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Latest Research Topic for computer science are:

 

Data Mining include analysis of large amount of unorganized data in form of text files, image, tabular data, etc. Here further classification of this is done by working in specific area of research such as

  • Web Mining:

    Here website related information like page content optimization can be done by using its features of web log, web content, web structure.

    Web Page Prediction: This comes under web mining where web log is use for understanding the user behavior on the website, in this step page content was also used. Some algorithm like Ant colony,  markov modal, etc are use for the same.

    Web Page Ranking: In this work website various pages are analyzed for ranking the pages of the site by using methods of google rank, linear rank, page rank etc.

  • Text Mining:

    Here documents are either arrange, summarized, fetch, etc by using pattern or Term feature.

    Content Retrieval / Document Retrieval / Information Retrieval: In this work text files are either arrange in specific order OR fetch list of files based on the query of user.

  • Temporal Mining:

    Here analysis done on the basis of time stamp where various information are summarized as per there happening and there causes.

    Event Activity Happening: In this work events are proposed with there probability where chance of data going was done.

    Nature Prediction: Large amount of information gather from the satellite images for predicting the glacier movements, galaxy analysis.

    Dataset Available for Research in Computer Science are

  • Adult Dataset (code 101): for pattern recognition, privacy preserving mining, Description: It contain 32560 number of session with 11 fields where attribute contain both number and textual data. To Get Data / Download Data Send Request
  • Information Retrieval Dataset (code 102): Its an collection of images which contain 1000 images of ten category like people, elephant, bus, etc. where each subcategory have 100 images. This dataset is use for image retrieval, fetching, clustering, etc. To Get Data / Download Data Send Request
  • Text File Classification (code 103): In this dataset small set of text files are collect where 1000 text file contain article on hockey different worldcup debate. This dataset is used for text mining such as classification of text files, fetching of relevant document as per user query, conclusion / abstract creation, disputant identification, etc. To Get Data / Download Data Send Request
  • Tax Relation Dataset (code 104): In this dataset tax payer relations are present with company details, transaction between them, individual relations, here dataset is used in generating association rules, Tax evasion identification, Transaction identification for business, etc. To Get Data / Download Data Send Request
  • Brain Tumor Segmentation Dataset (code 105): In this dataset 50 images with there ground truth were present, so dataset has 100 images. Image have skull, brain and tumor section. Hence scholar can segment image into three class.  To Get Data / Download Data Send Request
  • Jaffe Images (code 106): In order to study the expression of the face this set of dataset is very helpful where seven emotion are expressed by 11 Japaneses girls. Here this dataset used for image fragmentation, expression recognition, face recognition, etc. To Get Data / Download Data Send Request
  • Chess Dataset (code 107): This dataset is collection of 76 items where 3196 number of transactions are present, its an set of numeric id of the frequent items purchase by the store. This is used for Association Rule Mining, Privacy Preserving, FP-Tree, etc. To Get Data / Download Data Send Request
  • Image Watermarking (code 108): in this dataset standard set of images are present both in color and gray format, with two size 256×256 and 512×512 name of those images are mandrilla, lena, etc. This is used for image watermarking, cryptography, encryption, decryption, etc.  To Get Data / Download Data Send Request
  • Character Recognition (code 109): Its an collection of images for identifying the character present in the hand expression used by dumb people. This is used in image processing for shape identification.  To Get Data / Download Data Send Request
  • Object Detection (code 110): In this dataset few set of video are present where people perform various action such as running, walking, jumping, etc. To Get Data / Download Data Send Request
  • Web Log (code 111): In this dataset web log of famous nasa site was present with there complete path of various random surfer, it contain 10000 sessions in the dataset in text file.  To Get Data / Download Data Send Request
  • Twitter Sentiment (code 112): Here one can get sentiment of the tweets done by the user on different emotions. This can be used in sentiment analysis, classification, emotion identification, etc. To Get Data / Download Data Send Request
  • Geosptial Tagging (code 113): Here as per the geographical co-ordination various set of image are tag by specifying its longitude and latitude value. This is used in geographical location based learning,  etc. To Get Data / Download Data Send Request
  • SAR Image (Code 114): In this dataset set of SAR image is present with different time span for the study of ice / snow precipitation, melting rate, etc. Here researcher can use this for segmentation, rate identification, etc. To Get Data / Download Data Send Request
  • Wisconsin Breast Cancer (code 1014): Its an numeric value dataset used for identifying the cluster of data which tends towards breast cancer. This dataset is implement for pattern recognition, binary classification testing techniques, etc. To Get Data / Download Data Send Request
Published by:

IJSRET Volume 3 Issue 6, November-2017

Archive Volume 3 Issue 6

An Approach for Trusted Computing of Load Balancing in Cloud Environment

Authors: Manoj Kumar Selkare, Vimal Shukla

Abstract: Cloud computing is a novel approach in order to use the resource of computing where these resources may be hardware or software. This facility is delivered as a service in the communication network. This facility known as cloud, which occurred from the use of a service as a cloud, which is an abstraction for the complex infrastructure system containing diagrams. Services of cloud computing involve trusted remote user data, and computer software. This paper proposed to an approach to efficient load balancing in cloud computing

Low Power Three Input XOR Gate For Arithmetic And Logical Operation

Authors:Ms. Poojashree Sahu, Mr. Ashish Raghuwanshi

Abstract: With advancement of microelectronics technology scaling, the main objective of design i.e. low power consumption can be easily acquired. For any digital logic design the power consumption depends on; Supply voltage, number of transistors incorporated in circuit and scaling ratios of the same. As CMOS technology supports inversion logic designs; NAND & NOR structures are useful for converting any logic equation into physical level design that comprises of PMOS and NMOS transistors. In similar way, logic can be implemented in other styles as well, with the difference in number of transistors required. The conventional CMOS design for three input XOR logic can be possible with 10 or more than 10 transistors, with the methodology discussed in this paper, the same design for three inputs XOR logic can be made possible with 16 transistors. The proposed methodology consists of transmission gate and systematic cell design methodology (SCDM). This design consumes 45% (35%) less power dissipation than that of conventional LPHS-FA and SCDM based XO10 XOR logic design with CMOS technology. Since the design for XOR logic, is useful for variety of applications such as Data encryption, Arithmetic circuits, Binary to Gray encoding etc. the XOR logic has been selected for design. The design explained in this paper is simulated with 130nm technology.

