bluuebunny/crossref_metadata_2025 huggingface dataset:
An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications
Authors: Deepa S. , Arafat I.S. , Priya M. Sathya , Saravanan S.
Published: January 2024

The possible application of wireless sensor networks is hampered and the widespread use of this novel method is slowed, according to recent surveys conducted within the field of automation in industry, which identified that accuracy pertains indicate currently among the primary obstacles to the dissemination of wireless networking for recognizing and regulating applications. In order to overcome these constraints, it is necessary to raise public understanding of the reasons for dependability issues and the potential approaches to resolving them. Low-power communications of sensor nodes are, in reality, quite susceptible to adverse channel conditions and can readily be affected by transmissions of other co-located devices, making them seem unreliable. In this dissertation, I explore several strategies that may be used to either eliminate interference altogether or reduce its negative consequences. In this paper, we study the creation and modeling of a brand-new spectrum allocation mechanism for wireless sensor networks. Cognitive radio technology can detect spectrum holes in the environment, learn from its surroundings using artificial intelligence, adjust the system’s operating parameters in real-time, and use the secondary spectrum to increase efficiency. In this study, we present a reinforcement learning-based strategy for choosing the power of transmission and frequency that can help individual sensors learn from their prior decisions and those of their peers. Our suggested approach is multiple agents decentralized and adaptable to both the data needs from source to sink and the amount of energy that sensing devices in the network have left over. In comparison to different resource allocation algorithms, the results reveal a dramatic increase in the lifespan of the network.
Investigating privacy-preserving machine learning for healthcare data sharing through federated learning
Authors: Shaik Khaleel Ahamed  , Neerav Nishant  , Ayyakkannu Selvaraj  , Nisarg Gandhewar  , Srithar A  , K.K.Baseer
Published: January 2024

Privacy-Preserving Machine Learning (PPML) is a pivotal paradigm in healthcare research, offering innovative solutions to the challenges of data sharing and privacy preservation. In the context of Federated Learning, this paper investigates the implementation of PPML for healthcare data sharing, focusing on the dynamic nature of data collection, sample sizes, data modalities, patient demographics, and comorbidity indices. The results reveal substantial variations in sample sizes across substudies, underscoring the need to align data collection with research objectives and available resources. The distribution of measures demonstrates a balanced approach to healthcare data modalities, ensuring data fairness and equity. The interplay between average age and sample size highlights the significance of tailored privacy-preserving strategies. The comorbidity index distribution provides insights into the health status of the studied population and aids in personalized healthcare. Additionally, the fluctuation of sample sizes over substudies emphasizes the adaptability of privacy-preserving machine learning models in diverse healthcare research scenarios. Overall, this investigation contributes to the evolving landscape of healthcare data sharing by addressing the challenges of data heterogeneity, regulatory compliance, and collaborative model development. The findings empower researchers and healthcare professionals to strike a balance between data utility and privacy preservation, ultimately advancing the field of privacy-preserving machine learning in healthcare research.
Exploring advancements in deep learning for natural language processing tasks
Authors: Abhishek Pandey  , V Ramesh  , Puneet Mittal  , Suruthi  , Muniyandy Elangovan  , G.Deepa
Published: January 2024

This literature survey explores the transformative influence of deep learning on Natural Language Processing (NLP), revealing a dynamic interplay between these fields. Deep learning techniques, characterized by neural network architectures, have propelled NLP into a realm where machines not only comprehend but also generate human language. The survey covers various NLP applications, such as sentiment analysis, machine translation, text summarization, question answering, and speech recognition, scasing significant strides attributed to deep learning models like Transformer, BERT, GPT, and attention-based Sequence-to-Sequence models. These advancements have redefined the landscape of NLP tasks, setting new benchmarks for performance. ever, challenges persist, including limited data availability in certain languages, increasing model sizes, and ethical considerations related to bias and fairness. Overcoming these hurdles requires innovative approaches for data scarcity, the development of computationally efficient models, and a focus on ethical practices in research and application. This survey provides a comprehensive overview of the progress and obstacles in integrating deep learning with NLP, offering a roadmap for navigating this evolving domain.
Developing interpretable models and techniques for explainable AI in decision-making
Authors: Jayaganesh Jagannathan  , Dr. Agrawal Rajesh K  , Dr. Neelam Labhade-Kumar  , Ravi Rastogi  , Manu Vasudevan Unni  , K. K. Baseer
Published: January 2024

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.
Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach
Authors: Balaji V  , Purnendu Bikash Acharjee  , Muniyandy Elangovan  , Gauri Kalnoor  , Ravi Rastogi  , Vishnu Patidar
Published: January 2024

