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Research Advances on Foreign Portfolio Investments: A Bibliometric and Thematic Analysis
[No abstract available] -
Research aligned analysis on web access behavioral pattern mining for user identification
Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques. BEIESP. -
Research article toxicological evaluation of ethanolic leaf and fruit extracts of phaseolus vulgaris l. Treated with wastewater in danio rerio hamilton (zebrafish)
Background and Objective: The cultivation of Vegetables in the world is facing a shortage of water so that the farmers are forced to use sewage wastewater for cultivation in underdeveloped countries. Therefore, the present study was an attempt to examine the toxicity level of accumulated heavy metals in the vegetables irrigated with sewage water and treated sewage water. The concentration-dependent changes in toxicity of ethanolic leaf and fruit extracts of Phaseolus vulgaris treated with wastewater in Zebrafish were analysed in this study. Materials and Methods: For the experiment, finely ground powders of leaves and fruits of Phaseolus vulgaris were extracted with ethanol. Using different concentrations of these extracts, a toxicity test was done with Danio rerio as per the OECD guidelines 203. Results: Using AAS, heavy metals like lead and manganese were found in higher concentrations in untreated wastewater than in distilled water and treated wastewater. The results indicated that ethanolic leaf extracts of treated wastewater irrigated Phaseolus vulgaris does not induce toxicity when used at a dose below 400 mg LG1. Leaf extracts of Phaseolus vulgaris grown with wastewater showed the lowest and highest mortality at 100 and 400 mg LG1, respectively, when compared to other plant extracts. Histopathological variations were also observed in the fishes exposed to the lethal concentrations of plant extracts. Statistical evaluation of the correlation between concentration and mortality percentage was carried out using SPSS. Conclusion: The present study revealed that the leaf and fruit extracts of Phaseolus vulgaris grown with untreated wastewater were more toxic to Danio rerio than other extracts used in the experiment. 2022 Aleesa Augustine and Jobi Xavier. -
Research asset creation (RAC) model for PhD awarding universities in India
Importance of higher education and research is becoming more prominent and admission for PhD is increasing year by year in India. Most of the time Research done as part of PhD ends with the submission and acceptance of thesis in Universities. This research future work or extension work might be picked by other PhD candidate in the same or from the different Universities but not sure when it will happen. Also not sure the research completed has achieved its end objective and University is fine to stop that research after spending so much of time and resources of University. This paper insists on continuous research with PhD candidates every year in Universities until the completion of research and scope for reducing the waste of resources and time. This paper considers the importance and effectiveness of Indexing parameters, Indexing agencies, review methods, Journals and relevant concepts. Considering the usefulness and facts of the same to research community building a standard process with procedures to control quality, performance and original research in place, I have built a Research Asset Creation (RAC) model for universities offering PhDs in India by making use of University level Indexing, Indexing parameters, Inter and Intra University peer review methods and University Journal as magazine and as well as Indexing agency. Research cannot be considered just for the award of degree. There is a need for making use of research, resources of University and time spent on research till the objective of the research is accomplished. This can be accomplished by adopting Research Asset Creation model. Complete details of the model, components of the model, implementation, its benefits and usage is discussed in this paper. This is a generalized Model and fit for all Countries which are looking for effective use of research and resources in a progressive manner. BEIESP. -
Research challenges in self-driving vehicle by using internet of things (IoT)
This article summarizes the benefits, safety hazards, and limitations of owning a self-driving vehicle. Finding a way to use an SDV(Self Driving Vehicle) is minimizing the risk for an accident is important for public and road safety. The actual rate of accidents for self-driving vehicles are lower than that for regular vehicles since the total number of miles of self-driving vehicles combined is nowhere close to that of regular fossil-fueled vehicles. Even though there is no proof that self-driving vehicles will not cause accidents, it is important to know that self-driving vehicles weren't the cause in all the cases they have been involved. That is, it will not be purely considered as the machine's mistake. The safety level of self-driving vehicles has been proven to be one of the best and that has led to the number of serious accident-related wounds in self-driving vehicles to remain lower than the standard level. Nevertheless, Internet of Things plays a major role in developing the self-driving vehicle concept. 2021 IEEE. -
Research Competence of University Teachers in Relation to Organisational Ethos and Research Culture
The standards of research depend on the maintenance and coordination of research activities that are conducted by the universities. The flexibility in ordinance and statutes empowers the universities to frame the guidelines that empower the research competence of the teachers. However, newlinethe existing framework is not adjusted to modern approaches to research competence which creates issues in developing a framework for evaluation for research competence. The objective of the present newlinestudy is to develop and validate a framework for research competence for university teachers. The study measures the relationship between research competence, organisational ethos, and research culture of university teachers. The study used the existing measuring instruments to evaluate newlineorganisational ethos and research culture. Researcher has developed the measurement scale for research competence. The validity and reliability have been done for all the measuring instruments research competence, organisational ethos and research culture. The factor analysis has conducted for newlinethe measuring instrument of research competence. The quantitative data for the study has been taken from the self-reported experience of 451 university teachers. The study found that there was a significant difference in demographic variables such as gender, age, work experience, educational newlinequalification, and subject background with organisational ethos, research culture, and research competence of university teachers. The structural equation model showed the relationship between the components of research competence, organisational ethos, and research culture. The present study can newlineassist policymakers to evaluate the research competence, organisational ethos, and research culture of the university teachers. The study indicates the practical and academic importance of university newlineteachers to enhance research performance. -