Low Read Power Delay Product Based Differential Eight Transistor SRAM cell
Authors: Ms. Jaya Sahu,Mr. Ashish Raghuwanshi

Abstract: SRAM is designed to provide an temporary storage for Central Processing Unit and replace Dynamic systems that require very low power consumption. Low power SRAM design is critical aspect since it takes a large fraction of total power and die area in high performance processors. This paper include the work on eight transistor SRAM cell that of smaller read power delay product due to cascading of pull offers 28% (74%) smaller read ‘0’ (‘1’) than exiting 7T. The SRAM cell read and cycle is characterized at 45nm technology using SPICE EDA tool.

A Robust Classification Algorithm for Multiple Type of Dataset

Authors:M.Tech. Scholar Afshan Idrees, Prof. Avinash Sharma

Abstract: With the increase in different internet services number of users are also increasing. Although while taking service user may be on risk for sharing data. So this work focus on increasing the security of the user data while taking classification service. Here algorithm provide robustness by encrypting the data and send to server, while server classify the data in encrypted form. One more security issue is that instead of transferring whole encrypted data, features are extract from the data first then encrypt and send to server for classification. Here proposed work successfully classify all type of user data in form of text, image, numeric.

An Unsupervised TLBO Based Drought Prediction By Utilizing Various Features

Authors:M.Tech. Scholar Shikha Ranjan Patel, Prof. Priyanka Verma

Abstract: Agricultural vulnerability is generally referred to as the degree to which agricultural systems are likely to experience harm due to a stress. In this work, an existing analytical method to quantify vulnerability was adopted to assess the magnitude as well as the spatial pattern of agricultural vulnerability to varying drought conditions. Based on the standardized precipitation index (SPI) was used as a measure of drought severity. A number of features including normalized difference vegetation index (NDVI), vegetation condition index (VCI), and SPI will be use for classification. Here proposed modal use Teacher Learning Based Optimization genetic approach for classify the different location present in geospatial dataset. By use of  this TLBO approach prior knowledge is not required. Experiment results shows that proposed work is better as compare to previous work.

Optimizing Material Management through Advanced System Integration, Control Bus, and Scalable Architecture
Authors:RamaKrishna Manchana

Abstract:This paper presents an advanced approach to material management by leveraging modern system integration techniques and scalable microservices architecture. The proposed solution addresses the limitations of traditional monolithic systems by introducing microservices, control bus mechanisms, and event-driven designs that enhance operational efficiency and scalability. By utilizing advanced integration patterns, including synchronous and asynchronous services, the system improves real-time data processing and decision-making capabilities. This document outlines the technical architecture, key components, integration designs, and implementation strategies that underpin a robust and adaptable material management system, demonstrating significant improvements in scalability, performance, and responsiveness.

DOI: 10.61137/ijsret.vol.3.issue6.200

Optimizing Healthcare Data Warehouses for Future Scalability: Big Data and Cloud Strategies
Authors:Srinivasa Chakravarthy Seethala

Abstract:Healthcare organizations generate vast amounts of data, driven by regulatory compliance, patient care needs, and advances in medical technology. Legacy data warehouses, while central to healthcare data management, often struggle to accommodate escalating data volumes, new data types, and real-time processing demands. This paper presents strategic insights into leveraging Big Data and cloud computing to modernize healthcare data warehouses for future scalability. We examine technical approaches, review cloud and Big Data integration techniques, and propose a roadmap for healthcare data scalability, addressing concerns of security, compliance, and data interoperability.

DOI: 10.61137/ijsret.vol.3.issue6.201

Driving Business Decisions With Data: A Practical Framework For Successful Power BI Adoption

Authors: Anjali Thomas

 

Abstract: In today’s competitive business landscape, data-driven decision-making has become a strategic imperative. Organizations are increasingly turning to business intelligence (BI) platforms to transform raw data into actionable insights that guide growth, efficiency, and innovation. Among these platforms, Power BI stands out as a versatile solution that bridges the gap between technical complexity and user accessibility. This review article presents a comprehensive framework for successful Power BI adoption, emphasizing the interplay between governance, integration, scalability, and organizational readiness. The paper begins by outlining the challenges enterprises face when shifting from intuition-based management to data-centric practices, highlighting issues of data silos, inconsistent reporting, and resistance to cultural change. It then explores how Power BI’s architecture—spanning ETL processes, SQL integration, cloud deployment, and security mechanisms—can serve as the backbone for a sustainable BI strategy. The review further examines practical use cases across industries, DevOps-driven automation, and the role of training programs in fostering a self-service analytics culture. Through a critical discussion of opportunities and limitations, the article underscores that successful Power BI adoption requires more than technology; it demands alignment between people, processes, and platforms. By providing a structured roadmap, this study offers organizations a pragmatic guide to embedding Power BI within their BI lifecycle. The conclusion reaffirms that Power BI is not simply a reporting tool but a catalyst for building data-driven cultures that enhance agility, competitiveness, and long-term decision-making excellence.

