Scholars, researchers and other academic individuals who want to publish their research paper or articles but don’t have any idea what process is required in submitting paper to journal for research publication or what steps they should take so that their work gets published in a research journal. 

This blog will cover all the aspects that help young scholars as well as academic individuals who just begin their research journey and looking for portals for submitting article for publication. 

Submit Paper Now

Firstly cover the things one should know in advance if opting for an international journal for paper publications. Students should check the following before submitting a research article or paper in it.

Paper Publication Charges

Research domain – Check the research domain of the journal. If you are opting for a journal which does not publish in the same domain as yours then eventually submitted paper would get rejected in the first phase of reviewing. So why go with the way that does not lead anywhere instead of checking the archive section of the journal to know the prospective research areas of that journal.

Reviewing process – reviewing plays an important role while publishing research work. An author learns a lot through peer reviewing and works to improve their research work through the suggestions given by reviewers. Journal’s offer different types of peer reviewing single blind, double blind, open peer review etc to ensure the quality of the research papers before publication.

Editorial board – Some research papers  need expertise in related subjects.  One should check the editorial board of that journal whether they have the required expertise in related domains to ensure quality reviewing and relevant paper publication in the journal.  

Citations – citations show an average number or rate of the research papers cited by individuals. It reflects the quality of research work published in the journal.

Process of submitting paper to journal

• Go to the journal’s website and look for a submit paper portal. It can be named as manuscript submission, submit paper, submit manuscript etc. 
• Fill the details asked by the journal for paper submission. Like- author name, research domain, department, title of the paper, abstract, corresponding authors (if required), contact details, email, etc.
• Now upload the research document in the format accepted by the journal then click on the submit button.

You have successfully submitted your research paper to the journal. Now wait for the response from the other side. Generally it would take 10 to 15 days. So wait patiently. 

Published by: vikaspatanker

Authors: Nirmal Kumar Jingar

Abstract: Supply chain operations frequently experience incidents such as delays, shortages, quality failures, and logistics breakdowns. Identifying root causes quickly is critical, yet current incident management processes are largely manual, reactive, and error-prone. Existing systems primarily use rule-based alerts or statistical anomaly detection. Although effective in detecting issues, they lack deep causal reasoning and fail to correlate multi-source data across suppliers, transportation, and operations. This results in delayed resolution and repeated incidents. This paper introduces an automated incident intelligence framework using agentic AI with root cause reasoning. Specialized agents monitor supply chain signals, detect anomalies, and collaboratively perform causal analysis using knowledge graphs and probabilistic reasoning. Generative AI supports hypothesis generation and explanation of root causes in natural language, enabling faster human understanding and response. The proposed system was evaluated on simulated and real operational datasets involving multi-tier supply chains. Results show a 30% reduction in mean time to root cause identification and 22% improvement in incident resolution accuracy compared to traditional approaches. Additionally, the system successfully identified hidden dependencies that were missed by baseline methods. This work demonstrates the effectiveness of agentic AI in transforming incident management from reactive monitoring to proactive intelligence.

DOI: http://doi.org/10.5281/zenodo.18628070

Published by: vikaspatanker

Academic and research individuals often look for journals in their related fields to publish research papers or articles, to find databases for ongoing or upcoming research, gain knowledge, etc. As we know research is a continuous process which requires both time and money at same time. 

Submit Paper Now

Why choose journal with doi

DOI or digital object identifier provides a process to identify the online published documents, articles, research  papers and other work.  An international Journal with doi provides many advantages to scholars and researchers who published a research paper or article in it. Journals offers DOI for the research paper helps authors in getting global identity through their research work.

Paper Publication Charges

Most of the time when we explore journal’s websites it is found that some of them state one thing and do others. They write and show the logo of doi on their website but in reality they did not provide any. In this case the one who suffers loses is the author.  So it would be better to ensure whether the journal stating is correct or not. Because getting a DOI number is not that easy for everyone. One has to fulfill all the requirements along with paying a good amount for getting a DOI number for their published work. 

If a journal is published for free then the amount of DOI most of the time bear by the journal itself. In the case of a paid journal it is the author.

How to find out the DOI is authentic or fake:

To check whether the DOI provided by the journal is real or fake just go to DOI official website and put the reference doi number of the journal into the search box presented at the website. Then click on the search button. If the doi is correct then it will show the journal’s name or profile on the window. In other cases it would show none.

 

Advantages

There are many advantages one can have by associating journals with doi. They are discussed in the following points:

Separate URL – DOI provides a separate URL for each online published content. It gives a Unique identification number which reflects the source of the content and provides a global identity to the research paper individually.

