Redefining governance: a critical analysis of sustainability transformation in e-governance.

With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.

Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems.

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications.

Botnet Attack Detection in IoT Using Machine Learning.

There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk attacks. These attacks disrupt IoT transition by disrupting networks and services for IoT devices. Many recent studies have proposed ML and DL techniques for detecting and classifying botnet attacks in the IoT environment. This study proposes machine learning methods for classifying binary classes. This purpose is served by using the publicly available dataset UNSW-NB15. This dataset resolved a class imbalance problem using the SMOTE-OverSampling technique. A complete machine learning pipeline was proposed, including exploratory data analysis, which provides detailed insights into the data, followed by preprocessing. During this process, the data passes through six fundamental steps. A decision tree, an XgBoost model, and a logistic regression model are proposed, trained, tested, and evaluated on the dataset. In addition to model accuracy, F1-score, recall, and precision are also considered. Based on all experiments, it is concluded that the decision tree outperformed with 94% test accuracy.

Hybrid Approach for Improving the Performance of Data Reliability in Cloud Storage Management.

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research.

Moderating role of institutional quality in validation of pollution haven hypothesis in BRICS: a new evidence by using DCCE approach.

The technological innovation and strict environmental protocols in the highly developed regions have become the primary sources for foreign direct investment to move in the pollution haven economies. In this regard, this study attempted to identify the role of foreign direct investment (FDI) in the developing economies of the Brazil, Russia, India, China, and South Africa (BRICS) region. For this reason, a dataset was obtained between 1995 and 2019. Chudik and Pesaran's (2015) latest dynamic common correlated effects (DCCE) technique is used because of its new features when integrating the problems of heterogeneity and structural breaks into panel data that are general and do not encompass much recent research in this context. According to the empirical outcomes, foreign direct investment is a source of pollution haven in this region. However, the moderating effect of institutional quality on foreign direct investment has been found negative for ecological footprint. It also found the threshold point where the foreign direct investment effect becomes negative on ecological footprint. Based on these empirical results, this research suggests that foreign direct investment strategy should be maintained in the presence of good institutional efficiency as it enhances the environment and promotes economic development.

Acral melanoma detection using dermoscopic images and convolutional neural networks.

Acral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

Economic growth, fiscal imbalance, and environmental sustainability: What is desirable and undesirable for developing economies?

Developing economies are suffering to fulfill the sustainable environment's commitments in fiscal imbalance. This study attempted to highlight the core issue of fiscal imbalance in developing economies and its impact on a sustainable environment. For this purpose, the study utilized generalized least squares (GLS) and quantile autoregressive distributive lag (QARDL) on a 19-year dataset (2000-2018) of the South Asian region. The results of GLS indicate that fiscal imbalance contributing positively to South Asia's environmental degradation process. Here, energy consumption (due to dirty sources of energy) and energy intensity (due to inefficient energy conversion technology) are also sources of environmental degradation in this region. The results of QARDL confirm that economic and political fluctuations can be the long-run source of fiscal imbalance in this region, which ultimately slows down the process of the environmental Kuznets curve (EKC) theory and contributes positively to environmental degradation. Based on the empirical analysis, this study provides a comprehensive set of policy guidance for developing and developed economies for the smooth transition of sustainable environmental conditions in South Asia.

Globalization, sustainable development, and variation in cost of power plant technologies: A perspective of developing economies.

This study evaluates the sustainable power plant cost in the outlook of global power plant efficiency to reduce fossil fuel dependency and greenhouse gas emissions. For this purpose, the Global Change Assessment Model (GCAM) applied for conducting the cost assessment of power zone technologies for all principal electricity generation. This study considers the characteristics of essential factors (cement, price of resources, possible increases in employees, and metals) that affect costs. This study suggests that the cost of electricity-generating technologies significantly affects growth efficiency, reduction in production cost, and improving environmental conditions. It also suggests that the cost of electricity-generating technologies, combined with technology mixture, is the key factor behind replacing existing technology in the electricity sector. EPRI cost assessments expanded by around 30% and 50% during 2015-2016. Factors like competition amongst power plant owners, the ambiguous marketplace, production procedures, and lack of environment-related strategies have resulted in massive environmental degradation in developing economies like Pakistan. Based on empirical findings, this study recommends designing efficient technologies, which would reduce power plant costs and ensure vertical prospects related to environmental conditions in the future.

Managerial policy and economic analysis of wind-generated renewable hydrogen for light-duty vehicles: Green solution of energy crises.

The unconventional energy sources like hydrogen energy have tremendous potential of filling the gap between economic growth and clean energy consumption. A little intention has been made in this regard in the developing economies like Pakistan. This study develops a fusibility analysis to highlight the potential of hydrogen energy source in Pakistan. For this purpose, this study used a hybrid mathematical model that combines the range of wind speed with the log law to push wind power's potential to generate wind hydrogen in Pakistan. The study results indicate that Pakistan has an excellent source to generate hydrogen energy through wind power stations. According to the outcomes, Nooriabad can produce 303.66 million RE/kWh per year through wind energy sources. According to the results, the rest of the seven wind generation sites also can generate enough hydrogen energy. This study also concluded that hydrogen energy has enough sources to meet the demand for light-duty vehicles in Pakistan.