This study introduces a Semantic Framework for Categorizing IoT Agriculture Sensor Data, leveraging Machine Learning and Web Semantics. IoT sensors in agriculture generate vast real-time data on crucial factors like soil conditions and weather, promising optimization in resource use and crop yields. While machine learning aids data categorization, semantic aspects often remain unexplored. By combining machine learning with web semantics (RDF and OWL), this research establishes a structured framework that not only categorizes data but also links it to actionable farming recommendations. Methodologically, it involves data collection, preprocessing, machine learning, and semantic integration. Performance evaluation through metrics and visualizations reveals the framework's effectiveness, aiding decision-making in precision agriculture. This study contributes to IoT-based precision agriculture by bridging the gap between raw sensor data and actionable insights, empowering a semantic framework for contextual categorization and recommendation generation. The fusion of machine learning and web semantics holds transformative potential for agriculture, enhancing data management and decision-making processes.
Automated machine learning and neural architecture optimization
Authors: Adivarekar1 Pravin P. , A Amarnath Prabhakaran , Sharma Sukhwinder , P Divya , Muniyandy Elangovan  , Ravi Rastogi
Published: January 2024

Automated machine learning (AutoML) and neural architecture optimization (NAO) represent pivotal components in the landscape of machine learning and artificial intelligence. This paper extensively explores these domains, aiming to delineate their significance, methodologies, cutting-edge techniques, challenges, and emerging trends. AutoML streamlines and democratizes machine learning by automating intricate procedures, such as algorithm selection and hyperparameter tuning. Conversely, NAO automates the design of neural network architectures, a critical aspect for optimizing deep learning model performance. Both domains have made substantial advancements, significantly impacting research, industry practices, and societal applications. Through a series of experiments, classifier accuracy, NAO model selection based on hidden unit count, and learning curve analysis were investigated. The results underscored the efficacy of machine learning models, the substantial impact of architectural choices on test accuracy, and the significance of selecting an optimal number of training epochs for model convergence. These findings offer valuable insights into the potential and limitations of AutoML and NAO, emphasizing the transformative potential of automation and optimization within the machine learning field. Additionally, this study highlights the imperative for further research to explore synergies between AutoML and NAO, aiming to bridge the gap between model selection, architecture design, and hyperparameter tuning. Such endeavors hold promise in opening new frontiers in automated machine learning methodologies.
Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review
Authors: Prabakar D. , Kumar D.R. Santhosh , Kumar R.S. , M. Chitra , K. Somasundaram , Ragavendiran S.D.P. , Vyas Narayan K.
Published: January 2024

Objectives: This article reviews and provides an exhaustive examination of task offloading and trajectory control techniques in unmanned aerial vehicles (UAVs) integrated with Internet of Things (IoT), highlighting their significance and impact on the UAV ecosystem. The paper begins by introducing the fundamental concepts of UAVs, IoT, and their integration, emphasizing the potential benefits and challenges of this union. Subsequently, it delves into an extensive exploration of task offloading, a critical aspect that optimizes UAV operations by distributing tasks between the UAV and edge/cloud computing resources. Various task offloading strategies, including computation offloading, data offloading, and control offloading, is discussed in detail, elucidating their role in optimizing resource utilization, energy efficiency, and real-time decision-making in UAVs.Methods: The review comprehensively covers trajectory control techniques, which are essential for ensuring UAVs can navigate through dynamic environments safely and efficiently. This study outlines the use of IoT technologies, such as GPS, sensors, and communication networks, to enable precise trajectory planning, obstacle avoidance, and adaptive path adjustments. It also discusses the integration of machine learning and AI algorithms for autonomous UAV navigation, taking into account environmental factors, mission objectives, and real-time data from IoT sources. The paper further discusses the challenges and potential security concerns associated with IoT integration in UAVs, as well as the emerging trends and future prospects of this dynamic field. It emphasizes the need for standardized protocols and robust cybersecurity measures to ensure the reliability and safety of UAV-IoT systems.Findings: This exhaustive review offers a comprehensive understanding of the synergistic relationship between UAVs and IoT, shedding light on the task offloading and trajectory control techniques that empower these autonomous aerial vehicles. By leveraging IoT technologies, UAVs are poised to continue transforming industries and driving innovation in ways previously unimaginable, making this interdisciplinary field an area of great promise and significance.
A smart irrigation monitoring service using wireless sensor networks
Authors: Reddy Elangovan G. , V Anjana Devi , V Subedha , Reddy B Tirapathi , R Viswanathan
Published: January 2024

