The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.

Mobile Learning (M-Learning), driven by technological digital advancement, is one of the essential formats of online learning, providing flexibility to learners. Cloud-based mobile learning (CBML) provides value additions by providing an economic alternative to E-learning. Revolutionary changes in smartphone design and features have enhanced the user experience, thus encouraging mobile learning. During the COVID-19 pandemic, E-Learning and M-Learning allowed continuing education to occur. These methods continue to offer more opportunities to learners than constrained face-to-face classroom learning. There are many main critical success factors (CSFs) and subfactors that play an influential role in sustainable M-Learning success. The current study focuses on the assessment and ranking of various main factors and subfactors of CBML. Analytic hierarchy process-group decision-making (AHP-GDM)- and fuzzy analytic hierarchy process (FAHP)-based methodologies were used to evaluate and model the main factors and subfactors of CBML in crisp and fuzzy environments. Higher education institutes must strive to address these main factors and subfactors if they are to fulfill their vision and mission in the teaching–learning system while adopting sustainable M-Learning.

Data Mining Application in Classification Scheme of Human Subjects According to Ayurvedic Prakruti – Temperament

For information technology (IT) projects to be successful, it is imperative to identify the critical success factors (CSFs) that are essential to the IT project management process. This study aims to illuminate these CSFs by incorporating insights from professionals involved in the management of IT projects. The major contribution of this study lies in classifying these factors into different categories and quantifying their impact while taking IT project management experts’ viewpoints into account. To accomplish this, a thorough assessment of the literature was carried out, which resulted in the identification of 20 success factors that fall into five categories: project team capability, top management support, project quality, project management, and organizational process assets (OPAs). A survey covering these variables was then sent to professionals in the IT industry, and answers were obtained from five experts. The collected data was analyzed using the fuzzy analytic hierarchy process (FAHP) method, which mitigates imprecision and avoids bias in the data. The most important factor was determined to be project team capability, whereas organizational process assets were deemed to be the least significant factor. It is anticipated that the insight garnered from this study will help IT project management professionals in supporting, evaluating, and improving project success.