Smart building technology incorporates efficient and automated controls and applications that use smart energy products, networked sensors, and data analytics software to monitor environmental data and occupants’ energy consumption habits to improve buildings’ operation and energy performance. Smart technologies and controls are becoming increasingly important not only in research and development (R&D) but also in industrial and commercial domains, leading to a steady growth in their application in the building sector. This study examines the literature on SBEMS published between 2010 and 2020 with a systematic approach. It examines the trend with the annual number of the published studies before exploring the classification of publications in terms of factors such as domain of SBEMS, control approaches, smart technologies, and quality attributes. Recent developments around the smart building energy management systems (SBEMS) have focused on features that provide occupants with an interface to monitor, schedule, and modify building energy consumption profiles and allow a utility to participate in a communication grid through demand response programs and automatic self-report outage functionality. The study also explores future research avenues, especially in terms of improvements in privacy and security, and interoperability. It is also suggested that the smart building technologies’ smartness can be improved with the help of solutions such as real-time data monitoring and machine learning

This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system’s performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business.

Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the datasets and data pre-processing. The study compiles a new comprehensive public set of training datasets for building occupancy profile prediction with over 40,000 records. To the best of our knowledge, it is the largest dataset to date, with the most realistic and challenging setting in building occupancy prediction. Furthermore, to the best of our knowledge, this is the first study that attained a robust occupancy count by considering a multimodal input to a single output regression model through the mining and mapping of feature importance, which has advantages over statistical approaches. The proposed solution is tested in a living room with a prototype system integrated with various sensors to obtain occupant-surrounding environmental datasets. The model’s prediction results indicate that the proposed solution can obtain data, and process and predict the occupants’ presence and their number with high accuracy values of 99.7% and 99.35%, respectively, using random forest.

An ICT Framework for Tourism Industry of Nepal: Prospect and Challenges ICT;information access;internet;data center;tourism industry; Information and Communication Technology (ICT) has revolutionized the world and has profound impact on the social and economic development of a country. Implementation, practice and accessibility of ICT is viewed as an integral part of any countries' strategy today. These new technologies are becoming popular due to their ability to produce, distribute and provide instant access to massive information in no time. ICT has pervaded almost every aspect of human endeavor that may include health, education, economics, governance, entertainment etc. Tourism is one such vital industry that find enormous application of ICT in its strategic and operational level, to promise long term benefits and enhance economic growth. Tourism industry in western world and some developed countries of Asia have applied ICT for more than 30 years, and have gained tremendous benefits. Nepal which is also growing as one of the favourite tourist destinations lacks proper implementation of ICT in this industry. In our study we examined how the ICT can play a vital role in developing the tourism industry of Nepal. This study is an exploratory research based on primary data collected from tourist visiting Nepal, supported by information from tour operators, government agencies, NGOs and INGOS. A framework is devised on the basis of data and information collected and finally, discussions elaborate on the prospect and challenges of implementation of ICT in tourism industry of $Nep{\grave{a}}l$.

KSII Transactions on Internet and Information Systems TIIS Paper Details