The present research uses wireless sensor networks (WSN) to create a smart watering system. The system’s ability to perform real-time monitoring and management of irrigation makes sure that crops get the right quantity of water depending on their unique needs. The suggested method boosts agricultural yields, decreases labor costs, and improves water usage efficiency. The system uses a field-deployed network of inexpensive wireless sensors to track the soil moisture levels in real time. The central controller utilizes the wirelessly sent sensor data to decide when and how much water should be applied to the crops. Utilizing wireless protocols like Zigbee, these nodes connect to a central gateway, where the data is processed and examined to establish the ideal watering needs for each crop. The technology is scalable and simple to install in larger agricultural fields. The study’s findings indicate that the system can boost crop yields by up to 30% while boosting water usage efficiency by up to 60%. Farmers may decrease their water use, save time and money, and enhance their profitability by adopting the smart irrigation monitoring service powered by WSN.
E-HRM: Learning approaches, applications and the role of artificial intelligence
Authors: Balakrishnan Bajeesh , Parivara Swetha A.
Published: January 2024

E-HRM (Electronic Human Resources Management), which is derived from the concept of HRM (Human Resources Management) plays a significant role in automating certain key processes in the department of human resources. One of the modules of E-HRM is the training or the learning module, which when combined with a digital source turns out to be an E-Learning (Electronic Learning) or E-Training (Electronic Training) module. This is a transformation of converting the learning platform from an offline to an online mode. The organizations to increase their level of training from their employees should look for a fast-paced solution at a shorter turn-around time and the prime way to perform such a strategy is to automate the whole process of training and predict the training need and outcome. This research paper is focused on two aspects of e-learning i.e., how an e-learning system is collaborated with an intelligent system in the form of Artificial Intelligence and the other aspect is how an employee turn over data fetched from organizations in the IT (Information Technology) sector can help understand the real requirements of learning among employees in the IT organizations.
Integrated deep learning classification of Mudras of Bharatanatyam: A case of hand gesture recognition
Authors: Naaz Saba , Shiva Kumar K.B.
Published: January 2024

Bharatanatyam is a famous Indian classical dance which incorporates the essence of hand gestures or mudras as a means of communication by performers. Bharatanatyam has around 28 single and 23 double hand gestures respectively. Recognizing the gestures is challenging due to minor structural differences between the gestures and presence of gestures with higher structural similarity. This work proposes a deep learning strategy combining Scale invariant feature transform features with convolutional neural network to address the challenge in accurate recognition of mudras. The gestures are segmented using active net based segmentation model to reduce the influence of background in gesture recognition. The gestures are then grouped based on similarity and convolutional neural network is trained for each group to solve the problem of classification in presence of higher structural similarity. Convolutional neural network with structural conflict minimization kernel is used to classify the gestures. The proposed model attained an accuracy of 95% in classification of mudras and it has lower false positives of 2%.
Optimizing durability of the thin white topping applying Taguchi method using desirability function
Authors: J. Akshay , Kumar G. Mahesh , Manjunath B. H.
Published: January 2024

White topping is a relatively new rehabilitation technology for giving strength to Asphalt pavement. Time to time resurfacing is necessary for bituminous pavement in hot climate region of India where heavy vehicles, operating under frequent start/stop conditions. So, we can apply white topping where recurring problem arises due to rutting of bituminous pavement. In India, endeavors to develop both traditional and ultra-thin white toppings as well as thin white toppings were built on an experimental basis, but their outcomes have not been specifically tracked. The performance and existence of pavement is affected by a range of things such as the load imposed by means of the site visitors plying on it, the weather, indigenous materials used and renovation standards. To examine their overall performance below Indian site visitors and climatic conditions road roughness test, compressive power by way of taking concrete cores and visible inspection survey has been executed.Moreover, the necessity for the new sorts of concrete with higher durability and higher overall performance for precise purposes, as compared to the available sorts, is sensed greater than ever. The concrete durability, laid low with one kind of variables consisting of compressive strength, resistance and permeability, has already attracted the attention of many researchers. In this research the effects of adding white topping concrete, changes in the humidity and temperature of the rehabilitated surrounding atmosphere are investigated to observe whether they will cause any growth in compressive strength and resistance of concrete and/or any decay in its permeability. For this, the Taguchi method is carried out as a method of design of experiment to get the most excellent level for every variable, accompanied by applying the regression approach to model the variables. finally, the final most desirable degrees for the sturdiness are offered primarily based on the desirability function.
Exploring AI-driven approaches to drug discovery and development
Authors: Acharjee1 Purnendu B. , Ghai Bhupaesh , Elangovan Muniyandy , Bhuvaneshwari S. , Rastogi Ravi , Rajkumar P.
Published: January 2024

