminal Investigation
Deoxyribonucleic acid (DNA) has been a significant factor in the criminal justice system since it was first used in forensic investigations. The reference sample's DNA profile is typically compared to the DNA profile from the evidence sample from the crime scene criminal cases. Familial DNA analysis can identify a person and provide significant investigation leads even without a reference sample for comparison in a criminal investigation process. The potential source of a forensic biological sample is determined using several indirect database searching techniques. These DNA-based techniques include Mitochondrial DNA (mtDNA) analysis, investigative genetic genealogy (IGG), familial searching, and Y-STR database searching. This study examined these methods and compares them in terms of searching efficiency, database structures, searching methods, genotyping technologies, data security, data quality, and costs. It also raises several possible legal and privacy problems for scientists to consider further. The significance of familial DNA analysis, the procedures used for finding and identifying relatives using familial DNA, and its benefits in forensics are all covered in this paper. Additionally, future options for the appropriate application of this technology and social, legal, and ethical concerns related to familial DNA analysis have been considered. 2023 WITPress. All rights reserved. -
Forensic applications of graphene oxide
Forensic analysis is an enormous field comprising the detection of various types of samples. The objective of forensic evidence examination is to provide a cohesive, transparent, and unbiased judgment of the evidence's significance to the investigators. Graphene oxide (GO) is an abundant substance that comprises carbon, hydrogen, and oxygen in a single layer making it highly economical. Therefore, the utilization of GO is highly considered for achieving the detection and analysis of various substrates. This can be justified by its facile and economical preparation that contributes to improving its significance and applicability. Forensic samples include trace elements that can be pre-concentrated with the help of a sustainable medium provided by GO. This book chapter aims to provide exciting insights into the synthesis, properties, and applications of GO in the detection of various forensic samples of GO. 2024 -
Foreign policy of China under Deng Xiaoping and its contemporary relevance
Political leadership plays an important role in foreign policy decision making in general. Studying leadership traits, styles, beliefs and world view is one of the common methods to comprehend political leaders and their influence on foreign policy. When it comes to authoritarian countries like China, its foreign policy decision making has several layers of which political leaders play all the more crucial role. Irrespective of the period – Imperial, Nationalist or Communist – the political leaders of China are guided by its history, philosophy and the then existing domestic and global circumstances, in formulating and implementing the country’s foreign policy. -
Foreign Policy of China a Under Deng Xiaoping and its Contemporary Relevance
Political leadership plays an important role in foreign policy decision making in general. Studying leadership traits, styles, beliefs and world view is one of the common methods to comprehend political leaders and their influence on foreign policy. When it comes to authoritarian countries like China, its foreign policy decision making has several layers of which political leaders play all the more crucial role. Irrespective of the period Imperial, Nationalist or Communist the political leaders of China are guided by its history, philosophy and the then existing domestic and global circumstances, in formulating and implementing the country s foreign policy. Political leadership plays an important role in foreign policy decision making in general. Studying leadership traits, styles, beliefs and world view is one of the common methods to comprehend political leaders and their influence on foreign policy. When it comes to authoritarian countries like China, its foreign policy decision making has several layers of which political leaders play all the more crucial role. Irrespective of the period Imperial, Nationalist or Communist the political leaders of China are guided by its history, philosophy and the then existing domestic and global circumstances, in formulating and implementing the country s foreign policy. The central enquiry of the study is to assess contemporary relevance of Deng s foreign newlinepolicy paradigm. Through field visits and rigorous analysis of primary sources, the newlinestudy establishes that relevance of Deng s policy continues in the present context except on China s pro-activeness towards issues pertaining to its territorial integrity and sovereignty. Using China s case, the study advances the framework of understanding pertaining to the role of political leadership in foreign policy making. The study also makes certain broad policy recommendations to various stakeholders for consideration. -
Foreign Exchange, Gold, and Real Estate Markets in India: An Analysis of Return Volatility and Transmission
This empirical analysis endeavored to investigate the return volatility, covolatility, and the spillover impact of gold, real estate, and U.S. dollar in India. The generalized autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH -DCC) was used to reveal the return volatility and conditional correlation. The volatility spillover was examined by using the variance decomposition technique. The empirical outcome clearly revealed the presence of ARCH and GARCH effect on gold, realty, and U.S. dollar. Additionally, the results also manifested that the returns of these variables were not moving away from their means in the long run. On the other hand, the consequences of volatility spillover reported that real estate was the most dominating among all markets. This is so because returns on real estate had a significant contribution to the return volatility of the other markets. Finally, it was also found that return volatility of U.S. dollar was most affected as it was the net receiver of volatility, while return volatility of gold seemed to be neutral in the Indian financial market. -
