Contemporary automated speech analysis systems face several fundamental limitations, including the fragmented analysis of linguistic and emotional characteristics, a focus on a limited set of basic emotions, and insufficient adaptation to diverse language systems. This study proposes a comprehensive approach to speech feature analysis, consisting of two complementary components designed to overcome these limitations. The proposed framework comprises two complementary methods. The first method is designed to assess the level of speech activity and is based on a comprehensive analysis of 36 linguistic parameters. These parameters include quantitative features (mean utterance length, number of sentences), syntactic features (construction complexity), lexical diversity (vocabulary richness), as well as the frequency of various parts of speech. The MannWhitney U test is employed to classify the level of speech activity. The second method is a stateof-the-art multi-task neural network architecture based on the pre-trained RuBERT-large language model. This architecture is capable of simultaneously evaluating three fundamental parameters of emotional state: valence, arousal, and dominance. The combination of these parameters enables the identification of 26 complex emotional states. Experimental validation on heterogeneous Russianand English-language corpora, including professional speech and spontaneous dialogues, demonstrated the superiority of the proposed methods over existing analogues. The speech activity assessment method achieved an accuracy of 92% for English and 89% for Russian. The multi-task model attained an accuracy of 85% in determining valence, 80% for arousal, and 76% for dominance. For 10 main emotional categories, the classification accuracy reached 57%. The results of this study can be applied to the development of intelligent dialogue systems and chatbots capable of adapting their communication style and emotional responses based on the user's speech activity and emotional state, thereby significantly enhancing the quality and naturalness of interaction.

Recent advancements in information technology have improved the design and development of disease prediction systems significantly. The disease prediction system is useful in diagnosing diseases by analyzing medical data. In this digital world, disease prediction systems are extremely important, especially during pandemic situations when physicians are in high demand andpeople are unable to reach hospitals to monitor and diagnose their health conditions. Many medical expert systems and disease prediction systems have been published in recent years. Still, there is a gap for people to have an effective disease prediction system to predict a patient's disease and severity level at the right time. Predicting the impact level of disease in the human body is considered one of the most difficult issues nowadays due to the increase in voluminous medical data with various new symptoms.

In this manuscript, we expand the concept of generalized altering distance function and introduced a generalized (𝛼ƒ − 𝛽𝜑) −contractive mappings and give fixed point theorems for such contractions in metric spaces. Some existing results of literature are also provided which are direct consequences of our main results.

Purpose of the study – the purpose of this study is to investigate the changing scenario of digital entrepreneurship across the world and in Hyderabad specifically and understanding the pros and cons on the phenomenon and the opportunities that are available for these startups and the challenges being faced by them in the volatile, uncertainty, complex and ambiguous (VUCA) environment Design/Methodology/Approach – a descriptive study of Hyderabad based select organizations is conducted using a online and social network analysis. The study is based on qualitative data, complemented by quantitative data. These select startups are investigated within its time, providing an integrative approach to analysis. Research limitations/ Implications – This research brings implications to startup based in Hyderabad only that are aiming to transform their businesses towards greater digitalization, and drives on the importance and sustainability the other segments of India are not captured which is also a limitation of the study Originality/Value – The novelty of this research is related to how external factors contribute to a these select startups to adapt and create value, and how these Hyderabad based digital startups may exploit opportunities by configuring internal resources and external factors with the support of information technology. The study considers digital entrepreneurship in dynamics, distinguishes between different levels of digitalization, and prescribes them different enablers in achieving sustainability

Entrepreneurship is critical to any nation's economic growth. Societies that have both a high concentration of entrepreneurs and the economic and legal framework necessary to support and inspire entrepreneurial endeavors tend to be the most prosperous. More than 66% of India's population lives in rural areas, therefore encouraging entrepreneurship there is crucial to the country's rise to economic superpower status. Self-Help-Group (SHG) women business owners are the subject of this research. In this investigation, researchers used a variety of approaches. Primary we gathered quantitative and qualitative information from 58 people in 20 SHGs spread out over 5 districts in Telangana, India. Literature analysis found that selling their wares was the greatest problem for rural women business owners. They should put a premium on product branding and packaging as part of their marketing strategy. This study was motivated by a need to fill a knowledge gap in the marketing of SHG goods. The research showed that SHG's product is in demand all throughout India at all times of the year. Unfortunately, they aren't keeping tabs on product demand and are stuck on antiquated marketing strategies like the SARAS exhibition, weekly haats, and orders through agents. They've never had any experience with internet marketing. The product's marketing severely lacks. Entrepreneurs in rural areas are just as ambitious and curious as their urban counterparts. A gap in the promotion of products created by rural entrepreneurs might be closed with the help of digital marketing. Until rural India is digitalized, the Indian government's ambition of Digital India would remain unfulfilled.