The integration of artificial intelligence (AI) into drug discovery and development has ushered in a transformative era in pharmaceutical research. The research explores the profound impact of AI-driven approaches in drug discovery and development, demonstrating, that computational intelligence and biomedicine synergize to enhance innovation, efficiency, and precision in pharmaceutical science. AI’s influence spans multiple phases of drug development, from target identification and validation to the optimization of drug candidates, while also facilitating personalized medicine and expediting drug repurposing. Recent studies underscore the precision and swiftness that AI brings to the discovery of drug candidates and the prediction of molecular properties, illustrating the potential advantages of AI in pharmaceutical research. However, AI’s application in healthcare demands cautious consideration, as concerns such as model interpretability, ethical data usage, and regulatory frameworks loom large. The research also the critical need for ethical and secure data utilization. It investigates the methodology employed to create data visualizations that offer comprehensive insights into the advantages and disadvantages of AI algorithms in drug discovery. The analysis emphasizes that a judicious and context-specific approach to AI algorithm selection is essential to harness the transformative power of AI while mitigating its limitations.
A general stochastic model to handle deduplication challenges using hidden Markov model in big data analytics
Authors: A Sahaya Jenitha , Prakash Sinthu J.
Published: January 2024

Background: Since increased interest of consumers, cloud computing is needed to store and access the information about their data in their convenient way. In recent days, cloud computing offers many services stipulated by the internet. Data duplication is one of the main challenges in big data analytics that leads to increased data storage and processing time. Therefore, there is a need to develop a data deduplication process. It eliminates excessive copies of data as well as decreases the storage space. In order to preserve the accurate data information without any duplication, joint probability distribution computes the likelihood of two events occurring together at the same time and thus it leads to removing the redundant data before data is sent to the cloud server.Methods: this paper presents a GSM algorithm that uses hidden markov model, likelihood estimation, markov chain transition, and poisson distribution model.Findings: Joint probability distribution computes the likelihood of two events occurring together at the same time and thus it leads to removing the redundant data before data is sent to the cloud server.Novelty and applications: This paper proposes the general stochastic model (GSM) to handle redundant data by a multi-level process using hidden markov model (HMM), likelihood estimation, transition probability and poisson distribution model (PDM).
Location-specific trusted third-party authentication model for environment monitoring using internet of things and an enhancement of quality of service
Authors: Akila K.
Published: January 2024

In the modern digital world, the Internet of Things (IoT) is a modern and advanced technology that interconnects many immeasurable devices. The collection of wireless sensors formed the wireless sensor network. WSN nodes are battery-powered nodes with limited power and computational capability. When using loT-based wireless sensor networks, the nodes are used to communicate with the internet, where there is a need for more secure protocols. In this technological era where time factor plays a key role in everyone’s personal busy life. The need for smart and sensor appliances that work without human intervention can be a solution to some extent for the time factor. IoT is a network where physical objects, vehicles, devices, buildings and many other smart devices are electronically embedded with hardware and software with huge network connectivity. But the communication and data exchange are not that much easy to carry out, it requires a high secured protocol for authentication as well as key encryptions. Besides focusing on secured key distribution importance for enhancing various parameters are also considered which includes, EC additions, multiplications, pairing, hash-to-point operations, security performances, and energy consumption are also considered. In this paper, focuses on “LSTTP” which authenticates the nodes based on the Device Finger Print (DFP) with a Trusted Third Party and proposes the algorithm for enhancing the quality of service parameters such as Throughput, Jitter, Latency and Security.
Detecting denial of sleep attacks by analysis of wireless sensor networks and the Internet of Things
Authors: Jenifer R.R. , Prakash V.S.J.
Published: January 2024

The Internet of Things (IoT) amalgamates a large number of physical objects that are distinctively identified, ubiquitously interconnected and accessible through the Internet. IoT endeavors to renovate any object in the real world into a computing device that has sensing, communicating, computing and control capabilities. There are a budding number of IoT devices and applications and this escort to an increase in the number and complexity of malicious attacks. It is important to defend IoT systems against malicious attacks, especially to prevent attackers from acquiring control over the devices. Energy utilization is significant for battery-enabled devices in the IoT and wireless sensor networks which are operated long time period. The Denial-of-Sleep attack is an explicit type of denial-of-service attack that beleaguered a battery-powered device’s power supply that results in the exhaustion of this critical resource. This paper focuses on the survey on Denial of sleep attacks in Wireless Sensor networks and the IoT.
Content analysis of academic library website with special reference to the central universities in Northeast India
Authors: Gohain1 Anurag B. , Mishra Devanand , Mera Vithou U
Published: January 2024