DOI: https://doi.org/10.5281/zenodo.17275574

 

Creating A Single Source Of Truth: Data Governance With Power BI, SQL, And Effective ETL Processes

Authors: Vivek Sharma

 

Abstract: In contemporary enterprises, data fragmentation across multiple systems, departments, and formats poses significant challenges to decision-making, reporting accuracy, and operational efficiency. A Single Source of Truth (SSOT) addresses these challenges by consolidating heterogeneous data into a centralized, authoritative repository. This review examines the implementation of SSOT using SQL databases, robust ETL pipelines, and Power BI for visualization and governance. It explores the principles of data governance, including data ownership, quality control, role-based security, and regulatory compliance, emphasizing their critical role in maintaining data integrity and trustworthiness. The review also details best practices for relational database design, performance optimization, and ETL automation to ensure timely and accurate data delivery. Case studies across healthcare, financial services, and retail illustrate practical applications, demonstrating improved reporting efficiency, operational responsiveness, and decision-making capabilities. Furthermore, the integration of SSOT across enterprise workflows, combined with monitoring, audit trails, and automated alerts, underscores the value of a governed, centralized data ecosystem. The article highlights current challenges, including system complexity, adoption barriers, and legacy integration, and offers strategies for mitigation. Looking forward, emerging trends such as cloud-native architectures, real-time streaming, AI-enhanced analytics, and hybrid or federated data models suggest new avenues for enhancing SSOT utility and scalability. By providing a comprehensive framework, this review underscores the strategic, operational, and compliance benefits of SSOT, positioning it as a cornerstone for modern, data-driven enterprises seeking reliability, agility, and insight-driven decision-making.

DOI: https://doi.org/10.5281/zenodo.17275623

 

Tableau’s Secret Sauce: Leveraging RHEL And Centos For High-Performance Data Visualization

Authors: Kavya Menon

 

Abstract: Modern enterprises increasingly rely on business intelligence (BI) platforms to transform raw data into actionable insights. Tableau, as a leading BI tool, offers sophisticated visualization, analytics, and reporting capabilities. However, the underlying operating environment significantly impacts performance, scalability, security, and cost efficiency. This review explores the strategic advantages of deploying Tableau on Linux-based systems, specifically Red Hat Enterprise Linux (RHEL) and CentOS, for enterprise-grade BI implementations. It examines the role of Linux in enhancing system stability, providing robust security frameworks, supporting modular and automated workflows, and enabling high availability and scalability. The article analyzes data integration strategies, ETL pipelines, and dashboard optimization practices tailored to Linux environments, emphasizing both operational efficiency and user experience. Case studies across healthcare, finance, and retail illustrate real-world applications, demonstrating how Linux-based Tableau deployments support secure, high-performance analytics, regulatory compliance, and business agility. Furthermore, the review addresses monitoring, maintenance, and performance tuning, highlighting best practices for sustained system reliability. Future trends, including AI integration, real-time streaming, hybrid cloud architectures, and advanced automation, are discussed to illustrate the evolving landscape of enterprise BI. By combining Tableau’s visualization capabilities with Linux’s reliability and flexibility, organizations can achieve cost-effective, scalable, and secure BI solutions. This article underscores the importance of selecting an appropriate operating environment to maximize Tableau’s potential and provides a comprehensive guide for IT professionals, analysts, and business leaders seeking to optimize their BI infrastructure.

DOI: https://doi.org/10.5281/zenodo.17275816

 

Migrating Legacy Information Management Systems To AWS And GCP: Challenges, Hybrid Strategies, And A Dual-Cloud Readiness Playbook

Authors: Sudhir Vishnubhatla

Abstract: Legacy Information Management Systems (IMS) remain central to operations in banking, healthcare, public sector, and media, yet their monolithic design, proprietary data formats, and brittle integrations have become barriers to agility and intelligent analytics. Earlier literature identified the persistent costs and risks of legacy IMS and proposed incremental modernization through wrapping, service extraction, and reengineering, while empirical studies in the early 2010s established the feasibility and business value of infrastructure-as-a-service (IaaS) rehosting. As of late 2017, however, the migration decision space has widened: enterprises are not merely choosing whether to move to the cloud, but how to distribute workloads across multiple hyperscalers, primarily Amazon Web Services (AWS) and Google Cloud Platform (GCP). This article synthesizes more than a decade of academic and industrial work on legacy migration and proposes a Dual-Cloud Readiness Playbook tailored to IMS modernization. The playbook comprises a readiness scorecard, a five-phase migration lifecycle, and a layered hybrid architecture that balances compliance, cost, and capability while reducing vendor lock-in. The result is a pragmatic framework that aligns with the realities of petabyte-scale content archives, metadata-heavy workflows, and emerging regulatory constraints, offering a credible path from monolithic legacy platforms to modern, cloud-native information management

DOI: https://zenodo.org/records/17298069

Published by:

IJSRET Volume 3 Issue 5, September-2017

Volume 3 Issue 5

MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

Secret Sharing Schemes over MANET to Avoid Cheater Participation [102-109]

Author: Nisha Bharti, Hansa Acharya
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

 

 

Web URL Classification and Malicious Activities: A Review [110-115]

Author: Anshika Bansal
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

 

 

Malicious Web URL Classification using Evolutionary Algorithm [116-199]

Author: Anshika Bansal
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

 

 

Attack over Email System: Review [200-206]

Author: Anuradha Kumari, Nitin Agrawal, Umesh Lilhore
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

 

 

Encryption Scheme for Mobile Ad Hoc Networks: A Survey [207-209]

Author: Neha Dwivedi, Dr. Rajesh Shukla
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