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Research Initiative on Sustainable Education System: Model of Balancing Green Computing and ICT in Quality Education
Green Computing Practices (GCP) convey the revolutionary changes of the modern education system. The education system is transforming into a hybrid mode of operations in effective teaching and learning procedure. In the modern era, computer devices are playing a foremost role in performing ICT based teaching and learning (ICT-BTL). The GCP and ICT-BTL are the creative and innovative practices that can ensure the eco-friendly enactment and safeguard from various harmful environmental impacts. The motive of projecting the present research outcome is to address the impact of GCP on ICT-BTL activities. The creative and innovative practices of ICT-BTL support the implementation of GCP towards a sustainable education system. A sustainable education system interconnects the teachers, learners, institutions, and industrial experts through eco-friendly electronic and computer devices that ensure maximum efficiency in education with minimum environmental impacts. 2022 IEEE. -
RESEARCH INSIGHTS ON TEACHER EDUCATION
Teacher education is an important academic domain, as it determines the quality of school education through the teachers produced by teacher education. The changing demands and nature of learners at school force teachers to adapt themselves constantly. This requires change in the methods of teacher training and its curriculum. Thus, researchers specialised in the area of teacher education must find innovative approaches to train preservice and in-service teachers to meet the changing demands. The present chapter presents the research conducted on the developmental aspects of teacher education across the globe in the last two decades. The chapter organises the research inputs in five different areas for ease of understanding: research related to the application of sociological, psychological and technological theories to teacher education practices, research related to transformational changes, research on teacher and teaching competence and research pertaining to teacher reflection. This would help teacher educators, schoolteachers, trainee teachers and policy makers to organise and implement effective teacher training programmes. 2023 selection and editorial matter G.S. Prakasha and Anthony Kenneth; individual chapters, the contributors. -
Research Intention Towards Incremental Clustering
Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental database, they simply run their algorithms repeatedly, taking lots of time to properly cluster those new ones coming data. It takes too much time and has also been realized that applying the existing algorithm frequently for updated databases may be too costly. So, the existing K-means clustering algorithm is not suitable for a dynamic environment. Thats why incremental versions of K-means and DBSCAN have been introduced in our work to overcome these challenges.To address the aforementioned issue, incremental clustering algorithms were developed to measure new cluster centers by simply computing the distance of new data from the means of current clusters rather than rerunning the entire clustering procedure. Both the K-means and the DBSCANDBSCAN algorithms use a similar approach. As a result, it specifies the delta change in the original database at which incremental K-means or DBSCANDBSCAN clustering outperforms prior techniques. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Research methodologies and practical applications in psychoneuroimmunology
Research methodologies in psychoneuroimmunology (PNI) are diverse, incorporating a blend of experimental, clinical, and observational approaches to study the complex mechanisms underlying the brain-immune relationship. Techniques range from molecular and genetic analyses to neuroimaging, psychophysiological assessments, and behavioral interventions. The practical applications of PNI impact areas like stress management, mental health treatment, chronic disease prevention, and immune system functioning. By examining how psychological factors, such as stress and emotions, can affect immune responses and overall well-being, PNI offers valuable insights into personalized healthcare and the development of therapeutic strategies for holistic treatment. Research Methodologies and Practical Applications in Psychoneuroimmunology explores PNI, the interactions between behavior, the nervous system, the endocrine system, and the immune system. It examines theoretical frameworks, research methodologies, and practical applications within the field, offering insights into the mechanisms underlying health and disease. This book covers topics such as immunology, cognitive function, and neuroscience, and is a useful resource for psychologists, medical professionals, policymakers, healthcare workers, scientists, academicians, and researchers. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Research Methodology and Quantitative Techniques: A Guide for Interdisciplinary Research