In this study, an assessment has been made to analyze the library website content of central universities in Northeast India. The current research is based on a checklist prepared using various criteria and assessments conducted by previous researchers. The methodology of the study includes the use of ‘Yes’ and ‘No’ criteria for content analysis, with data being tabulated and analyzed using Microsoft Excel. The analysis covers topics such as the availability of library websites or web portals, the accessibility of these websites, design factors, site descriptions, statements of responsibility/contact information, website aids and tools, e-resources, and Web 2.0 applications. This study highlights the varying degrees of website quality and services offered by central university libraries in Northeast India and provides a comprehensive assessment of their online presence.
Evaluating the Responsiveness of Hindi version of International Physical Activity Questionnaire-Long Form (IPAQ-LF) in healthy adults.
Authors: Prabhu Sukhada S. , Thakur Anuprita M.
Published: January 2024

IPAQ-LF is widely used tool for subjective assessment of physical activity. It has been translated, cross culturally adapted into Hindi language and tested for its reliability and validity. However, Hindi version of this questionnaire is not evaluated for its responsiveness. The current study aims to evaluate the responsiveness of Hindi version of International Physical Activity Questionnaire-Long Form (IPAQ-LF) in healthy adults. The responsiveness was assessed in 60 healthy adults by administering 6-week pedometer-based programme and behavioral modification. Responsiveness statistics suggested that Hindi version of IPAQ-LF was responsive to change based on internal responsiveness statistics. The values calculated for Effect size I, Effect size II (SRM) and Effect size III were 0.223, 0.618, 0.631 respectively, suggesting small to moderate effect size. Also, the MCID calculated in the current study for healthy individuals was 1425.06 MET-mins/week. The study demonstrates that Hindi version of IPAQ-LF is a responsive tool for assessing physical activity levels for Hindi speaking population.
Psychoemotional characteristics in psychosomatic diseases
Authors: Karimovna Narmetova Y. , Khasanboy Abdusamatov , Iltifotkhon Abdinazarova , Khabiba Nurbaeva , Adiba Mirzayeva
Published: January 2024

It was prepared based on the results of the research conducted at the "Family and Women" Research Institute. This article describes psychoemotional changes in patients with psychosomatic diseases. While the emotions of anger and sadness predominate in patients with cardiological and gastrointestinal diseases, the feeling of fear is more pronounced in patients with endocrinological diseases than other emotional states. In addition, the results of the analysis of the cognitive, emotional, and conative components of the internal appearance of the disease proved that the psychological state of the patient, his attitude to his disease and the treatment process depend on the character of the disease, the possibilities of recovery, the course of the disease and the effectiveness of the treatment.
Cultural continuity and change through ceramic ethnoarchaeology: A comparative analysis of Rang Mahal and contemporary pottery in Nohar, Hanumangarh district, Rajasthan
Authors: Sharma Alok , Deb Roumi , Manjul Sanjay Kumar
Published: January 2024

This paper explores the cultural continuity of ceramics in Nohar, Hanumangarh District, Rajasthan. The study employs qualitative research methods and comparative analysis to analyze the collected data. The data was obtained through documentation methods, archival research, and historical documents. The study reveals through the comparative analysis that Rang Mahal pottery and contemporary pottery show a remarkable continuity of the Rang Mahal culture, also recognized as Kushan pottery, within the contemporary context. The implications of these findings are significant for understanding the cultural continuity and change in human societies and for preserving the traditional knowledge and practices of pottery-making.
Impact of mindfulness-based programs on reducing stress and enhancing academic performance in college students
Authors: Deshmukh Kalpana , Dighe Aparna , Raje Harshal
Published: January 2024