 

 

Evolutionary Algorithm Based Optimized Encryption Scheme for Mobile Ad-Hoc Network [210-216]

Author: Neha Dwivedi, Dr. Rajesh Shukla
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research
MANET, Evolutionary Algorithm, Email System, encryption scheme, journal entries, international journal of current research

Power BI’s Role In The BI Lifecycle: A Complete Guide To Implementation, Development, And Maintenance

Authors: Joseph Fernandes

Abstract: Power BI has established itself as a versatile and comprehensive platform for the business intelligence (BI) lifecycle, supporting data integration, development, visualization, collaboration, and ongoing maintenance. This review article examines Power BI’s capabilities in consolidating heterogeneous data sources, performing robust ETL transformations, and delivering interactive dashboards that provide actionable insights for enterprise decision-making. The discussion explores key aspects of implementation, including agile development methodologies, data governance, role-based access controls, and performance optimization techniques. Case studies across healthcare, retail, and finance demonstrate the platform’s practical impact, highlighting efficiency gains, improved reporting accuracy, real-time analytics, and enhanced regulatory compliance. Additionally, the article addresses common challenges such as integration complexity, technical skill requirements, and governance concerns, providing recommendations for mitigation. Emerging trends such as AI-driven analytics, predictive modeling, real-time streaming data, and cloud-native architectures are analyzed, illustrating the evolving role of Power BI in enabling intelligent decision-support systems. The review emphasizes the strategic advantages of Power BI, including democratization of analytics, scalability, and adaptability to diverse organizational requirements. By synthesizing current practices, technological capabilities, and future innovations, this article provides a roadmap for leveraging Power BI effectively to drive operational efficiency, data-driven decision-making, and organizational agility in dynamic business environments.

DOI: https://doi.org/10.5281/zenodo.17275705

 

From Spreadsheets To Stories: Creating Actionable Insights With Tableau And The Business Intelligence Lifecycle

Authors: Rani Kumari

Abstract: The transition from traditional reporting methods to interactive, data-driven dashboards has transformed how organizations interpret and act upon information. This review examines the role of Tableau in the Business Intelligence (BI) lifecycle, focusing on its ability to convert raw data into actionable insights that support both strategic and operational decision-making. Tableau’s integration capabilities, including connections to diverse data sources and support for live or extracted datasets, enable organizations to streamline data preparation, cleansing, and transformation. Its visual analytics and interactive dashboards allow stakeholders to explore trends, perform what-if analyses, and monitor key performance indicators (KPIs) in real time. Advanced features, such as calculated fields, predictive modeling, and integration with AI/ML frameworks, enhance the depth and accuracy of insights, while collaborative and cloud-enabled solutions facilitate enterprise-wide adoption. Case studies from retail, healthcare, and finance illustrate Tableau’s practical impact in improving operational efficiency, forecasting, and decision support. The review also addresses challenges, including data quality management, user adoption barriers, and performance scaling, highlighting best practices to overcome these limitations. Looking forward, the integration of AI-driven analytics, real-time data streams, and embedded BI promises to expand Tableau’s influence in decision-making workflows. By adopting Tableau strategically, organizations can foster a culture of data literacy, enhance agility, and ensure that insights are actionable, timely, and aligned with business objectives. Overall, Tableau represents a bridge between complex datasets and operational intelligence, providing organizations with a robust, flexible, and scalable BI platform.

DOI: https://doi.org/10.5281/zenodo.17275764

 

Published by:

IJSRET Volume 3 Issue 4, July-2017

Uncategorized

Efficient and Scalable Multiple Class Classification: A Review[76-79]

Author: Tarun Yadav

An Approach to Detect Malicious URL through Selective Classification[80-84]

Author: Ghanshyam Sen, Himanshu Yadav, Anurag Jain

Use of LC-Filters to Protect Equipment from Electromagnetic Pulse: Is it Real Necessity or “Bisiness as Usual”?[85-89]

Author: Vladimir Gurevich

Pilot Tone based Winner Filtering Approach for Carrier Channel Offset Estimation in OFDM Systems[90-95]

Author: Shweta Sharma, Minal Saxena

Node Replacement and Alternate Path based Energy Efficient Routing Protocol for MANET[96-101]

Author: Nehalastami, Hansa Acharya

Published by:

IJSRET Volume 3 Issue 3, May-2017

Uncategorized

Author: Neelam Kushwaha, Dharmendra Kumar Singh

Encrypted and Unencrypted Computation for Abstract Machine [59-62]

Author: Thripthi.P.Balakrishnan, Mr. S.Vijayanand, Dr. T. Senthil Prakash

Software Defined Visibility using REST API [63-66]

Author: Sindhu T, Shilpa Biradar

Battery Power Aware LAR Protocol for Mobile Ad-Hoc Network [67-69]

Author: Tarun Yadav

LAR Protocol over Mobile Ad-Hoc Network: Survey [70-75]

Author: Jasmeen Akhter, Akhilesh Shukla

Resilient Hybrid Middleware Frameworks: Automating Tomcat, JBoss, And WebSphere Governance Across Unix/Linux Enterprise Infrastructures