In a city like Ahmedabad, vehicles travel on road all day as compared to other districts of Gujarat. In Ahmedabad Traffic includes a different type of transportation mode on road like bikes, scooters, cars, trucks, auto-rickshaws, BRTS, AMTS, etc. So queue clearance is an important factor which helps to increase the flow of vehicles. Optimization of queue clearance can be achieved with help of proper channelization of traffic, road marking, and improvisation in traffic signals. It helps to reduce the delay of vehicles and proper management of the platoon. This can be achieved by analyzing the traffic parameters at an intersection under mixed traffic conditions with help of macroscopic traffic parameters and Microscopic traffic parameters. In this study first of all I will evaluate the present condition of intersection by conducting a pilot survey for getting the basic idea of the queue length and queue clearance time at Dudheshwar Y (3-leg) intersection and Dafnala crossroad (4-leg intersection). After that collected data and analysis of that data will help to formulate and solution of a new algorithm for queue management with help of Regression analysis of the collected data from different surveys and simulation by PTV VISSIM software.

A Monowave synthetic route for the production of Palladium nanoparticles using Gum karaya(GK) is examined for in vitro cytotoxic efficacy against MCF-7. The present work proves the influence of gum karaya composition on the size and morphology of the produced Palladium nanoparticles. The biosynthetic method is quick, simple and easily synthesized and color changes observed in Ultraviolet and visible spectroscopy. Subsequently, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR) and Transmission electron microscopy (TEM) analyses were employed to characterize the as synthesized GKPdNP. The XRD investigation confirmed the presence of Pd (0) nanoparticles and interactions between the bio degradable compounds of the Gum and the produced GKPdNP were confirmed by the FTIR spectra. TEM images demonstrated that the nanoparticles has a size distribution of 3±2 nm. Moreover, the anticancer activity of the GKPdNP was tested against MCF-7 strains. All synthesized Pd nanoparticles exhibited good antioxidant activity.

The Growing world population and the resulting higher consumption of products and services has driven a rapid increase of organic wastes originating from households, industry and agriculture. This situation generates serious environmental issues, calling for safe and sustainable strategies to treat these wastes, from their production to their recycling or elimination. On the other hand, the organic fraction of wastes represents a valuable organic resource, which could be recycled and transformed into nutrient-rich fertilizer and/or soil conditioner. Biological degradation during composting is one of the strategies to transform organic wastes into organic products. Composting is a time consuming process 1 including degradation of waste by the microbial population converting them into a stable organic fertilizer. Hence, a considerable research in the field of rapid composting is to be done so as to accelerate the composting process. The different authors had discussed the methodologies used previously and gave outline of some modified technologies. This review is summarized from study of literature regarding composting process highlighted in various journals, papers, relevant text books, relevant websites and proceedings of national and international conferences. It describes the best and optimum additives as well as techniques enhancing the composting process by reducing the period of composting and hence achieving rapid composting. The purpose of this review is to get an overview of current approaches related with rapid composting and other treatment techniques which were used for normal composting so that various suitable techniques can be merged in combination in order to generate a new technique which will be beneficial for reducing the duration of composting.

Viscosity (5), Density (𝜌) and Ultrasonic velocity (U) of the binary system of Diethylenetriamine (DETA) and 2-Ethoxy Ethanol are measured over entire composition range. These measurements are done at constant temperature 296 K and at frequencies 1MHZ, 3MHZ & 5MHZ. The acoustic parameters computed using these data values are intermolecular free length (𝐿ƒ), Rao constant (𝑅), Wada’s constant (W), adiabatic compressibility (𝛽𝑎𝑑), excess intermolecular free length (𝐿𝐸), excess adiabatic compressibility (𝛽𝐸 ) and excess ultrasonic velocity (𝑈𝐸). These parameters have to be interpreted in terms of intermolecular interactions at frequencies 1MHZ, 3MHZ & 5MHZ at constant temperature296K. This work aims to evolve the impact of ultrasonic sound over the molecular interaction of binary liquid mixture quoted above at the said conditions.

