Energy plays a pivotal role for economic development of a country. A reliable energy source is needed to improve the living standards of people. To achieve such a goal, governments and industries are trying to install a new energy infrastructure called the “Smart Grid”. This helps to manage the electricity generation and distribution in an efficient manner. Buildings and other structures are the biggest consumers of electricity. There is a need to reduce the energy consumption so that the resources can be utilized efficiently. Therefore, in this paper, we give a comprehensive state-of-the-art on various recent techniques and solutions which provide energy savings in smart homes and buildings. This includes statistical models, cloud computing based solutions, fog computing and smart metering based architectures, and several other IoT (internet of things) inspired solutions. We also present a hypothetical model that treats energy supply and usage in buildings as a self-managing energy system (SES). This paper is concluded by highlighting several open issues and challenges related to energy management in buildings.

Software testing is an important task in software development activities, and it requires most of the resources, namely, time, cost and effort. To minimize this fatigue, software bug prediction (SBP) models are applied to improve the software quality assurance (SQA) processes by predicting buggy components. The bug prediction models use machine learning classifiers so that bugs can be predicted in software components in some software metrics. These classifiers are characterized by some configurable parameters, called hyper-parameters that need to be optimized to ensure better performance. Many methods have been proposed by researchers to predict the defective components but these classifiers sometimes not perform well when default settings are used for machine learning classifiers. In this paper, software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization. For this purpose, seven machine learning classifiers, such as support vector machine Radial base function (SVM-RBF), K-nearest neighbor (KNN) (Minkowski metric), KNN (Euclidean metric), Naive Bayes (NB), Decision Tree (DT), Linear discriminate analysis (LDA), Random forest (RF) and adaptive boosting (AdaBoost), were used. The experiment was carried out on bug prediction dataset. The results showed that hyperparameter optimization of machine learning classifiers, using AIN and its applications for software bug prediction, performed better than when classifiers with their default hyper-parameters were used.

With improvement in AI and deep learning, Human Activity Recognition (HAR) has started to play an important role in many real-world applications. These range from healthcare and elderly care to security to enhancing user experience in smart environments. Smart devices with their ubiquity and multi-modal sensors, provide critical data that can be used to improve the quality of life and efficiency of services. Given the rapid technological advancements and recent developments in the field, it is essential to examine the current state of Human Activity Recognition (HAR), highlighting its strengths as well as the challenges it continues to face. This study presents a systematic review of the literature (SLR) of HAR that is conducted through a detailed study of articles published from 2021 to 2024 using open data sets. This study explores major databases i.e., Springer, IEEE Xplore, Science Direct, Taylor & Francis, and ACM to access the recent advancements in HAR that are facilitated by smartphones and smartwatches. Our SLR looks at the most commonly used datasets i.e. WISDM, PAMAP2, UCI-HAR, Opportunity, and MHealth. Moreover, this SLR compares the performance of commonly used deep learning techniques i.e. Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), Transformers and Ensemble models on these datasets. The objectives of this study are threefold i.e. to identify recent studies that employ deep learning techniques for Human Activity Recognition (HAR) tasks, to review publicly available open datasets commonly used in HAR research during 2021 to 2024, and to identify common challenges and limitations in current HAR research while proposing potential future research directions.

Crowdsourced spatial analytics is a rapidly developing field that involves collecting and analyzing geographical data, utilizing the collective power of human observation. This paper explores the field of spatial data analytics and crowdsourcing and how recently developed tools, cloud-based GIS, and artificial intelligence (AI) are being applied in this domain. This paper examines and discusses cutting-edge technologies and case studies in different fields of spatial data analytics and crowdsourcing used in a wide range of industries and government departments such as urban planning, health, transportation, and environmental sustainability. Furthermore, by understanding the concerns associated with data quality and data privacy, this paper explores the potential of crowdsourced data while also examining the related problems. This study analyzes the obstacles and challenges related to “geospatial crowdsourcing”, identifying significant limitations and predicting future trends intended to overcome the related challenges.

The public transport system plays an important role in a city as it moves people from one place to another efficiently and economically. The public transport network must be organized in a way that will cover as many places and as much of the population as possible, and support the city’s growth. As one of Australia’s largest capital cities, Melbourne is growing and expanding its metropolitan area to reflect the growth in population and an increased number of activities. To date, little research has been conducted to determine the accessibility and adequacy of public transport taking into consideration the blank spot areas, the number of public transport options for each area, the population density within specific geographical areas, and other issues. In this study, a new measurement model is developed that examines public transport in residential areas and the extent to which it is adequate for the various local government areas (LGAs). An accessibility approach is adopted to evaluate the accessibility of different types of public transportation in residential areas in metropolitan Melbourne, Victoria, Australia. The results show that in most LGAs, the number of blank spots will decrease as the population density increases. This indicates that residents in lower-density areas will have less accessibility to public transportation. However, there is no indication that there is a greater level of services (such as more night-time and weekend public transportation services) in the high-density areas. This research is significant as it will point to and help to improve the areas with inadequate public transportation and other issues, taking into consideration their geographical locations and population density.