Authors: Sambasiva Rao Madamanchi

Abstract: This article examines the orchestration of Tomcat, JBoss, and WebSphere across hybrid Unix/Linux infrastructures, emphasizing the role of automation in creating resilient middleware frameworks. It explores the challenges of heterogeneous environments, including fragmentation, manual administration, compliance demands, and security risks. The discussion highlights how governance can be embedded into middleware provisioning, patching, and monitoring through tools such as Puppet, Chef, Ansible, Nagios, Zabbix, and Tripwire. Case studies from finance, telecommunications, healthcare, and government demonstrate how automated middleware governance improves resilience, compliance, and efficiency in mission-critical operations. The article also addresses challenges such as configuration drift, cultural resistance, and scalability while offering best practices for mitigation. Looking ahead, it identifies future directions including AI-driven predictive monitoring, cloud-native middleware orchestration, continuous compliance, and sustainability integra This article examines the transformation of enterprise governance from a compliance-centric model to a cognition-driven framework enabled by artificial intelligence. It highlights the limitations of traditional governance approaches, which focus on static audits and regulatory adherence, and explores how AI can enhance oversight by providing real-time anomaly detection, predictive compliance, and dynamic policy enforcement. Linux and Solaris, long recognized for their robust security and auditing capabilities, are positioned as ideal platforms for building AI-augmented governance systems that integrate intelligence, automation, and transparency. The discussion presents an architectural blueprint that layers AI engines and automation workflows over existing governance features, ensuring adaptability across hybrid IT environments. Challenges such as AI bias, ethical considerations, and data privacy are addressed alongside practical applications in finance, healthcare, telecommunications, and the public sector. Beyond technical benefits, the article demonstrates the strategic value of cognitive governance in reducing compliance costs, enhancing resilience, and fostering innovation.

DOI: http://doi.org/10.5281/zenodo.16925705

From Compliance To Cognition: Reimagining Enterprise Governance With AI-Augmented Linux And Solaris Frameworks

Authors: Sambasiva Rao Madamanchi

Abstract: This article examines the orchestration of Tomcat, JBoss, and WebSphere across hybrid Unix/Linux infrastructures, emphasizing the role of automation in creating resilient middleware frameworks. It explores the challenges of heterogeneous environments, including fragmentation, manual administration, compliance demands, and security risks. The discussion highlights how governance can be embedded into middleware provisioning, patching, and monitoring through tools such as Puppet, Chef, Ansible, Nagios, Zabbix, and Tripwire. Case studies from finance, telecommunications, healthcare, and government demonstrate how automated middleware governance improves resilience, compliance, and efficiency in mission-critical operations. The article also addresses challenges such as configuration drift, cultural resistance, and scalability while offering best practices for mitigation. Looking ahead, it identifies future directions including AI-driven predictive monitoring, cloud-native middleware orchestration, continuous compliance, and sustainability integra This article examines the transformation of enterprise governance from a compliance-centric model to a cognition-driven framework enabled by artificial intelligence. It highlights the limitations of traditional governance approaches, which focus on static audits and regulatory adherence, and explores how AI can enhance oversight by providing real-time anomaly detection, predictive compliance, and dynamic policy enforcement. Linux and Solaris, long recognized for their robust security and auditing capabilities, are positioned as ideal platforms for building AI-augmented governance systems that integrate intelligence, automation, and transparency. The discussion presents an architectural blueprint that layers AI engines and automation workflows over existing governance features, ensuring adaptability across hybrid IT environments. Challenges such as AI bias, ethical considerations, and data privacy are addressed alongside practical applications in finance, healthcare, telecommunications, and the public sector. Beyond technical benefits, the article demonstrates the strategic value of cognitive governance in reducing compliance costs, enhancing resilience, and fostering innovation.

DOI: http://doi.org/10.5281/zenodo.16925705

Published by:

IJSRET Volume 3 Issue 2, March-2017

Uncategorized

Comparative Study on Pilot Tone based Approach for Channel Offset Estimation in OFDM Systems [25-28]

Author: Raghvendra Sharma, Saurabh Pandey, Anubhav Sharma

Community Detection on Social Media: A Review [29-33]

Author: Sweta Rai, Shubha Chaturvedi, Chetan Agarwal

Survey on Energy-Efficient Wireless Sensor Networks[34-38]

Author: Krishna Kant Sharma, Prof. Shivendra Dubey, Prof. Mukesh Dixit

Efficient and Scalable Multiple Class Classification using Bee Colony based Probabilistic Approach[39-44]

Author: Tarun Yadav

Real-Time Transferring of Optimized Levels with Carbon and Nitrogen Gases[45-49]

Author: K. Susitra, K. Vijayalakshmi, P.N Jeipratha

Enhancing IoT Biometrics Systems Using Aspect-Oriented Programming and Java Frameworks

Author: Vinayak Ashok Bharadi

Published by:

IJSRET Volume 3 Issue 1, January-2017

Uncategorized

An Assessment of Location Aided Routing Protocol over MANET [1-5]

Author: Ankit Navgeet Joshi, Shivendra Dubey, Mukesh Dixit

Carrier Frequency Offset Estimation in OFDM Systems: Review [6-9]

Author: Padmaja Nagle, Prof. Nitin Lonbale

DFT Based Pilot tone Approach for Carrier Frequency Offset Estimation in OFDM Systems [10-15]

Author: Padmaja Nagle, Prof. Nitin Lonbale

Cloud Allocation Strategies for Load Balancing: Review [16-20]

Author: Shalini Bairagi, Prof. Vipin Verma

An Assessment of Handle Noise in Digitization of Historical Document [21-24]

Author: Anand Singh Rajput, Saurabh Pandey, Anubhav Sharma

Enhancing IoT Security and Performance Using Aspect- Oriented Programming in Java Applications

Author: Rajesh S. Bansode

Increasing Students’ Interest in Mathematics: A Teaching Quality That Connects Mathematics to Real-Life Problems
Authors:-Abhijit Sadashiv Patil

