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The user's intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user's complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach and explores user intent types based onmultidimensional online behavior in information acquisition. This research explores information search, verification, and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning. The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation. User feedback is based on online behavior and practices collected by using a survey method. The participants include both males and females fromdifferent occupation sectors and different ages. The data collected is subject to feature engineering, and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics. Different techniques are evaluated, and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136. Feature average is computed to identify user intent type characteristics. The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%. This research successfully extracts different user types based on their preferences in online content, platforms, criteria, and frequency. The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater. [ABSTRACT FROM AUTHOR]

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes. [ABSTRACT FROM AUTHOR]

[Display omitted] • Maximum removal of Congo Red on black cardamom activated carbon occurs at pH 6. • Langmuir adsorption capacity was found to be 69.93 mg/g. • Uptake of Congo Red follows pseudo-second-order kinetics. • Congo Red adsorption was found to be spontaneous, random, and exothermic. • Predictions of Congo Red adsorption by Machine Learning models were accurate and generalized (R2 > 0.99). The present work utilizes waste black cardamom (BC) as an inexpensive and environmentally friendly adsorbent for sequestering the Congo Red (CR) dye from aqueous media for the first time. Following a carbonization process at 600 °C, chemical activation with KOH was carried out for waste BC and subsequent black cardamom activated carbon (BCAC) was employed as an absorbent for CR eradication. The effect of experimental factors, including pH, adsorption time, dose and CR initial concentration, was investigated. 96.21 % of CR dye removal was achieved at pH 6 for 100 mg/L of CR concentration having 0.1 g dose at 30 °C. Maximum Langmuir adsorption capacity of BCAC was found to be 69.93 mg/g at 30 °C. The kinetic analyses showed that the CR adsorption over BCAC behaved in accordance with a pseudo-second order kinetic model as high R2 values (0.997–1) were obtained. Thermodynamic parameters (ΔH°, ΔS°, and ΔG°) demonstrated that the CR adsorption over BCAC was feasible, spontaneous and exothermic in nature. In addition, the state-of-the-art machine learning (ML) approaches namely, support vector regression (SVR) and artificial neural network (ANN) were employed for modeling the BCAC adsorbent for CR removal. The statistical analysis revealed high prediction performance of SVR model with AARE value of 0.0491 and RMSE value of 0.4635 while the corresponding values for the ANN model were 0.0781 and 0.5395, respectively. Furthermore, the plots between experimental CR data and ML forecasted data were closely matched (R2 > 0.99). Thus, it can be concluded that BC, an agro waste could be utilized for CR removal and that the adoption of ML approaches can benefit users by providing them with a tool to enhance the design and performance of wastewater treatment operations. [ABSTRACT FROM AUTHOR]

The forecast of river flow has high great importance in water resources and hazard management. It becomes more important in mountain areas because most of the downstream populations have high dependency for their livelihood, agriculture, and commercial activities like hydro power production. In this context, in recent times, machine learning models have got high attention due to their high accuracy in forecasting through self-learning from physical processes. In this work, we consider the potential of a data driven methods of machine learning, namely multilayer perceptron (MLP), support vector regression (SVR), and random forest (RF), are explored to forecast Hunza river flow in Pakistan using in-situ dataset for the period from 1962 to 2008. A set of five input combinations with lagged river flow values are developed based on the autocorrelation (ACF) and partial autocorrelation function (PACF) on historical river flow data. A comparative investigation is conducted to assess the performance of MLP, SVR and RF. The results of machine learning models are compared using forecasting metrics defined as correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE) between the observed and forecasted river flow data to assess the models' effectiveness. The results show that RF performed the best followed by MLP and SVR. In measurable terms, superiority of RF over MPL and SVR models was demonstrated by R2 = 0.993, 0.910, and 0.831, RMSE = 0.069, 0.084, and 0.104, MAE = 0.040, 0.058, and 0.062, respectively. The RF model performed 33.6% better than SVR and 17.85% to MLP. The results strengthen the argument that machine learning algorithms/models particularly RF model can be used for forecasting rivers flow with high accuracy which will further improve water and hazard management. [ABSTRACT FROM AUTHOR]

Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better expert system for prediction is a continuous exertion for hydrological studies. In this study, a deep neural network, namely a one-dimensional convolutional neural network (1D-CNN) and extreme learning machine (ELM) are explored for one-step-ahead streamflow forecasting for three-time horizons (daily, weekly and monthly) in Gilgit River, Pakistan. The 1D-CNN model gained incredible popularity due to its state-of-the-art performance and nominal computational complexity; while ELM model performed superfast as compared to traditional/deep learning architecture, gives comparable performance with fast execution rate. A comparative analysis is presented to assess the performance of the 1D-CNN related to the ELM model. The performance measurement matrices defined as the correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) computed between the observed and predicted streamflow to evaluate the 1D-CNN and ELM model efficacy. The results indicated that the ELM model performed relatively better than the 1D-CNN model based on predefined statistical measures in three-time scale. In numerical terms, the superiority of ELM over 1D-CNN model was demonstrated by R2 = 0.99, MAE = 18.8, RMSE = 50.14, and R2 = 0.97, MAE = 136.59, RMSE = 230.9, for daily streamflow (testing phase) respectively. Based on our findings, it can be concluded that the ELM model would be an alternative to the 1D-CNN model for highly accurate streamflow forecasting in mountainous regions of the world. [ABSTRACT FROM AUTHOR]

In this research work, a 40-km2 SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition ® program's Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier. [ABSTRACT FROM AUTHOR]

Angle-of-arrival (AoA) estimation is of great interest, particularly for using radio to localize a device; good estimates of angles result in good estimates of location. In this letter, we propose a signal processing and machine learning combined tool for the AoA estimation. In particular, we utilize regression models trained using the snapshot data collected using multiple antennas for estimating the angle of arrival. Based on a set of simulation and real measurements underthe Bluetooth 5 low-energy system in an indoor environment, the proposed method is able to provide a considerable and consistent improvement without significant additional computational effort. We show that the proposed approach for AoA estimation provides an improvement of at least 20% compared with the baseline approach of traditional Multiple Signal Classification algorithm. We evaluate the performance of the proposed methods and show a consistent improvement using a range of channel parameters, including elevation angles, SNRs, and channel configurations. [ABSTRACT FROM AUTHOR]