Research Methodology and Quantitative Techniques is a guide tailored for students and research scholars navigating the intricate landscape of research degrees across various disciplines. From clearing coursework to formulating research synopses, selecting methodologies, conducting analysis and penning impactful theses, this book is a roadmap for every stage of the research journey. It empowers scholars to undertake original, quality research that not only fulfills academic requirements but also contributes to the burgeoning pool of knowledge in diverse fields. Uniquely structured to address the specific needs of researchers, this guide goes beyond traditional boundaries, delving into areas like IPRs and research ethics often overlooked in discipline-oriented texts. By offering comprehensive support, from topic selection to publication, it aims to be the go-to resource for researchers seeking a seamless path from inception to dissemination. This book, Research Methodology and Quantitative Techniques, addresses every facet of research with clarity and insight and serves as both a companion and a vital tool for scholars poised to make a meaningful research impact in their fields. 2025 K.A. Varghese, B.R. Ranwah, Nisha Varghese and Nikhil Varghese. -
Research of Approaches to Assessing the Information Security Risks of an Organization:Qualitative Risk Assessment Methods
This article explores modern approaches to assessing information security risks within organizations, focusing on qualitative, quantitative, and hybrid risk management methodologies. The study examines the core principles of each approach, their practical applicability in different organizational contexts, the tools and frameworks commonly employed, and key implementation challenges. A comparative analysis of these methods highlights their respective strengths and limitations, providing insights for selecting the most suitable risk assessment strategy based on organizational needs, industry requirements, and available resources. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Research of Prospects and Challenges in Artificial Intelligence Technology Teaching and Learning
Recently, knowledge in the field of artificial intelligence in order to modernize various aspects of human activity has played a significant role. The exploration of the opportunities and difficulties associated with the development of artificial intelligence technologies is becoming an important area of research, as it profoundly affects our perception of work, education, medicine and other spheres of existence. New methods of machine learning, deep learning and reinforcement learning are being developed. These technologies are changing our understanding of how machines can learn and adapt to the world around them. The application of artificial intelligence covers many areas, including healthcare, finance, education and industry. In medicine, for example, AI can improve diagnostic accuracy and develop customized treatments. In education, it is possible to create personalized learning plans for each student. While in industry, artificial intelligence technologies are able to optimize production processes and increase business efficiency. However, despite the potential benefits associated with learning artificial intelligence technologies, there are serious challenges that require careful analysis. These challenges include ethical dilemmas, such as issues of algorithm transparency and responsibility for making principled decisions. Data security and privacy are also among the key aspects that require innovative approaches to AI technology training. The main purpose of the research is to deeply analyze the prospects and challenges in the field of artificial intelligence technology training, provide a comprehensive understanding of the current state of this field, identify key areas of development and propose practical strategies for effectively overcoming challenges. Taking into account both positive and negative aspects, it is necessary to have a meaningful look at the future of artificial intelligence technology education, taking into account social, ethical and technical aspects. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research on Big Data for Industry 4.0 Cyber-Physical Systems
The objective of the revolution known as Industry 4.0 seeks to optimize goods creation based on consumer requirements, specifications for quality, and financial viability. Big data collected by the Internet of Things (IoT)-based commercial Cyber-Physical Systems (CPS) plays an essential part in boosting platform operation efficiency to promote throughput with improved consumer encounters in Industry 4.0. This study shows big databases derived from IoT-based Optical-Wireless CPS (OWCPSs) for optimizing the functioning of maintenance networks in the electronics-manufacturing Industry 4.0. This research collected and analyzed big databases including five parameters: data delivery, delay, overload, throughput, and package error percentage in OWCPSs. The information gathered is important for optimizing the functioning of service systems in the production of electronic goods Industry 4.0. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Research on Effectiveness and Assistants in the Educational Process for Student Learning
The article discusses the topical issue of the use of AI assistants in the educational process of universities, which is becoming especially important in the context of the rapid development of digital technologies. Artificial intelligence, as one of the key tools of our time, is being actively introduced into the educational sphere, allowing us to optimize routine tasks and increase the effectiveness of the educational process. AI assistants are able not only to automate tasks such as checking assignments, drawing up curricula and analyzing student performance, but also to provide personalized recommendations for students, which significantly improves the quality of learning and creates an immersive environment for students. This article examines the role of AI assistants as a tool to support teachers in the educational process of universities. The main advantages of using them are considered, including improving the effectiveness of teachers, reducing the burden on teaching staff and the possibility of a more flexible approach to student learning. Attention is paid to the difficulties and limitations associated with the introduction of AI technologies, such as the need to adapt existing educational programs and the ethical aspects of using artificial intelligence. The purpose of the study is to analyze the effectiveness of using AI assistants in various educational environments, as well as to identify key factors for the successful integration of these technologies into the educational process. Based on the analysis, recommendations are offered for teachers on the optimal use of AI assistants, taking into account the specifics of educational tasks. The article will be useful both for researchers in the field of educational technologies using IT tools, and for practitioners interested in optimizing and improving the quality of the educational process. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research on secure workload execution scheme in heterogeneous cloud environment