Foreign exchange rate forecasting using Levenberg-Marquardt learning algorithm
Background/Objectives: Foreign currency Exchange (FOREX) plays a vital role for currency trading in the international market. Accurate prediction of foreign currency exchange rate is a challenging task. The paper investigates the FOREX prediction using feed forward neural network. Methods/Statistical analysis: This paper employs artificial neural network to forecast foreign currency exchange rate in India during 2010-2015.The exchange rates considered between Indian Rupee and four major currencies Euro, Japanese Yen, Pound Sterling and US Dollar. The network developed consists of an input layer, hidden layer and output layer. The neural network was trained with Levenberg-Marquardt (LM) learning algorithm. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Forecasting Error (FE) are used as indicators for the performance of the networks. Findings: Simulation results are presented to show the performance of the proposed system. The paper also aims to suggest about network topology that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed technique gives the evidence that there is possibility of extracting information hidden in the foreign exchange rate and predicting into the future. Applications/Improvements: Finally, this paper presents the best network topology for FOREX prediction by comparing the effectiveness of various hidden layer performance algorithm using MATLAB neural network software as a tool. -
Foreground algorithms for detection and extraction of an object in multimedia
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called "background image" and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It's quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required. 2020 Institute of Advanced Engineering and Science. -
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Forecasting volatility evidence from the futures market in India
This thesis focuses on modelling and forecasting of select products in the Indian futures market using econometric time series models and artificial neural network based models. These models have been compared for their forecasting accuracy to determine the best forecasting model for a particular futures series. This study applies GARCH, EGARCH, PARCH, TARCH, and Artificial Neural Networks (ANN) to assess the best predicting model for exchange rate futures, commodity index futures and stock index futures. After testing for stationarity of data series, GARCH, EGARCH, PARCH and TARCH models are developed. In addition to in-sample forecasts, 1-day, 5-day, 10-day, 15-day and 30-day out-of-sample forecasts have been carried out. For ANN, data is scaled using the minmax scaling methodology to ensure that newlinethe data series is normalised and in the range of 0 to 1. ANN is developed using the feedforward methodology. While the basic neural network architecture has one input layer, one hidden layer and one output layer, the number of neurons in the input and hidden layers vary from 1 to 20. The optimum number of input and hidden neurons in their respective layers are then selected based on the combination which gives the least error. These network combinations are used for out-of-sample forecasting and errors are compared with the forecast output of the GARCH models. RMSE, MAE, MAPE, Theil s-U statistic and Correlation coefficient is computed for error newlinecomparison. Results indicate that for currency futures and commodity index futures, ANN provides better forecast accuracy. For stock index futures, GARCH family models work better in some cases. -
Forecasting the Volatility of Indian Forex Market: An Evidence from GARCH Model
Forecasting the volatility of forex market will create more trading opportunities to investors, despite of ups and downs in the forex market. The present study attempted to examine how the volatility in the exchange rate between Indian rupee and selected four foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the market return. The data, used in the present study, covered the daily price observation of four foreign currencies, for a period of 5 years, from 2019-2023. The GARCH (1, 1) (generalized autoregressive conditional hetero skedasticity) was used for develop the model for foreign exchange (FX) rates volatility. Mean equation model confirmed that the series had attained stationary and previous price did influence the current price. It was also supported by co-efficient values in the variance equation. The co-efficient value, in the variance equation, was around one, which showed that the forex market was efficient. Further, it was validated that the volatility shocks in forex market were quite persistent. The active investors in the market may use this opportunity immediately. The policy maker may correct this deviation through timely intervention in the currency market. 2024, Iquz Galaxy Publisher. All rights reserved. -
Forecasting the Stock Market Index Using Artificial Intelligence Techniques
If the stock market would have a predictable to maximum accuracy, then every stockbroker and investor would have been billionaire. But it is not the ground truth. In a one-to-one interaction with stock analysts, who mention that the stock market is unpredictable and that is why their role is important, else everything would have been black and white. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants
In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors. 2023 IEEE.