Utilizing strategic marketing techniques that are especially created for obtaining qualified leads and attracting them into your sales funnel is the foundation of social media lead generation. Social media sales lead generation enables businesses to find potential customers. More significantly, these leads serve as a conduit to potential clients, enabling businesses to approach them with special deals and win their business. It speaks of the actions made by companies on social media platforms to find new leads. Leads being sent into the sales funnel, the leads being qualified via various channels (chatbots, email, social media, phone, meeting, or sales call), and finally the prospects being turned into actual sales. Building relationships with prospective customers, even if they are not immediately looking to purchase a good or service, is the goal of lead nurturing. They might have subscribed to your newsletter or followed you on social media, but they might not be prepared to make a purchase just yet. The last phase of social media leads is conversion. It speaks to the process of persuading potential customers to make judgments about their purchases. In this study, a sample size of 310 company managers in different types of companies based out of Pune was utilized. To streamline data collection, a simple random probability sampling method was employed, and respondents were drawn from diverse descriptive profiles. Descriptive Statistics, One way Anova and Independent sample t test were used to analyze the data collected from the respondents. In conclusion, there are many interesting potentials for social media and lead generation in the future. Businesses now have a wealth of chances to create leads and expand their businesses through social media thanks to the development of AI, the expansion of voice and visual search, the concentration on video, the advent of social commerce, and the greater usage of influencer marketing.
Crafting Heritage: Exploring the Socio-Economic Dynamics of Pottery-Making in Nohar, Rajasthan
Authors: Sharma Alok , Deb Roumi , Manjul Sanjay K
Published: January 2024

Pottery-making is one of the oldest crafts deeply rooted with India’s rich cultural heritage. Nohar, a town in Rajasthan and a rich pottery centre is renowned for unique pottery styles. With the objective of assessing the socio-economic status of potters in Nohar and understanding the awareness about government initiatives like KSY and PMAY among the potter community, 56 potters were selected through purposive and snowball sampling and were personally interviewed. The study concludes that the majority of participants fall under lower middle group in Kuppuswamy Socioeconomic Status (SES) scale emphasizing the importance of tailored policies for socio-economic development. The dual role of potters engaging in agricultural activities and the migration to semi-rural settings indicates the craft's adaptability to changing lifestyles while preserving its traditional essence. Low awareness about the schemes like KSY and the PMAY indicates the need to enhance awareness and understand potential gaps in implementation.
Social media usage: implications for empathy, passive aggressive behavior, and impulsiveness
Authors: Vibhu Tripathi  , Farooqi Saifur
Published: January 2024

This paper undertakes an examination of the intricate interplay between social media consumption and critical psychological attributes, specifically empathy, passive aggression, and impulsiveness while adopting a nuanced perspective on potential variations across age cohorts. The primary objectives of this research encompass a comprehensive exploration of the relationships between social media engagement and the aforementioned psychological traits, coupled with a comparative assessment of these connections within two distinct age groups: young adults (aged 18–30) and the elderly population (aged 40 and above). The research design employed a purposive sampling strategy to assemble a diverse cohort of 200 Indian citizens. The empirical findings of this study substantiate a discernible positive correlation between increased social media use and heightened levels of passive aggression, affirming the initial hypothesis. In contrast, the anticipated associations between social media usage, empathy, and impulsiveness are not supported by empirical evidence. It is notable that no significant variations in the influence of social media on empathy and impulsiveness emerged between the two age groups. Furthermore, the regression analysis confirms the outcomes of the correlation analysis, underscoring the limited effect of social media on empathy and impulsiveness. However, the analysis also underscores a positive link between social media engagement and passive aggression, particularly among young adults. This research underscores the necessity of considering age-related nuances when exploring the impact of social media on individual behavior, emphasizing the requirement for a more nuanced comprehension of how social media shapes our psychological well-being
Problems and prospects of tourism financing in Sikkim
Authors: Sharma Tara K.
Published: January 2024

In Sikkim, tourism is an industry that has a variety of positive economic and social effects. Utilizing both forward and backward connections with other economic sectors like agriculture, industry, and other service sectors, not only increases foreign exchange earnings and the country’s limited resources but also provides opportunities for employment on all fronts, including direct, indirect, and induced employment. However, it has always struggled with administration and insufficient finances. Keeping this in mind, the current study aimed to examine the problem and prospects of tourism financing in Sikkim. The primary data was gathered from 385 respondents (Investors, Stakeholders, and Tourism Businesses) from Sikkim, who were randomly selected from the targeted population. The responses have been collected through a structured questionnaire, and SPSS 25 has been utilized for data analysis. The findings revealed that there is a significant impact of tourism financing on the development of local communities, and there is a significant relationship between the involvement of financial institutions and the development of tourism projects. It has been concluded that tourism financing in Sikkim has resulted in the progress of tourism in the state, which led to the development of local communities and the development of tourism projects.
Correlation between personality traits and coping strategies of young adults in India
Authors: Jaiswal Anushka , Pandey Neerja , Sarraf Seema R
Published: January 2024

