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Green computing in network security : energy efficient solutions for business and home
\"This book focuses on green computing-based network security techniques and addresses the challenges involved in practical implementation. It also explores the idea of energy-efficient computing for network and data security and covers the security threats involved in social networks, data centers, IoT, and biomedical applications. Green Computing in Network Security: Energy Efficient Solutions for Business and Home includes analysis of green-security mechanisms and explores the role of green computing for secured modern internet applications. It discusses green computing-based distributed learning approaches for security and emphasizes the development of green computing-based security systems for IoT devices. Written with researchers, academic libraries, and professionals in mind so they can get up to speed on network security, the challenges, and implementation processes\"-- Provided by publisher.
Book
Green Communications and Networking
This book presents recent advances in green communications and networking for wired, wireless, and smart-grid networks. These technologies not only provide the emission reduction and energy savings in Information and Communication Technology (ICT) products and services, but also enable low GHG emissions in other industries, such as electric power. The text covers new algorithms, protocols, and network architectures to make cellular networks more energy efficient. It also includes a rich set of references in each chapter.
eBook
The sustainable network : the accidental answer for a troubled planet
This book demonstrates how we can tackle challenges, ranging from energy conservation to economic and social innovation, using the global network of which the public Internet is just one piece. To help solve a myriad of problems today, author Sarah Sorensen points out that the best tool for enacting change already exists, lying literally at our fingertips. This book demystifies the power of the network and issues a strong call to action.
Book
Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry
Abstract This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.
Journal Article
Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. Among DL models, ANN demonstrated the best performance, achieving an R-squared value of 0.7248 and a RMSE of 0.1516. The results show that DL models, especially ANN, did slightly better than the best ML models. This means that they are better at modeling non-linear dependencies in multivariate data. Preprocessing techniques, including feature scaling and parameter tuning, improved model performance by enhancing data consistency and optimizing hyperparameters. When compared to previous benchmarks, the performance of both ANN and ET demonstrates significant predictive accuracy gains in WT power output forecasting. This study’s novelty lies in directly comparing a diverse range of ML and DL algorithms while highlighting the potential of advanced computational approaches for renewable energy optimization.
Journal Article
The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress
Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.
Journal Article
Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment
Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.
Journal Article
Deep learning via LSTM models for COVID-19 infection forecasting in India
The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.
Journal Article
Ideological biases in social sharing of online information about climate change
Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.
Journal Article
Modelling vegetation land fragmentation in urban areas of Western Province, Sri Lanka using an Artificial Intelligence-based simulation technique
Vegetation land fragmentation has had numerous negative repercussions on sustainable development around the world. Urban planners are currently avidly investigating vegetation land fragmentation due to its effects on sustainable development. The literature has identified a research gap in the development of Artificial Intelligence [AI]-based models to simulate vegetation land fragmentation in urban contexts with multiple affecting elements. As a result, the primary aim of this research is to create an AI-based simulation framework to simulate vegetation land fragmentation in metropolitan settings. The main objective is to use non-linear analysis to identify the factors that contribute to vegetation land fragmentation. The proposed methodology is applied for Western Province, Sri Lanka. Accessibility growth, initial vegetation large patch size, initial vegetation land fragmentation, initial built-up land fragmentation, initial vegetation shape irregularity, initial vegetation circularity, initial building density, and initial vegetation patch association are the main variables used to frame the model among the 20 variables related to patches, corridors, matrix and other. This study created a feed-forward Artificial Neural Network [ANN] using R statistical software to analyze non-linear interactions and their magnitudes. The study likewise utilized WEKA software to create a Decision Tree [DT] modeling framework to explain the effect of variables. According to the ANN olden algorithm, accessibility growth has the maximum importance level [44] between -50 and 50, while DT reveals accessibility growth as the root of the Level of Vegetation Land Fragmentation [LVLF]. Small, irregular, and dispersed vegetation patches are especially vulnerable to fragmentation. As a result, study contributes detech and managing vegetation land fragmentation patterns in urban environments, while opening up vegetation land fragmentation research topics to AI applications.
Journal Article