Abstract-Students’ enthusiasm in mathematics is largely influenced by their math professors’ capacity to relate the subject to real-world issues. In order to increase students’ interest in mathematics, the current study aims to determine what aspects they would want to see their math teachers enhance. Using a random sample technique, the study chose ten (10) high schools and 1,263 students from different subject areas to answer a structured questionnaire about factors that influence students’ interest in mathematics. The study examined the impact of teachers’ capacity to relate mathematics to real-world issues on students’ interest in the subject using principal component analysis and multiple linear regression analysis. According to the study, 57.4% of students’ interest in mathematics is predicted by teachers’ capacity to relate mathematics to real-world problems, which may be divided into two main components (p<0.001). The components utilized to connect mathematics to real-world problems were evaluated and their relative importance index was calculated. According to the study, when math teachers set aside quality time for pupils to do class exercises, they would be more engaged in the subject. The study also discovered that students' interest in mathematics is largely influenced by the teacher's capacity to connect mathematics to other topic areas. According to the study's findings, teachers' capacity to relate mathematics to real-world issues is crucial to the growth of students' interest in the subject.

DOI: 10.61137/ijsret.vol.3.issue1.144

Published by:

IJSRET Volume 2 Issue 6, November-2016

Uncategorized

Detection and Classification of One Conductor Open Faults in Parallel Transmission Line using Artificial Neural Network [139-146]

Author: A.M. Abdel-Aziz, B. M. Hasaneen, A. A. Dawood

Ant Colony based Optimized Authentication Mechanism for Vehicular Ad Hoc Networks [147-150]

Author: Priyanka Rathore, Pankaj Kawadakar

Network Lifetime Enhancement in Mobile Ad-Hoc Network: A Review [151-154]

Author: Ashok Kumar Yadav, Ravendra Ratan Singh

Ant Colony Based Optimized Encryption Scheme for Network-Coded Mobile Ad-Hoc Networks [155-159]

Author: Garima Boriya, Anubhav Sharma

Linear-Regression Based Node Relocation Scheme for Energy Efficient Wireless Sensor Network [160-164]

Author: Rishank Rathore, Mr. Akhilesh Bansiya

Review on BAT Algorithm in IDS for Optimization [165-168]

Author: Aliya Ahmad, Bhanu Pratap Singh Senger

AI-Driven Data Warehouse Modernization in the Healthcare Sector: A Blueprint for Efficiency Modernizing Legacy Data Warehouses with AI-Enhanced Workflows/strong>
Authors:-Srinivasa Chakravarthy Seethala

Abstract-The healthcare industry is at a pivotal moment in terms of data management. Legacy data warehouses, which once served the sector’s needs, are now proving inefficient in an era of rapid technological advancements. This article proposes a framework for modernizing these legacy systems with Artificial Intelligence (AI) technologies, particularly AI-enhanced Extract, Transform, Load (ETL) workflows. These technologies have the potential to significantly improve data quality, operational efficiency, and scalability, especially in key areas such as Electronic Health Records (EHRs), Medical Imaging, Hospital Management, and Medical Research. Additionally, AI enables predictive analytics, offering healthcare organizations the ability to anticipate patient needs and optimize resource allocation. This paper explores the challenges healthcare organizations face, the benefits of AI-driven solutions, and best practices for implementation.

DOI: 10.61137/ijsret.vol.2.issue6.135
55

Optimizing Performance In Qlikview: Essential Tips And Tricks For Faster, More Responsive Dashboards

Authors: Leela Sundari

Abstract: Optimizing performance in QlikView dashboards is critical for ensuring fast, responsive, and actionable business intelligence. As organizations increasingly rely on interactive and data-driven decision-making, performance bottlenecks due to large datasets, complex calculations, and suboptimal dashboard design can hinder operational efficiency and user adoption. This review article examines essential strategies and techniques for enhancing QlikView performance, focusing on data modeling, dashboard design, scripting optimization, server tuning, and advanced analytical integration. Key areas include implementing star and snowflake schemas, managing synthetic keys and circular references, leveraging QVDs and incremental loading, and optimizing expressions using set analysis and pre-aggregated measures. Additionally, server and environment considerations—such as memory allocation, load balancing, multi-threading, and monitoring—are discussed to maintain responsiveness under high concurrency. The article also highlights industry-specific applications in finance, healthcare, and retail, demonstrating practical implementation of optimization strategies in real-world scenarios. Emerging trends, including AI-assisted performance tuning, cloud and hybrid deployments, real-time analytics, and integration with advanced predictive and prescriptive analytics tools, are explored to illustrate the evolving landscape of QlikView performance management. By adopting these best practices, organizations can ensure that dashboards remain scalable, accurate, and efficient, enabling users to derive actionable insights quickly. This comprehensive review serves as a practical guide for BI developers, architects, and enterprise decision-makers seeking to maintain high-performance QlikView environments and maximize the value of their data-driven initiatives.

DOI: http://doi.org/

A Comparative Analysis Of Tableau And Power BI: Choosing The Right Tool For Your Business Intelligence Strategy

Authors: Arjun Mehta

Abstract: Business intelligence (BI) has evolved significantly from static reporting to interactive, self-service analytics, enabling organizations to make data-driven decisions efficiently. Among the leading BI platforms, Tableau and Power BI have emerged as dominant solutions, each offering unique capabilities, strengths, and limitations. This review provides a comprehensive comparative analysis of these tools, focusing on architecture, data integration, visualization, performance, scalability, pricing, and industry applications. Tableau is recognized for its advanced visualization, interactive storytelling, and flexibility in handling complex datasets, making it ideal for organizations that prioritize deep data exploration and narrative-driven insights. Power BI, tightly integrated with Microsoft ecosystems, offers cost-effective, self-service analytics with AI-assisted features, real-time dashboards, and ease of deployment across cloud and hybrid environments. The study examines practical applications across finance, healthcare, and retail sectors, highlighting real-world benefits and use cases. Challenges such as performance bottlenecks, data modeling complexity, user adoption, and platform-specific limitations are discussed, along with mitigation strategies including governance, optimized data management, and training. Finally, the review explores emerging trends in BI, including AI-driven analytics, predictive modeling, cloud-native deployments, real-time streaming, and mobile BI, providing guidance for organizations planning future-proof BI strategies. By offering actionable insights and best practices, this review assists enterprises in selecting the most suitable BI tool to maximize operational efficiency, analytical depth, and return on investment.