Correction to: Optimal oil stockpiling, peak oil, and general equilibrium: case study of South Asia (oil importers) and Middle East (oil supplier).

The correct affiliation of the 1st Author is shown in this paper.

Correction to: Assessing the integration of solar power projects: SWOT-based AHP-F-TOPSIS case study of Turkey.

The correct affiliation of the 1st Author is shown in this paper.

Trilemma among energy, economic and environmental efficiency: Can dilemma of EEE address simultaneously in era of COP 21?

Economic growth is a prerequisite for development while energy is the engine for growth process. In the era of globalization, production cost heavily dependent on energy intensity and efficiency with keeping environmental conditions intact. Therefore, the structuring of a significant environmental index is pre-requisition of the world with energy intensity, energy efficiency. For this objective, this study employs 18-years data set between 2000 and 2018 of Organization for Economic Co-operation and Development (OECD) to form a universal aspect of economics, environmental index, and energy efficiency (EEE). The data envelopment analysis (DEA) and arithmetic mean aggregation in the formation of mathematical aggregation mechanisms are applied for empirical analysis. According to the results, Iceland maintained an overall high rank regarding energy efficiency, energy intensity, and environment followed by Greece, New Zeeland, and Norway. In this investigation, Ireland followed by the UK and the USA are the lowest performer regarding energy and environment. Thus, the study concludes that energy (efficiency & intensity) and environment hold complex relations and needs a special set of policies to address them collectively and it is expected that this study can be used as valuable information for the policymaking process.

The role of fixed capital formation, renewable and non-renewable energy in economic growth and carbon emission: a case study of Belt and Road Initiative project.

Economic integration in the form of Belt and Road Initiative project opens many opportunities and hazards, especially of the participating nations' environment. The current study attempted to empirically test the economic and energy usage (renewable and non-renewable) impact on some selected countries of belt and road projects. For this purpose, the panel data set of twenty-four emerging economies of belt and road projects was selected from 1995 to 2014. The autoregressive distributed lags technique of econometric applied to determine the effect of renewable and non-renewable energy, GDP and GDP2 for EKC, and gross fixed capital formation on carbon emission in the selected countries of Belt and Road Initiative project. The outcomes of this study confirm the existence of EKC in these underlined countries. Here, fossil fuel-based energy consumption is a source of environmental degradation, while renewable and clean energy usage can help sustain environmental conditions without affecting economic growth progress. Capital fixed formation in these economies can enhance economic growth and help to sustainable environmental conditions in the belt and road countries. Thus, based on these empirical outcomes, this study suggests economic and financial assistance in green renewable energy sources and clean technological innovation to enhance economic benefits of Belt and Road Initiative project without compromising the environmental conditions of the region.

Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana.

Wind energy is seen as an important energy to sustainably meet the energy needs of Ghana. However, the industry in Ghana is yet to take off due to policy uncertainty and regulatory costs. The paper analyzed the key determinants and how they interact to impact the scaling up of wind energy in Ghana, using time series data, the vector auto regression (VAR) model from 2013 to 2019.There were four endogenous variables, grouped under policy, population growth, wind capacity, and electrification rate. The findings revealed the dynamic behavior of the variables from the VAR to a strongly significant positive correlation to deploying wind energy in Ghana. The impulse response functions (IRFs) equally exhibited a positive impact long-run trajectory growth of the variables after a shock to the system. The response of the first lags had differences of log policy and that of the log of GDP produced a curious result from the shock by taking a steady positive growth path in the short run and nosedived to a negative pathway in the long run. On the other hand, the interaction of the first differences of the lags of log wind capacity and log policy is quite instructive, as the headwind produced a negative relationship in the short run and to a positive growth path in the long run. This was anticipated, as the wind capacity installation of Ghana is expected to increase in the long run, when pipeline projects materialize.

Assessing the integration of solar power projects: SWOT-based AHP-F-TOPSIS case study of Turkey.

Solar energy systems are a cheaper and easy solution to cope with severe energy crisis especially in emerging economies including Turkey which exerted huge efforts to enhance the existing solar power projects. However, the selection of the optimal site for the installation of solar projects needs vigorous investigation through various factors. Adequate quantitative scientific research is required for the process of site selection in Turkey. This paper categorizes various sites in Turkey through various factors such as economic, environmental, and social factors. Various major criteria have been combined through mathematical development to install the solar power project in remote areas of Turkey. The scientific evaluation of remote and rural solar projects in Turkey has been taken as a case study in the current paper. Additionally, the analytical hierarchy process (AHP) and F-VIKOR methods were used to aggregate the criteria. The results show that economic and social ratio is significant, whereas the transmission matrix, land cost, and the sun irradiance got a major score in order to generate electricity. The study results show that total sunshine time per year determined is 2741 h (a total of 7.5 h per day) and the total solar energy obtained each year is 1527 kWh per square meter per year (a total of 4.18 kWh per square meter per day).

