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Named entity recognition (NER) in code-mixed documents, which have different languages, is hard for natural language processing. In this paper, we use hugging face's multilingual transformers to come up with a way to do code-mixed NER without any problems. Our work tries to solve the problems that come up when you try to recognise named entities in more than one language within the same text. We did thorough tests by fine-tuning the multilingual transformer model on a dataset with mixed codes. With an F1-score of 0.85, we got great results. This works better than previous methods and proves that our model can accurately find named items. We also examine at how well the model works with other language pairs and code-mixed patterns. This shows how well the model can handle different language situations. Our study helps us understand how to handle data in multiple languages, makes code-mixed NER techniques better, and shows how multilingual transformers can help break down language barriers. The research has implications for areas that need to understand more than one language, such as analysing social media, creating language-specific customer service systems, and finding information across languages. Speaking different languages can communicate more easily and effectively in these fields, which encourage inclusion.

Smart Sensing has shown notable contributions in the healthcare industry and revamps immense advancement. With this, the present smart sensing applications such as the Internet of Medical Things (IoMT) applications are elongated in the COVID-19 outbreak to facilitate the victims and alleviate the extensive contamination frequency of this pathogenic virus. Although, the existing IoMT applications are utilized productively in this pandemic, but somehow, the Quality of Service (QoS) metrics are overlooked, which is the basic need of these applications followed by patients, physicians, nursing staff, etc. In this review article, we will give a comprehensive assessment of the QoS of IoMT applications used in this pandemic from 2019 to 2021 to identify their requirements and current challenges by taking into account various network components and communication metrics. To claim the contribution of this work, we explored layer-wise QoS challenges in the existing literature to identify particular requirements, and set the footprint for future research. Finally, we compared each section with the existing review articles to acknowledge the uniqueness of this work followed by the answer of a question why this survey paper is needed in the presence of current state-of-the-art review papers.

The energy production in Pakistan is highly dependent on fossil fuels. Consequently, the least grid power with maximal renewable energy must be utilized to meet load demand. It can only be accomplished by medium- and small-scaled power consumers through installing hybrid systems on their sites. The generated excess electricity can be injected into national grid through net metering (NM) for financial assistance. The main objective of study is to design grid-connected hybrid (PV–wind) microgrid system using NM facility. The site selected for proposed work is an academic building of Mehran University of Engineering and Technology, Pakistan. The meteorological data collected from NASA were placed into HOMER to evaluate availability of RE sources. The load profile of building was calculated to measure energy consumption for 24 h/day. The on-grid PV–wind hybrid system has been analyzed before and after using NM facility. The simulation results before NM have obtained NPC, COE, payback period and CO2as 125,158 $, 0.0700 $/kWh, 2.86 years. and 20,175 kg/years, respectively. These same constraints by using NM facility have been estimated as 50,521 $, 0.0238 $/kWh, 2.79 years and 14,733 kg/years, respectively. The NM strategy is most preferable due to its feasible outcomes and accomplishment of distinct energy demands adequately.

With the increase in the generation and spread of textual content on social media, natural language processing (NLP) has become an important area of research for detecting underlying threats, racial abuse, violence, and implied warnings in the content. The subtlety and ambiguity of language make the development of effective models for detecting threats in text a challenging task. This task is further complicated when the threat is not explicitly conveyed. This study focuses on the task of implied threat detection using an explicitly designed machine-generated dataset with both linguistic and lexical features. We evaluated the performance of different machine learning algorithms on these features including Support Vector Machines, Logistic Regression, Naive Bayes, Decision Tree, and K-nearest neighbors. The ensembling approaches of Adaboost, Random Forest, and Gradient Boosting were also explored. Deep learning modeling was performed using Long Short-Term Memory, Deep Neural Networks (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). Based on the evaluation, it was observed that classical and ensemble models overfit while working with linguistic features. The performance of these models improved when working with lexical features. The model based on logistic regression exhibited superior performance with an F1score of 77.13%. While experimenting with deep learning models, DNN achieved an F1score of 91.49% while the BiLSTM achieved an F1score of 91.61% while working with lexical features. The current study provides a baseline for future research in the domain of implied threat detection.

In the current era, a new class of lightweight, silent, and all-electric aircraft that can take off and land vertically is about to transform mobility in major urban centers. Safer and quieter than helicopters, and operating in some cases without a pilot, they can supplement land mobility. Indeed, urban air mobility (UAM) has recently been a hot topic for debate and scientific research. However, it requires new types of service and technology and a novel business model. For a successful implementation, this paper provides the most insightful knowledge and actors, focusing on demand assessment through current research advancements in the area of urban air mobility. For that purpose, bibliometric analysis has been conducted, using a four-step methodology based on the search of the most frequently used keywords. The research work investigated the needs of the main stakeholders and illustrated how UAM can mitigate the pressure put on decision-makers regarding the mobility demand. The results show recent trends in scientific publications and citations, most cited articles, countries, and organizations that are more involved in this research subject, keyword co-co-occurrence analysis to identify and analyze current research areas and their associated barriers and challenges, using the VOSviewer software and WoS analytic feature.

Smart transportation, powered by IoT, transforms mobility with interconnected sensors and devices collecting real-time data on traffic, vehicle locations, and passenger needs. This fosters a safer and more sustainable transportation ecosystem, optimizing traffic flow and enhancing public transit efficiency. However, security and privacy challenges emerge in smart transportation systems. Our proposed solution involves a deep neural network (DNN) model trained on extensive datasets from sustainable cities, incorporating historical information like traffic patterns and sensor readings. This model identifies potential malicious nodes, achieving a 90% accuracy rate in predicting threats such as Denial of Service 88%, Whitewash attacks 80%, and Brute Force attacks 75%. This high precision ensures the security and privacy of passenger vehicle data and routes.

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.

Telemedicine systems have been rapidly increasing in number and size over the past decades. This increasing trend intensifies the need for tele-medicine in the area of national healthcare system. The increasing size of distributed telemedicine systems creates problems of data integration, vendor lock-in, and interoperability. In this paper, we present a next generation telemedicine system architecture, which is based on service oriented architecture (SOA). We believe SOA can resolve several problems in today’s telemedicine systems. In the current infrastructure of telemedicine, several problems have been identified such as users becoming limited to a single vendor. Therefore, we performed requirements analysis and evaluation to propose a robust solution to avoid the problem of vendor locking and interoperability.

Online analytical processing (OLAP) is considered as a database prototype that gives a platform for the rich analysis of multidimensional data. A logical data structure known as the data cube often supports the OLAP. However, mining association rules from multidimensional data using OLAP techniques with data mining facilities is an issue of substantial complexity. In practice, the complexity is excited by the existence of dimension hierarchies that subdivide dimensions into aggregation layers of various granularity. Discovery of hierarchy-sensitive association rules can be very costly on large cubes. In this paper, we present an OLAP hierarchy-sensitive framework that supports the efficient and transparent manipulation of dimension hierarchies for extracting association rules from data cube. The experimental results show that, when compared to the alternatives, very slight overhead is required to handle streams of inter-dimensional association rules requests.