DOI: https://doi.org/10.5281/zenodo.17277257

 

Beyond The Basics: Advanced Data Modeling Techniques For Optimized Performance In Qlik Sense

Authors: Simran Kaur

Abstract: Business Intelligence (BI) has evolved from static reporting to interactive, self-service analytics, enabling organizations to make data-driven decisions in real time. Qlik Sense, a leading BI platform, offers an associative in-memory data model, advanced visualization tools, and robust ETL capabilities that empower users to explore and analyze complex datasets efficiently. This review article focuses on advanced data modeling techniques and performance optimization strategies that enhance Qlik Sense dashboard responsiveness, scalability, and usability. Key topics include star, snowflake, and galaxy schemas, management of synthetic keys and circular references, incremental loading, and QVD optimization. The article also highlights best practices in dashboard design, scripting, set analysis, and integration with external analytics tools like R and Python, enabling predictive and prescriptive analytics. Practical applications across finance, healthcare, retail, and supply chain sectors demonstrate how Qlik Sense supports actionable insights, operational efficiency, and strategic decision-making. Additionally, the review addresses common implementation challenges, such as data quality issues, model complexity, and user adoption barriers, and proposes mitigation strategies through governance, training, and iterative refinement. Future trends, including AI-driven analytics, cloud deployment, mobile BI, and natural language querying, illustrate the ongoing evolution of Qlik Sense as an intelligent, user-centric BI platform. By adopting advanced modeling techniques, optimization strategies, and best practices, organizations can fully leverage their data assets to drive informed, timely, and sustainable business decisions.

DOI: https://doi.org/10.5281/zenodo.17277327

 

Comparative Study Of Wired Vs. Wireless Communication Protocols For Industrial IoT Networks

Authors: Haritha Bhuvaneswari Illa

Abstract: Industrial Internet of Things (IIoT) networks form the backbone of smart manufacturing and digital transformation under Industry 4.0. Efficient and reliable communication between sensors, controllers, and cloud systems is essential to ensure high productivity, safety, and automation efficiency. This paper presents a comparative study of wired and wireless communication protocols used in IIoT environments. It evaluates popular wired protocols such as Ethernet/IP, PROFINET, Modbus, and EtherCAT alongside wireless alternatives like Wi-Fi, ZigBee, LoRaWAN, Bluetooth Low Energy (BLE), and 5G. Each protocol is analyzed in terms of latency, bandwidth, reliability, scalability, security, and energy efficiency. The research employs both analytical comparison from literature and simulation-based performance evaluation using MATLAB and NS-3 environments. Results reveal that wired protocols offer superior deterministic performance and reliability suitable for real-time control applications, whereas wireless technologies provide flexibility and scalability for monitoring and mobility-driven scenarios. The study highlights that hybrid architectures integrating wired backbones with wireless edge nodes can balance performance and deployment costs. This comparative analysis aims to guide industries in selecting suitable communication frameworks aligned with their operational requirements.

DOI: http://doi.org/10.5281/zenodo.17528800

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IJSRET Volume 2 Issue 5, September-2016

Uncategorized

DBSCAN: An Assessment of Density Based Clustering and It’s Approaches [109-113]

Author: Karuna Kant Tiwari, Virendra Raguvanshi, Anurag Jain

An Genetic Based Fuzzy Approach for Density Based Clustering by Using K-Means [114-120]

Author: Karuna Kant Tiwari, Anurag Jain

Review of Channel Estimation over Orthogonal Frequency Division Multiplexing in Communication System [121-125]

Author: Sonali L. Raulkar, Akant Raghuwanshi

A CFD Analysis of a Wind Turbine Blade Design at Various Angle of Attack and Low Reynolds Number [126-132]

Author: Raju Kumar, Priyanka Jhawar, Sanjay Kalraiya

A Survey: Analytics of Web Log File through Map Reduce and Hadoop [133-138]

Author: Chetan Sharma, Arun Jhapate

Aspect-Oriented Programming in Spring: Enhancing Code Modularity and Maintainability
Authors:-RamaKrishna Manchana

Abstract-Aspect-Oriented Programming (AOP) in Spring allows for the modularization of cross-cutting concerns such as logging, security, and transaction management, improving code maintainability and reducing duplication. This paper explores AOP concepts, implementation details, and practical use cases, specifically focusing on the Common Logger and Global Exception Handler.