The present work aims to investigate the correlation between personality traits and coping strategies among young adults in India. Correlational and ex-post facto research designs were employed on a sample of 126 young adults (61 males and 65 females) between 18 to 35 years old. Personality traits and coping strategies were measured using NEO five-factor inventory and coping strategies. It was hypnotized that (a) There will be a significant correlation between personality traits and coping strategies of young adults. (b) There will be significant differences in coping strategies between young adult males and females. (c) There will be a significant difference in personality traits between young adult males and females. The data was analyzed using Pearson correlation and t-tests. Results demonstrated a significant correlation between the two variables. Additionally, a significant difference between males and females has been found in behavior avoidance coping strategies and personality traits of openness and agreeableness.
Assessing the Impact of Stress on the Health and Job Performance of Employees in Indian Banks
Authors: Jasleen Kaur  , Singh Sultan
Published: January 2024

Purpose: This study examined the impact of stress on the well-being and performance at work of the individuals employed at Indian public and private sector banks in the selected districts of Haryana and explored whether the effects of stress between these two sectors are significant.Design/methodology/approach: Data were gathered from 300 bank employees, with 150 each from selected public and private sector banks. A pre-validated structured questionnaire using a Likert scale was employed to measure stress-related health issues and job performance. Statistical analysis of data was done using descriptive statistics and to ensure the validity of the study’s findings, an independent sample t-test was employed.Findings: The research reveals that employees in both sectors experience stress-related health issues, including headaches, back pain, sleep disturbances, and anxiety. However, employees in public sector banks reported higher levels of back pain, fatigue and anxiety in comparison to those in private sector banks. While the impact on job performance on all the employees is reduced job satisfaction, decreased productivity and increased absenteeism. Notably, employees in public sector banks reported a higher likelihood of decreased productivity and premature retirement plans due to stress.Practical implications: Elevated levels of stress have the potential to exert adverse consequences on the productivity and performance of employees. By acknowledging stress as a plausible determinant affecting performance, banks can prioritize the establishment of a work environment that fosters productivity and efficiency, potentially resulting in enhanced employees’ performance.Originality/value: The research conducted is original and based on empirical data and contributes to the understanding of how stress affects bank employees in a specific regional context, shedding light on differences between public and private sector banks.
Marketing strategies in the pharmaceutical industry
Authors: Harsha  , S. Kumar Alwin , Jwalapuram Srihari , Kumar Sravan
Published: January 2024

Marketing is believed to maintain a steady supply of customers, which a business requires to survive. Pharmaceuticals are a global force; they include everything from domestic items to drugs and medical supplies that can save lives. Due to the dependence of the pharmaceutical industry on specialized training, pharmaceutical education is now a requirement for medical school. Distribution or marketing of pharmaceuticals makes drugs accessible to the public. Since most customers only actively seek out pharmaceuticals or medical supplies when they have an urgent need, pharma marketing is distinctive. Today, selling medications for a profit confronts this greatest obstacle. This Review article will examine the fundamental values of pharmaceutical marketing to comprehend it better. The study will also evaluate marketing materials for pharmaceuticals. This study draws on the knowledge of various academics and pharmaceutical marketing executives. The research focuses on the marketing balance, consumer drug attitudes, eco-friendly marketing, and pharmaceutical product promotion. The author’s choices and availability may necessitate that the examined articles and literature accurately reflect the study’s implication, and the findings may be suggestive rather than conclusive.
A Study on credit facilities of micro, small, and medium enterprises at Syndicate Bank
Authors: R. Behera Namita
Published: January 2024

One of the most crucial inputs for any organization is the adequate and timely availability of finance. An entrepreneur’s establishment of an MSME unit falls under this umbrella. Micro, small, and medium-sized business owners are frequently first-time business owners with little to no capital. They require technical, marketing, and management assistance and significant seed capital to launch their businesses. In addition, many MSME units need additional funding for marketing, imports, exports, generating upgrades, capability growth, expansion, and marketing. Through their branch offices and local offices spread out over the country, Indian banks provided financial centers to several businesses.
Role of social media in lead generation
Authors: Lanjekar Kunal , Kalshetti Prashant , Lopez Joe C.
Published: January 2024

