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Environmental degradation has put significant consequences at both the national and global levels. Economic development and ecological sustainability can be achieved by an increase in economic growth but on the cost of environmental quality. The basic aim of this analysis is to find the existence of the aviation Kuznets curve (AKC) and the environmental Kuznets curve (EKC) for the selected 21 OECD nations from 1980 to 2018. The aviation cargo and aviation passengers’ patterns are separately analyzed in relation to CO2 emissions to quantify whether Kuznets phenomenon exists in the aviation industry of OECD nations? The econometric results of panel GMM confirm the validation of the aviation Kuznets curve in case of aviation passengers such that the “inverted U shaped” pattern. While the environmental Kuznets curve and aviation cargo Kuznets curve do not exist in the OECD nation. It means that an increase in aviation cargo and economic growth increases the CO2 emissions showing the “U shaped” association. Moreover, the Pairwise Dumitrescu Hurlin panel causality test exhibits uni–directional association moving from economic growth to carbon emissions, while there is bidirectional granger causality among trade openness, aviation sector, and energy consumption to carbon emissions. The overall results suggest that OECD nations must consider the pollution reduction techniques, investment in renewable sources, continuous research, implementation of efficient energy sources, and the imposition of aviation emissions taxes to limit the pollution.

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

This study investigates the impact of factors (network coverage, customer service, video calls, and downloading Speed) of 3G and 4G telecommunication services performance on customer satisfaction in the Punjab region of Pakistan. This research indicates how to make strong relations with customers and what factors of the 3G and 4G networks need to be improved to enhance the revenue of telecom operator companies. The study has recognized the four main hypotheses responsible for checking the level of customer satisfaction in the telecommunication industry of Pakistan in the Punjab region. The study depends on essential insights gathered on the arbitrary premise from 300 clients of significant telecom administrators in the Punjab area. The respondents have selected on irregular premises and were welcomed to express their sentiments through an organized survey. A complete questionnaire has been utilized for statistics collection and exists based on the analysis of descriptive measurement, correlation, and regression analysis and analyzed through SmartPLS software. They indicate that the independent variables network coverage, customer service, video calls, and downloading speed are key driving factors of customer satisfaction. Among all independent variables “Internet downloading speed” highly impacts the dependent variable “customer satisfaction” based on 3G and 4G network performance. There are a limited number of studies that focus on customer satisfaction in the telecommunication sector in Pakistan. The study will fill the gap in the literature and help service providers to increase the satisfaction level of their customers and captivate new customers.

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

With the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control the interior temperature, monitor security, and perform routine maintenance. The Internet of Things (IoT) is used to connect the various components of smart buildings. As the IoT concept spreads, SGs are being integrated into larger networks. The IoT is an important part of SGs because it provides services that improve everyone’s lives. It has been established that the current life support systems are safe and effective at sustaining life. The primary goal of this research is to determine the motivation for IoT device installation in smart buildings and the grid. From this vantage point, the infrastructure that supports IoT devices and the components that comprise them is critical. The remote configuration of smart grid monitoring systems can improve the security and comfort of building occupants. Sensors are required to operate and monitor everything from consumer electronics to SGs. Network-connected devices should consume less energy and be remotely monitorable. The authors’ goal is to aid in the development of solutions based on AI, IoT, and SGs. Furthermore, the authors investigate networking, machine intelligence, and SG. Finally, we examine research on SG and IoT. Several IoT platform components are subject to debate. The first section of this paper discusses the most common machine learning methods for forecasting building energy demand. The authors then discuss IoT and how it works, in addition to the SG and smart meters, which are required for receiving real-time energy data. Then, we investigate how the various SG, IoT, and ML components integrate and operate using a simple architecture with layers organized into entities that communicate with one another via connections.

The critical component of HCI is face recognition technology. Emotional computing heavily relies on the identification of facial emotions. Applications for emotion-driven face animation and dynamic assessment are numerous (FER). Universities have started to support real-world face expression recognition research as a result. Short video clips are continually uploaded and shared online, building up a library of videos on various topics. The enormous amount of movie data appeals to system engineers and researchers of autonomous emotion mining and sentiment analysis. The main idea is that categorizing things may be done by looking at how individuals feel about specific issues. People might choose to have a basic or complex facial appearance. People worldwide continually express their feelings through their faces, whether they are happy, sad, or uncertain. An online user can visually express themselves through a video’s editing, music, and subtitles. Additionally, before the video data can be used, noise in the data must frequently be eliminated. Automatically figuring out how someone feels in a video is a challenging task that will only get harder over time. Therefore, this paper aims to show how facial recognition video analysis can be used to show how sentiment analysis can help with business growth and essential decision-making. To determine how people are affected by reviewers’ writing, we use a technique for deciding emotions in this analysis. The feelings in movies are assessed using machine learning algorithms to categorize them. A lightweight machine learning algorithm is proposed to help in Aspect-oriented emotion classification for movie reviews. Moreover, to analyze real and published datasets, experimental results are compared with different Machine Learning algorithms, i.e., Naive Bayes, Support Vector Machine, Random Forest, and CNN. The proposed approach obtained 84.72 accuracy and 79.24 sensitivity. Furthermore, the method has a specificity of 90.64 and a precision of 90.2. Thus, the proposed method significantly increases the accuracy and sensitivity of the emotion detection system from facial feature recognition. Our proposed algorithm has shown contribution to detect datasets of different emotions with symmetric characteristics and symmetrically-designed facial image recognition tasks.

A lung nodule is a tiny growth that develops in the lung. Non-cancerous nodules do not spread to other sections of the body. Malignant nodules can spread rapidly. One of the numerous dangerous kinds of cancer is lung cancer. It is responsible for taking the lives of millions of individuals each year. It is necessary to have a highly efficient technology capable of analyzing the nodule in the pre-cancerous phases of the disease. However, it is still difficult to detect nodules in CT scan data, which is an issue that has to be overcome if the following treatment is going to be effective. CT scans have been used for several years to diagnose nodules for future therapy. The radiologist can make a mistake while determining the nodule’s presence and size. There is room for error in this process. Radiologists will compare and analyze the images obtained from the CT scan to ascertain the nodule’s location and current status. It is necessary to have a dependable system that can locate the nodule in the CT scan images and provide radiologists with an automated report analysis that is easy to comprehend. In this study, we created and evaluated an algorithm that can identify a nodule by comparing multiple photos. This gives the radiologist additional data to work with in diagnosing cancer in its earliest stages in the nodule. In addition to accuracy, various characteristics were assessed during the performance assessment process. The final CNN algorithm has 84.8% accuracy, 90.47% precision, and 90.64% specificity. These numbers are all relatively close to one another. As a result, one may argue that CNN is capable of minimizing the number of false positives through in-depth training that is performed frequently.

The objective of this study is to examine the impact of innovation on three heterogeneous countries, Canada, South Korea, and Pakistan by using the consistent time series data from 1991-2011. The results confirmed the positive association between innovation and country’s economic growth controlling the factors of FDI inflows, R&D expenditures, high technology exports, patent applications residents, and scientific & technical journals. The study conclude that technological innovation have a substantial impact on increasing country’s economic growth, hence it is imperative to device strong policies to attract foreign investors to devote resources in a host country for development.