Nexus between sustainable entrepreneurship and environmental pollution: evidence from developing economy.

Today, society is seeking solutions to achieve sustainable development, through association between entrepreneurship, innovation and sustainable development has become a topic of great apprehension. In this perspective, this article aims to link environmental responsive entrepreneurship with sustainable development through empirical evidences from developing country. Therefore, the purpose of this study is to validate the environmental Kuznets curve hypothesis to confirm the achievement of sustainable development goals in Pakistan. We use the combined mean estimator of the autoregressive distribution lag model and GMM model to determine the long-term relationship between the variables and analyze the environmental Kuznets curve hypothesis. We found U-shaped environmental Kuznets curves in Pakistan. Further results show long-term relationship using the PMG-ARDL estimator. Our findings indicate the presence of EKC, U-shaped EKC. This means that at a certain level of economic growth, a 1% increase in per capita income can lead to reductions in environmental pollution by 2.88%, 4.54%, and 2.48%. Therefore, governments and policy makers should strengthen policies to reduce environmental pollution and, more importantly, formulate green financing policies to encourage aspiring environmental entrepreneurs to establish environmentally driven enterprises, promote the use of environmental products to reduce environmental problems, and achieve sustainable development in Pakistan.

Scaling up renewable energy in Africa: measuring wind energy through econometric approach.

Wind energy continues to make inroads in Africa due to falling costs and technological advancements. Most African countries are planning, exsiccating and connecting their renewable energy projects with national grid system with giving high propriety to energy security, sustainable energy consumption and low carbon emission. Many policies have been enacted by countries to promote the scaling up of wind energy and renewable energy in particular, across the globe. However, these policies have mixed effects on the deployment of wind energy. For this purpose, current study used panel data and fixed effects model for 17 African countries with wind installed generation capacity to determine the driver of wind energy development on the African continent between 2008 and 2017. The variables were grouped into three thematic areas: policy, socioeconomic, and country-specific factors. After conducting the analysis, socioeconomic variables (GDP, CO2, energy use) and energy security variables (energy import, electricity consumption) have significant effects in determining the scaling up of wind energy in Africa. However, the policy variables of FITs, licensing during, and Tax did not have significant effects on wind energy capacity addition for the case of Africa. This study adds to the drivers of nascent wind energy deployment literature in Africa. This study suggests that set of effecitive policies are deem necessary to scale up wind energy in Africa.

Trilemma assessment of energy intensity, efficiency, and environmental index: evidence from BRICS countries.

This paper provides an assessment of energy density and energy efficiency and creates an important indicator of environmental performance. This article applied two mathematical models and econometric techniques to obtain detailed and specific results. The DEA and the non-normative account aggregation mean a collective aggregation to form a mathematical aggregation tool to create an environmental index for the BRICS countries (Brazil, Russia, India, China, and South Africa) based on available data from 2011 to 2016. The advantage of the proposed approach is to manage the irregularities of the data and follow the desired properties of the index number. The current paper is relevant for the broad scope of construction, the environmental index, and the evolution of the rankings of countries based on multiple indicators. Our results indicate that Brazil and Russia have the highest values of the Environmental Performance Index, which range between 67.44 and 60.70, respectively. India has a minimum value of 30.57 of the environmental index. The analysis shows that Brazil, Russia, and South Africa have the best scores and that these countries have the best results, while China and India also have the best results. This study can help form a valuable political tool for the development and development of the country's politics.

Optimal oil stockpiling, peak oil, and general equilibrium: case study of South Asia (oil importers) and Middle East (oil supplier).

Optimal stockpiling is the best possible strategy to overcome the problem of peak oil periods of oil producer economies. We measured the properties of strategic petroleum oil reserve and general equilibrium and its peak oil effects. Measured the optimized scales of SPR through using oil price model, global oil market, and depletion effects of oil production classification. The peak oil period occurs from the interection between the geological era, proficiency in a practical skill, economy of consumers, and geopolitics, and the quality of deciding of demand and supply in which we have done a general dynamic balance model. Results reveal that peak oil time periods may lead towards diverse oil prices time profiles, economic development, and commodity flows. Interestingly, the macroeconomic effects of peak oil and the trajectories in objective function of two options maximize the households' welfare and oil revenues and its effect on growth trajectories of oil-consuming countries. If an oil supply disruption happens, the rate of oil acquisition will be considerably decreased, though it may not be a good strategy to interrupt the activities of oil reserve with the aim of minimizing the overall costs.