A Typical Review of Current and Prospective Microwave and Optical Remote Sensing Datasets for Soil Moisture Retrieval

Soil Moisture content is a vital indicator of both the weather and the water cycle. It has been a long-standing difficulty for the field of remote sensing to make sense of soil moisture's spatial and temporal distribution. For over five decades, researchers across the world have exclusively investigated the optical and microwave datasets for estimating soil moisture by developing various models, and algorithms. Nevertheless, challenges are faced in the consistent retrieval of SM at local, and global scales with higher accuracy in space and time resolution. The review was conducted in-depth, looking at the methods using optical and microwave data to determine soil moisture, and outlining the benefits and drawbacks considering the current needs.  With this research, a new age of widespread use of space technology for remote sensing of soil moisture has been ushered in. The study also acknowledges the scientific challenges of utilizing remote sensing datasets for soil moisture measurement

The Analysis of the Impact of Yoga on Healthcare and Conventional Strategies for Human Pose Recognition

Human pose estimation is a profound, established computer vision issue that has uncovered numerous past difficulties. Breaking down human exercise is advantageous in multiple fields like surveillance, biometrics, and many healthcare applications. Workout with yoga poses is famous these days since yoga activities can expand adaptability and muscular quality, and the respiration procedure will be improvised. The yoga postures evaluation is hard to check, so specialists will most likely be unable to benefit from the exercises ultimately. IoT-based yoga frameworks are required for individuals who need to rehearse Yoga at home. A few studies are recommended camera-oriented or wearable gadget-oriented yoga posture finding strategies with more precision. Nonetheless, camera-based plans have security and privacy issues, and the wearable device-based methods are illogical in the earlier applications. To build such systems, one must have a strong foundation and current research in pose estimation. In this paper, first, the impact of Yoga on humans with various stress levels is analysed on the real-time data. Second, the comprehensive review of yoga posture recognition systems from machine learning to deep learning strategies and evaluation metrics discusse

Energy Efficient XPath Query Processing on Wireless XML Streaming Data

An energy efficient way of disseminating XML data to several mobile clients is broadcast. Information such as alert on emergencies, election results and sporting event results can be of interest to large number of mobile clients. Since eXtensible Markup Language (XML) is widely used for information exchange, wireless information services require an energy efficient XML data dissemination. XML Path (XPath) represents selective data required by mobile clients. XPath query processing involves two performance metrics, namely tune-in time and access time. In this paper, we propose a novel structure for streaming XML data called Path Stream Group Level (PSGL) node by exploiting the tree structure of XML document. It possesses various small indices such as level, child, sibling, attribute, text for selective download of XML data by mobile clients. It organizes data based on the level of XML document tree and groups XML elements with same XML path prefix to conserve battery power at mobile clients. Experimental results show that proposed method has reduced tune-in time when compared with existing approaches. Hence PSGL approach enhances performance with energy conservation for processing various types of XPath queries

Membrane Separation of Tannery Process Effluents and Investigations on Flux enhancement

Specific treatment schemes for three tannery effluent streams namely, pickling, degreasing and fat-liquoring are developed using a hybrid separation process involving gravity settling, coagulation and combinations of membrane based separation processes (e.g. nanofiltration followed by reverse osmosis for the fatliquoring effluent)

Event Centric Modeling Approach in Colocation Pattern Snalysis from Spatial Data

Spatial co-location patterns are the subsets of Boolean spatial features whose instances are often located in close geographic proximity. Co-location rules can be identified by spatial statistics or data mining approaches. In data mining method, Association rule-based approaches can be used which are further divided into transaction-based approaches and distance-based approaches. Transaction-based approaches focus on defining transactions over space so that an Apriori algorithm can be used. The natural notion of transactions is absent in spatial data sets which are embedded in continuous geographic space. A new distance -based approach is developed to mine co-location patterns from spatial data by using the concept of proximity neighborhood. A new interest measure, a participation index, is used for spatial co-location patterns as it possesses an anti-monotone property. An algorithm to discover co-location patterns are designed which generates candidate locations and their table instances. Finally the co-location rules are generated to identify the patterns.Comment: 9 page