Validity – DOI of a research paper increases the validity of that research paper or article. One can look for it source anytime which can not be possible if  if a research paper had a DOI then it 

Increase number of citations – a doi no provides a unique identity to research paper or article and helps scholars in locating the content they are looking for from the original sources. By using DOI one can easily use or arrange citations and references in their research papers / articles. 

Authenticity of the articles – Before DOI there was no platform available that helps in authenticating the citation and references. DOI helps in finding the original sources of the published content and indirectly controls the issue of plagiarism.

Published by: vikaspatanker

Authors:-Smita Waskale, Arjun singh Parihar , Manisha Kadam

Abstract- Heart related diseases presently pose one of the major threat worldwide. Heart abnormalities show a wide variation because of which accurate diagnosis becomes challenging. Phonocardigram (PCG) signals and their analysis has opened up a new paradigm in telemedicine. The abrupt fluctuations and the randomness of the PCG signals make them difficult to analyze and extract key parameters called features. Conventional Fourier techniques fail in this regard. In this paper, we have proposed a wavelet based technique wherein the discrete wavelet transform (DWT) have been used for the processing and feature extraction of the PCG signals has been done subsequently. The features extracted are energy, variance, entropy and standard deviation. The features extracted can be subsequently utilized for the classification of the PCG signals using the Conjugate Gradient Algorithm. The three categories of classified are: stenosis, regurgidation and normal. It has been shown that the proposed algorithm attains an accuracy of 93%.

DOI: 10.61137/ijsret.vol.9.issue4.101

Cite: Smita Waskale, Arjun singh Parihar , Manisha Kadam. “A Deep Learning Based Approach for Heart Disease Classification using PCG Datasets”. IJSRET Volume 9 Issue 4, July-Aug-2023.

Published by: vikaspatanker

Authors: Srinivas Goud Thadakapally

Abstract: As the aging population grows rapidly, the need for smart home technology designed to assist the elderly in their daily lives, health care, and their overall quality of life is becoming more apparent. Although smart home systems have undergone remarkable technological developments, their use is still constrained in the face of concerns over usability, privacy, security, ethical issues, cost, and digital literacy. This paper is a user-centric study on the adoption of smart home that considers Technology Acceptance, ethical issues, and adoption issues related to older people. A thorough literature review is carried out to discover the main elements that affect the acceptance of the users and to analyze the existing smart home solutions. Moreover, a framework called User-Centric Smart Home Adoption Assessment (UCSHAA) is suggested, which assesses technology acceptance, ethical compliance, usability, privacy, and overall technology readiness for adoption. Examples of comparative analysis show that incorporating the principles of user centered design with the consideration of ethics can enhance trust, accessibility and adoption potential. The results offer guidance to researchers, developers, healthcare and policy makers in designing secure, ethical and user-friendly environments for the smart home to support healthy and independent aging. Future studies will aim at validating the proposed framework through large-scale studies in the real user environment.

DOI: https://doi.org/10.5281/zenodo.22268138

Published by: vikaspatanker

Authors: Vanaja Kumari Degala

Abstract: Early prediction of student academic performance has become an essential research problem in higher education due to increasing dropout rates and declining academic outcomes. The ability to identify at-risk students at an early stage enables institutions to implement timely interventions and personalized academic support. With the rapid growth of educational data, machine learning (ML) techniques have shown significant potential in extracting meaningful patterns from student records. This paper presents a comprehensive machine learning-based framework for early prediction of student academic performance using pre-admission data and first-year academic attributes. Several supervised learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting (XGBoost), are evaluated. Dimensionality reduction using t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to visualize high-dimensional student data. Experimental results demonstrate that combining admission scores with first-year course performance significantly improves prediction accuracy. The proposed approach can assist academic institutions in proactive decision-making to enhance student success and retention.

Published by: vikaspatanker

Authors: Hazel T. Richardson Assistant Professor, Audrey L. Hamilton, Stella J. Woods, Chaitanya Srinivas, Yashwanth kumar