The role of social media in lead generation is pivotal for contemporary businesses seeking to expand their customer base. Social media platforms serve as dynamic channels for brand promotion, offering widespread visibility and engagement opportunities. Through targeted advertising, businesses can reach specific demographics, ensuring a higher quality of leads. Content marketing on social media, including informative posts and interactive content, establishes credibility and attracts potential leads. Direct interaction with the audience fosters relationships and builds trust, guiding leads through the sales funnel. Additionally, social media facilitates the promotion of lead magnets, such as eBooks or webinars, incentivizing users to provide contact information. Positive social proof garnered through reviews and testimonials enhances a business’s credibility. The objective of the present study is to study the impact of social media on lead generation. The researchers have used descriptive research design for the present study. The nonprobability convenience sampling method is used for the selection of sample size. The data is collected from 215 respondents, with the help of a well-structured questionnaire using the survey method. The results of this study will be helpful to companies in designing their social media strategies to get more leads for their business.
Factors Affecting the Intention to Study a Master's Degree Abroad of Vietnamese Students and Recommendations
Authors: Anh Nguyen Thi Mai , Tam Le Nguyen Thanh , Nguyen Nguyen An , An Mai Thai Binh
Published: January 2024

Recently, there has been an annual increase in the number of masters in Vietnam. In contrast, universities in Vietnam do not recruit enough master's students to meet the set goals. This is due, in part, to the fact that students frequently pursue master's degrees abroad. This study aimed to determine the factors influencing students' intentions to pursue a master's degree abroad in engineering and economics. The proposed research model is based on Ajzen's Theory of Planned Behavior (TPB) (1991), developed from the previous Theory of Reasoned Action in 1975, and incorporates related studies. This model includes the factors that influence the intention to study for a master's degree—attitude, subjective norm, perceived behavioral control, communication, reasonable cost, and career opportunities—and the factors that influence the attitude to study for a master's degree. 434 valid samples for analysis, conducted several methods, including Cronbach's Alpha test, EFA, CFA, and SEM, to affirm the reliability of the measurement scale and the appropriateness of the variables in the research model. Based on the research results, we identify the influencing factors that underlie the article's recommendations.
A Review of Phycoremediation of Heavy Metals in Industrial Waste Water
Authors: Joseph Ikwebe , David Bando Christopher , Abershi Ajiduku Leyoa
Published: January 2024

Environmental Pollutants such as heavy metals poised serious threat to biological systems. Heavy metals which could be natural or anthropogenic sourced are non-degradable pollutants and must be eliminated or reduced to certain quantity that is analytically presumed to be a safe limit before discharging into the environment to avoid injurious effects on biological systems. This review work showed the abilities of microalgae to biologically remove heavy metals from waste water through the process known as ‘Phycoremediation’. Phycoremediation of heavy metals with the help of microalgae takes place in two stages: Biosorption and Bioaccumulation. Microalgae such as Nannochloropsis oculata, Scenedesmus sp, Scenedesmus obtusus, Gracilaria corticata, Sarcodia suidae and Spirulina sp has been proven to phycoremediate; Pb, Cr, Hg, As, Cd, and Cu respectively. Therefore, this review work has shown that the microalgae are efficient biological vectors for heavy metal uptake.
Uamilifu wa Ngazi ya Utambuzi katika Riwaya Teule za Kiswahili
Authors: Osango Dinah Sungu , Mbatiah Mwenda , Timammy Rayya
Published: January 2024

Makala haya yanachunguza ngazi ya utambuzi na uamilifu wake katika riwaya za Kiswahili zilizoteuliwa makusudi kwa uchunguzi huu.  Riwaya hizo ni; Nyuso za Mwanamke (S. Mohamed, 2010), Harufu ya Mapera (K. Wamitila, 2012), Hujafa Hujaumbika (F. Kagwa, 2018), Haini (A. Shafi, 2002), na Ndoto ya Almasi (K. Walibora, 2006). Ngazi ya utambuzi ni mojawapo ya ngazi mbalimbali za ufokasi zilizopendekezwa na wananaratolojia. Kimsingi, ufokasi ni mtazamo ambao huchukuliwa katika kuwasilisha simulizi na kuikuza hadithi kwa ujumla. Data ya kuudhibiti na kuthibitisha madai ya mjadala wetu imetolewa katika riwaya teule.  Uteuzi wa riwaya hizi unahalalishwa kwa misingi kwamba, hizi ni riwaya zilizo na matini pana zinazowezesha udondoaji wa mifano faafu inayodhihirisha ngazi za ufokasi na wakati huo huo kudhihirisha uamilifu wa ngazi hizo katika simulizi. Mjadala wa kimsingi katika makala haya umekitwa na kuongozwa na madai ya teneti za kimsingi za nadharia ya naratolojia ambayo inatambua ufokasi na uhusika kama vipengele muhimu katika uwasilishaji wa simulizi.