WEB PAGE ACCESS PREDICTION USING FUZZY CLUSTERING BY LOCAL APPROXIMATION MEMBERSHIPS (FLAME) ALGORITHM

ABSTRACT Web page prediction is a technique of web usage mining used to predict the next set of web pages that a user may visit based on the knowledge of previously visited web pages. The World Wide Web (WWW) is a popular and interactive medium for publishing the information. While browsing the web, users are visiting many unwanted pages instead of targeted page. The web usage mining techniques are used to solve that problem by analyzing the web usage patterns for a web site. Clustering is a data mining technique used to identify similar access patterns. If mining is done on those patterns, recommendation accuracy will be improved rather than mining dissimilar access patterns. The discovered patterns can be used for better web page access prediction. Here, two different clustering techniques, namely Fuzzy C-Means (FCM) clustering and FLAME clustering algorithms has been investigated to predict the webpage that will be accessed in the future based on the previous action of browsers behavior. The Performance of FLAME clustering algorithm was found to be better than that of fuzzy C-means, fuzzy K-means algorithms and fuzzy self-organizing maps (SOM). It also improves the user browsing time without compromising prediction accuracy

User Experiences in Over-The-Top (OTT) Streaming Media Platform Services

Understanding user experiences on Over-the-Top (OTT) platforms is vital as these services continue to transform digital entertainment consumption. This study investigates user experiences in “Over-the-Top” (OTT) streaming media platform services, focusing on the interplay between user behavior, influencer credibility, user satisfaction, and continuous intention to use. A quantitative approach was employed, utilizing data from 384 respondents across four major OTT platforms: Netflix, Hulu, Amazon Prime Video, and Disney+. Current studies focus on user engagement without a framework of content recommendations and strategies for marketing. The majority of research on initial adoption intentions lacks an entire model that includes technological, social, and psychological aspects. Influencer marketing's immediate effect on OTT subscriptions has been little examined, mostly in e-commerce situations. To fill these gaps, our research applies stratified random sampling to select OTT customers by age, gender, and consumption frequency. Therefore, the study minimises demographic biases and enhances generalisability. The research finds that influencer credibility has a moderate positive correlation (r = 0.62) with user behaviour, confirming the significant role of social media influencers in influencing user interactions with OTT platforms. Moreover, findings indicate a perfect positive correlation (r = 1.00) between user satisfaction and user experience, highlighting satisfaction as a critical determinant of overall user perception. In addition, the study reveals that continuous intention to use OTT platforms displays a strong positive beta coefficient (0.95), indicating that active users typically describe outstanding overall experiences. The novelty of the study consists of the establishment of a viewer classification framework that divides users based on behavioural patterns, offering useful information for content personalisation and targeted marketing. OTT user adoption and engagement are examined thoroughly through social impact, personalisation, and habit building within the TAM model. This research addresses gaps in theory and practice, helping OTT platforms improve user experiences and loyalty

Examining the Impact of Self-Regulation through Rajyoga Lifestyle on Anger, Irrational Belief, Interpersonal Relations, and Mental Health in Adults.

This study investigates the impact of self-regulation through Rajyoga lifestyle on anger, irrational beliefs, interpersonal relations, and mental health among adults. Non-communicable diseases (NCDs) cause approximately 41 million deaths annually, primarily driven by stress and unhealthy lifestyle choices. Rajyoga, emphasizing self-regulation and emotional management, offers a holistic approach to improving mental and physical health. A quasi-experimental post-test only design was employed with 68 participants in the Rajyoga intervention group and 49 in the control group, aged 25-50 years. Standardized questionnaires assessed anger (STAXI), interpersonal relations (FIRO-F), irrational beliefs, and mental health, with data collected in person and online. Results indicated that the intervention group exhibited significantly lower State Anger (SA) and Trait Anger (TA) scores compared to the control group (p < .001), highlighting improved anger management. FIRO-F scores showed significant improvements in expressed importance, confidence, and affection, reflecting enhanced interpersonal relations. Although differences in irrational beliefs were observed, only catastrophizing showed a statistically significant reduction (p = 0.014). Mental health scores were significantly higher in the Rajyoga group (p = 0.004), demonstrating a positive impact on psychological well-being. These findings suggest that Rajyoga's self-regulation practices effectively reduce anger, enhance interpersonal skills, and improve mental health, though effects on irrational beliefs appear selective. The study underscores Rajyoga’s potential as a complementary therapy to enhance emotional and psychological health in adults