The increasing demand for the hardware, software and infrastructure is playing a big role in the information technology domain towards the need of customers specific requirements. Cloud computing is a major backbone for providing such services over the internet. It includes the services such as applications, storage, network, scalability, sharing, virtualization, confidentiality, security, authentication, and integrity. A large number of data intensive workflow applications uses heterogeneous cloud environment for communication and computation operation. An intruder/attacker will utilize these environments for their benefit by flooding malicious links, unwanted information and others. In cloud environment, detecting a malicious device/packet during workflow execution is a critical and challenging task. The various workflow method with security, service level agreement (SLA) and quality of service (QoS) have been modelled in recent time; However, these models are not efficient in detecting malicious users and maintaining high level of QoS or workflow applications. This article focus is on addressing research future direction, issues and challenges of work in meeting secure and efficient workflow execution model for heterogeneous cloud environment. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Research on Unmanned Artificial intelligence Based Financial Volatility Prediction in International Stock Market
This study digs into the area of unmanned artificial intelligence (AI) for financial volatility prediction in the worldwide stock market, delivering unique insights into the deployment of cutting-edge technology to handle the multifarious issues of market dynamics. Our research uses Long Short-Term Memory (LSTM) networks as the AI model of choice, showing its usefulness in capturing temporal relationships in financial data by analyzing past stock price data, trading volumes, and a variety of technical indicators. Our findings suggest a potential capacity to reliably predict financial market volatility after extensive data pretreatment, feature engineering, and model training. A powerful instrument for investors, fund managers, and financial institutions to make better informed and accurate investment choices, the model's low Root Mean Squared Error (RMSE) and high (R2) values highlight its practical usefulness. Beyond the purely technical, our study considers the ethical, regulatory, risk reduction, and optimization implications for the financial sector. Financial decision-making and risk management are being transformed by the increasingly globalized market environment, and the results given here provide a concrete roadmap towards the appropriate integration of unmanned AI systems. 2024 IEEE. -
Research Perspectives on Load Balancing Strategies in Serverless Computing
Serverless computing, a groundbreaking trend in cloud computing, has transformed how applications are deployed and managed by abstracting the infrastructure layer. Serverless computing enables developers to concentrate exclusively on their code while cloud providers care for server provisioning, maintenance, and scaling. Services like AWS Lambda, Google Cloud Functions, and Azure Functions exemplify this model, offering s ubstantial advantages in terms of reduced operational complexity and cost. However, one persistent challenge in this domain is load balancing. Effective load balancing in serverless computing ensures efficient resource utilization, optimal performance, and cost-effectiveness. Unlike traditional load balancing, which typically relies on long-lived server instances, load balancing in serverless environments must accommodate the stateless and ephemeral nature of serverless functions. Traditional techniques are not directly applicable because serverless architectures functions that are instantiated on-demand in response to incoming requests. This paper surveys various strategies and approaches developed to address the unique load balancing challenges in serverless computing, providing a comprehensive overview of the current state of research and practice. The paper extends further research on serverless computing by analyzing the survey papers. The paper highly focuses the research areas in the field of edge computing, hybrid cloud models and distributed load balancing for the future usage. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research Potentials and Future Trends of Digital Sustainability
A once-in-a-century pandemic has left scars on countries already roiled by the increasingly erratic weather patterns induced by the climate crisis, wreaking havoc on sectors as diverse as food security, industrial production, and defence. Countries are torn between ameliorating COVID-19's devastating impact on education, health, and livelihoods of citizens, and finding their footing in a new global order. But from this ferment are emerging technologies, ideas, and solutions that will drive the world of the future; innovation and big ideas are building a vision that is bold and transformative. As the digital technologies evolve, its comprehensive impact on the environment needs to be considered to harness its full potential. Technology is transforming our world, but at the same time it brings new opportunities as well as challenges for sustainability. The unintended negative environmental impacts emerging from technologies are likely to be outweighed by potential of technology to solving it. Advances in technology, coupled with artificial intelligence, innovation in analytics, and data generation, is likely to have positive sustainability impacts. This chapter highlights the research potential and future trends of digital technologies for sustainability purposes. We intend to evaluate the implications of digital technology such as cloud computing, blockchain, Internet of Things, big data analytics, and artificial intelligence on pollution reduction, sustainable farming practices, conservation of biodiversity, and natural disaster management. Using real-life cases, we will investigate how digital technologies can be both an obstacle and enabler to global sustainability, which will enable devising appropriate digitalization strategies geared towards the achievement of sustainability. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa.