International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management最新文献

Identification of human physical activities is an active research area since long due to its application in personalized health and fitness monitoring. The performance accuracy of human activity recognition (HAR) models mainly depend on the features which are extracted from domain knowledge. The features are the input of the classification algorithm to efficiently identify human physical activities. Manually extracted features (handcrafted) need expert domain knowledge. Thus these features have significant importance to identify different human activities. Recently deep learning methods are utilized to extract the features automatically from raw sensory data for HAR models. However, state-of-the-art HAR literature established that the importance of handcrafted features can't be ignored as it is extracted from expert domain knowledge. Thus, in this paper we use the fusion of both the handcrafted features and automatically extracted features using deep learning (DL) for HAR model to enhance the performance of HAR. Extensive experimental results demonstrate that our proposed feature fusion based HAR model gives higher accuracy compared with state-of-the-art HAR literature for both the self collected and public dataset.

As people progressively resort to twitter to express their opinions or to disambiguate their sentiment, it's feasible to analyze the mass opinion to conclude the polarity of the subject at hand using sentiment analysis. Sentiment Analysis (SA) has revolutionized the way information is perceived today. Inspired by this, the work in this paper investigates the much-debated act- the Citizenship Amendment Act (CAA) by analyzing opinionated geo-tagged tweets, manually annotated and cross verified by six annotators. This is the first paper to the best of our knowledge to analyse CAA using SA and to provide a clear statistics of the mass opinion across the states of the nation. In this paper, machine learning approach is used for sentiment analysis of tweets. Support vector machine classifier is used to classify the tweets into three classes viz. positive, negative and neutral.

Regression is a kind of data analysis technique in which the relationship between the independent variable(x) and dependent variable(y) is modeled and for polynomial regression it is up to the nth degree polynomial. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted by E (y|x). In this paper polynomial regression analysis has been improved through efficient selection of variables that is coefficient of determination. Coefficient of determination is a square of the correlation between new predicted y values and actual y values and its values are in the range from 0 to 1. The main purpose of regression analysis is to discover the relationship among the independent and dependent variables or in other words it is an explanation of variation in one variable with another variable. In this paper, the main focus is on Multivariate data sets that have many attributes and it is not necessary that all variables are required for data analysis purposes. Using coefficient of determination (COD) irrelevant attributes get eliminated during analysis. The main objective of research is to reduce the cost of data maintenance, reduce the execution time and improve the prediction accuracy rate. COD helps in selecting suitable independent variables. It is a notch that is used in statistical analysis that assesses how well a model explains and forecasts upcoming outcomes. This method also helps in eliminating the irrelevant variables which are not required for the prediction model by this maintenance cost and size of data sets can be reduced.

The purpose of this research is to examine the causes of delay in airport projects in general and the King Abdulaziz International Airport (KAIA) of Saudi Arabia in particular. The KAIA is the most strategic and busiest airport of Saudi Arabia, which transports millions of pilgrims from two hundred countries to perform Hajj and Umrah in Makkah. In general, more than 50% of construction projects fail on one more area. Our method of research is empirical based on the analysis of responses received from seventy-one respondents to our survey questions. We shall also synthesize literature that utilizes previous research and case studies on aviation and public project failures. We believe that this research will be valuable in ascertaining and mitigating direct causes for the numerous project delays in Kingdom of Saudi Arabia.

We are living in the era of the fourth industrial revolution, which also treated as 4IR or Industry 4.0. Generally, 4IR considered as the mixture of robotics, artificial intelligence (AI), quantum computing, the Internet of Things (IoT) and other frontier technologies. It is obvious that nowadays a plethora of smart devices is providing services to make the daily life of humans easier. However, in the morning most people around the globe use a traditional mirror while preparing themselves for daily tasks. The aim is to build a low-cost intelligent mirror system that can display a variety of details based on user recommendations. Therefore, in this article, Internet of Things (IoT) and AI-based smart mirror is introduced that will support the users to receive the necessary daily update of weather information, date, time, calendar, to-do list, updated news headlines, traffic updates, COVID-19 cases status and so on. Moreover, a face detection method also implemented with the smart mirror to construct the architecture more secure. Our proposed MirrorME application provides a success rate of nearly 87% in interacting with the features of face recognition and voice input. The mirror is capable of delivering multimedia facilities while maintaining high levels of security within the device.

The global pandemic of COVID-19 has been going on for over sixteen months. During this period, we have witnessed a colossal loss of life, property, business, and a degradation of social life. Several different variants or strains of SARS-CoV-2, which causes COVID-19, have been found in different parts of the world. This pandemic has so far infected more than one hundred and thirty five million people, which has caused significant damage to the education sector. The majority of students around the world have lost access to face-to-face classes. While dealing with the crisis, some higher education institutions are still finding it difficult to adapt to alternative ways of imparting education. Many of them are using learning management systems and other online technologies and tools to facilitate online learning. The aim of this manuscript is to propose a cost-effective hybrid teaching model (CeHTM) for the King Abdulaziz University. The proposed model is designed after analyzing two anonymous online feedback surveys in which nearly four thousand students and more than four hundred instructors have participated. The CeHTM is novel as it is the first framework of its kind for imparting education during pandemic. Given the uniformity of educational system in Saudi Arabian universities, the proposed model can be used by other Saudi Arabian institutions, and adapted elsewhere, especially in the Middle East and North Africa.

COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task.