The Internet of Things (IoT) is fundamentally a system which contains smart objects with different sensors, networks, and processing technologies. IoT is regarded as to change the way internet works and carries altogether the areas like machine-to-machine communication, artificial intelligence, big data etc. to work underneath the same umbrella such that cyber space and human (physical systems) are more entangled and hence offers ubiquitous computing giving rise to cyber physical systems. Hence IoT is an integrated system working collectively to provide smart services to the end-users. IoT offers great benefits to us. It provides an environment where smart services are provided to use any activity anytime & anywhere. These smart services are provided through different applications running in the IoT environment. IoT applications examine & consequently help in quick decision-making process for client management. In the present work, application of IoT in the field of healthcare systems is discussed. In any healthcare system, the use of IoT technologies brings convenience to both physicians as well as patients. IoT applications assist in various medical areas like continuous real-time monitoring, management of patient information, management of blood banks etc.

With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. For example, a personalized route query is issued by providing some clues that describe the spatial context between PoIs along the route, where the result can be far from the optimal one. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-and-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures.

In current conditions, information flows are complicated by unreliable and malicious data, negatively impacting management systems and information security. Tools are needed to filter information before it is processed. The increase in false and malicious messages requires effective algorithms for analyzing and managing data, ensuring the resilience of automated systems. The purpose of this research is to create effective mathematical and computational methods for analyzing, classifying, and managing information to enhance the reliability of systems and the accuracy of data. The study proposes a simulation modeling method based on a mathematical model incorporating elements of probability theory, where the information flow is divided into reliable, false, and malicious information. Probabilistic methods are applied for message classification, considering prior and posterior probabilities, as well as the analysis of network, temporal, and semantic characteristics. Unlike existing methods, this approach focuses on data analysis before their use, which reduces the risk of destructive impacts. As a result, a mathematical model has been developed for analyzing information flows that include reliable, false, and malicious information. The model employs probabilistic approaches and considers the network, temporal, and semantic characteristics of messages for their classification and minimization of destructive influence. It allows for effective consideration of the peculiarities of each source, distinguishing reliable, false, and malicious messages, thereby ensuring high accuracy and reliability of the resulting information flow. The results can be used to monitor information threats, filter out malicious information, and ensure the security of critical systems, as well as support decision-making in government agencies, the economy, and the energy sector, increasing trust in information systems. The model is planned to be developed by integrating more complex machine learning methods, such as deep neural networks, and analyzing its adaptability in realtime conditions.

In the current paper, the authors propose an Interaction-Aware Maternal Health Outcome Estimation (IAMHOE) framework that assesses the determinants that influence maternal health outcomes based on the district-level Health Management Information System (HMIS) data. The model takes into account the studies of antenatal care (ANC), institutional deliveries, literacy, and diffusion of IT applications, with interaction terms added to the model to address the moderating effects. The dataset has been cleaned up before estimation, accounted for the missing cases, standardized on similar metrics, and added interaction variables to enhance the accuracy of the model. The main analytical tool was based on a fixed-effects regression model, hypothesis testing, and probability prediction. The outcome of the empirical results illustrates some trends. There are declining marginal effects of IT diffusion on institutional deliveries, which can be interpreted as indicating that infrastructural development will not experience substantial benefits unless there are conditions that encourage them. ANC coverage, on the other hand, has a positive amplification effect mediated by literacy, whereby higher education levels enhance healthcare utilisation. The probability analysis also suggests that hospitals are increasingly moving towards singlevendor sourcing to integrated vendor approach with increased IT integration. Integrating the interaction modeling with the powerful validation and predictive methods, IAMHOE offers something novel, reminding us that the maternal health outcomes require interdependence not only between the service delivery but also between the investments into education and digital infrastructure.