Abstract: Enterprise organizations increasingly rely on large-scale, heterogeneous data generated from transactional systems, cloud platforms, Internet of Things (IoT) devices, business applications, and external data sources to support operational processes, business intelligence, and artificial intelligence initiatives. However, maintaining the accuracy, consistency, completeness, and reliability of enterprise data remains a significant challenge due to the growing complexity, volume, and velocity of modern information ecosystems. Conventional rule-based data validation techniques often struggle to detect evolving anomalies, hidden data inconsistencies, and complex quality issues in real time, resulting in reduced analytical accuracy and increased operational risk. This paper proposes A Hybrid AI and Statistical Framework for Enterprise Data Validation and Anomaly Detection, which integrates statistical validation methods with artificial intelligence, machine learning, predictive analytics, metadata-driven governance, and automated anomaly detection to provide a comprehensive enterprise data validation solution. The proposed framework combines statistical techniques such as distribution analysis, correlation analysis, outlier detection, hypothesis testing, and confidence interval estimation with machine learning algorithms capable of identifying complex anomaly patterns, predicting potential data quality degradation, and continuously adapting to evolving enterprise environments. A centralized metadata repository, governance policies, and continuous monitoring mechanisms enhance data transparency, lineage, compliance, and automated decision-making while supporting scalable deployment across cloud-native and hybrid enterprise architectures. Intelligent validation workflows automate data profiling, quality assessment, anomaly classification, remediation recommendations, and governance reporting, significantly reducing manual intervention and improving operational efficiency. By integrating explainable artificial intelligence, adaptive learning, and predictive quality analytics, the proposed framework enhances enterprise data reliability, strengthens regulatory compliance, improves business intelligence, and provides a robust foundation for intelligent data management and sustainable digital transformation across modern enterprise information systems.

DOI: https://doi.org/10.5281/zenodo.22007466

Published by: vikaspatanker

A Deep Learning Based Approach for Heart Disease Classification using PCG Datasets
Authors:- Smita Waskale, Arjun singh Parihar , Manisha Kadam

Abstract- Heart related diseases presently pose one of the major threat worldwide. Heart abnormalities show a wide variation because of which accurate diagnosis becomes challenging. Phonocardigram (PCG) signals and their analysis has opened up a new paradigm in telemedicine. The abrupt fluctuations and the randomness of the PCG signals make them difficult to analyze and extract key parameters called features. Conventional Fourier techniques fail in this regard. In this paper, we have proposed a wavelet based technique wherein the discrete wavelet transform (DWT) have been used for the processing and feature extraction of the PCG signals has been done subsequently. The features extracted are energy, variance, entropy and standard deviation. The features extracted can be subsequently utilized for the classification of the PCG signals using the Conjugate Gradient Algorithm. The three categories of classified are: stenosis, regurgidation and normal. It has been shown that the proposed algorithm attains an accuracy of 93%.

Cite: Smita Waskale, Arjun singh Parihar , Manisha Kadam. “A Deep Learning Based Approach for Heart Disease Classification using PCG Datasets”. IJSRET Volume 9 Issue 4, July-Aug-2023.

 

Published by: vikaspatanker

Modeling and Analysis of Grid Connected Induction Generator for Wind Power Application: Review
Authors:-M. Tech. Scholar Mohit Kumar, Assistant Professor Harjit Singh

Abstract- Over the past few decades, there has been an increasing use of induction generator particularly in wind power applications. In generator operation, a prime mover (turbine, engine) drives the rotor above the synchronous speed. Stator flux still induces currents in the rotor, but since the opposing rotor flux is now cutting the stator coils, active current is produced in stator coils, and motor now operates as a generator, and sends power back to the electrical grid. Based on the source of reactive power induction generators can be classified into two types namely standalone generator and Grid connected induction generator. In case of standalone IGs the magnetizing flux is established by a capacitor bank connected to the machine and in case of grid connection it draws magnetizing current from the grid.

Cite: Mohit Kumar, Harjit Singh. “Modeling and Analysis of Grid Connected Induction Generator for Wind Power Application: Review”. IJSRET Volume 9 Issue 4, July-Aug-2023.

Published by: vikaspatanker

Enhancement of Micro-strip performance with improvement of antenna gain and feeding technique
Authors:- Pawan Kumar Nishad, Ashish Suryavanshi

Abstract- The goal of this observed is to design and analysis the Microstrip Patch Antenna which covers the Ultra Wide Band 3.1 to 10.6 GHz. This synopsis covers study of basics and fundamentals of microstrip patch antenna. A series of parametric study were done to find that how the characteristics of the antenna depends on its various geometrical and other parameters. The various geometrical parameters of the antenna are the dimensions of the patch and ground planes and the separation between them and it also includes the dielectric constant of the substrate material. The parametric study also contains the study of different techniques for optimizing the different parameters of antenna to get the optimum results and performance. This is a simulation based study. The design and simulation of the antenna is carried out using microwave Studio simulation software. Four antennas with different types of shapes were designed which cover the entire UWB range. The First designed antenna has two half circular patches which are overlapped to each other. A narrow rectangular slit is added to the patch to improve the performance of antenna.

Cite: Pawan Kumar Nishad, Ashish Suryavanshi. “Enhancement of Micro-strip performance with improvement of antenna gain and feeding technique”. IJSRET Volume 9 Issue 4, July-Aug-2023.

Published by: vikaspatanker