DOI: 10.61137/ijsret.vol.2.issue5.126

Visionary Approach In Corporate Sector Of Internet Of Things

Authors: Prabal Kumar Joshi

Abstract: Internet banking is one of the unavoidable advancements in the Information technology revolution. The advancement of Information technology and the Internet led the way to the evolution of internet banking. Internet banking connects the customers and the bank through the Internet to access certain services provided by the bank. It is the application of technological advancements for bestowing the available financial information resources in electronic form. This technology advancement also renders opportunities for banks to quickly and efficiently deliver specific services to the customers at anytime from anywhere without the physical visit of consumers at the bank locations

DOI: http://doi.org/10.5281/zenodo.17019115

Enhancing Customer Experiences With AI-Enhanced Salesforce Bots While Maintaining Compliance In Hybrid Unix Environments

Authors: Ravichandra Mulpuri

Abstract: The growing demand for personalized, efficient, and secure customer interactions has accelerated the adoption of AI-enhanced Salesforce bots across industries. These bots integrate natural language processing, machine learning, and CRM intelligence to streamline engagement while adapting to user needs in real time. Their deployment in hybrid Unix environments provides enterprises with a balance of stability, scalability, and flexibility. However, ensuring compliance with global regulations such as GDPR, HIPAA, and PCI DSS remains a central challenge. This review explores the role of AI-powered Salesforce bots in enhancing customer experiences, examines compliance strategies within hybrid Unix systems, and highlights ethical, operational, and organizational considerations. Future directions emphasize the importance of compliance-by-design, secure integration, and industry-specific applications, positioning AI-driven bots as transformative tools in enterprise digital ecosystems.

DOI: http://doi.org/10.5281/zenodo.17189840

Qlik Sense On The Cloud: A Strategic Approach To Building Scalable BI Solutions On AWS And GCP

Authors: Raghav Iyer

Abstract: Cloud-based Business Intelligence (BI) is transforming how organizations access, analyze, and act upon data. Qlik Sense, a leading BI platform, leverages an associative in-memory data model, interactive dashboards, and scalable architecture to deliver actionable insights in real time. This review explores the deployment of Qlik Sense on cloud platforms, specifically AWS and GCP, emphasizing scalability, performance optimization, and integration with advanced analytics tools. Key topics include cloud-optimized data modeling, incremental ETL loading, dashboard tuning, and disaster recovery strategies. The article also highlights industry applications in finance, healthcare, and retail, demonstrating how cloud-based Qlik Sense supports operational efficiency, regulatory compliance, and decision-making agility. Challenges such as cloud cost management, latency, and governance are addressed with mitigation strategies including load balancing, auto-scaling, and security frameworks. Future trends in multi-cloud strategies, AI-driven analytics, serverless computing, real-time dashboards, mobile BI, and natural language interaction are discussed, positioning Qlik Sense as a forward-looking, intelligent BI solution. By implementing best practices in cloud deployment, data modeling, and dashboard design, organizations can maximize insight generation, enhance operational performance, and achieve sustainable growth through data-driven decision-making.

DOI: https://doi.org/10.5281/zenodo.17277422

 

The Qlik Sense Data Story: Creating Compelling Narratives From Complex Datasets To Drive Decisions

Authors: Ibrahim Rahman

Abstract: In the era of data-driven decision-making, organizations face increasing challenges in converting complex datasets into actionable insights. Qlik Sense, a leading self-service business intelligence platform, enables the creation of interactive dashboards and compelling data stories that empower analysts and decision-makers alike. This review explores the principles, techniques, and best practices for effective data storytelling using Qlik Sense, emphasizing the integration of advanced analytics, predictive modeling, and real-time interactivity. The paper begins with an overview of the evolution of data storytelling in BI and the unique capabilities of Qlik Sense’s associative in-memory model, which allows dynamic exploration and discovery of hidden patterns across multiple data sources. It then discusses foundational storytelling principles, the identification of key metrics, and methods for achieving visual and narrative cohesion. Advanced techniques such as set analysis, variables, triggers, and AI integration are examined to illustrate how adaptive and predictive narratives enhance decision-making. Industry-specific applications in finance, healthcare, and retail are analyzed through case studies, highlighting the platform’s versatility in diverse operational contexts. Challenges related to data quality, user adoption, and maintaining narrative clarity are addressed, alongside mitigation strategies and governance recommendations. Finally, emerging trends such as AI-assisted narrative generation, real-time streaming analytics, cloud integration, and multi-platform deployment are explored to provide forward-looking insights. By combining technical expertise, visualization best practices, and narrative design, this review demonstrates how Qlik Sense transforms raw data into actionable stories that drive informed, strategic business decisions.

DOI: https://doi.org/10.5281/zenodo.17277490

 

Network Modernization In Large Enterprises: Firewall Transformation, Subnet Re-Architecture, And Cross-Platform Virtualization

Authors: Shravan Kumar Reddy Padur

Abstract: Enterprises in the early 2000s relied on static perimeter firewalls, monolithic rule sets, flat subnetting structures, and heterogeneous operating platforms that were adequate for client–server models but quickly proved fragile under the accelerating demands of virtualization, mobility, and regulatory compliance. These limitations gave rise to the imperative of network modernization, a process that is far more than a routine technical refresh and instead represents a strategic transformation. Modernization encompasses the redesign of firewall policies into layered and abstracted models to reduce misconfiguration risks, the re-architecture of subnetting schemes into hierarchical and modular structures to improve scalability and control, and the orchestration of cross-platform upgrades that integrate virtualized and legacy systems while maintaining resilience. Drawing from research and practice between 2000 and July 2016, this article synthesizes how organizations adopted abstraction frameworks, distributed enforcement mechanisms, and virtualization-aware network interfaces to create adaptive infrastructures. Three representative figures are used to contextualize this transformation: (1) abstraction in firewall management, (2) virtualization-driven network interfaces, and (3) distributed firewall topologies. The analysis underscores that modernization is not a tactical exercise but a governed, programmatic shift that depends on repeatable processes, structured governance, and incremental execution to sustain long-term enterprise agility and security.

DOI: https://doi.org/10.5281/zenodo.17291987

 

Published by:

IJSRET Volume 2 Issue 4, July-2016

Uncategorized

Energy Aware Location-Aided Routing Protocol for Mobile Ad-hoc Networks: Research Avenue [102-104]

Author: Ashok Kumar Yadav, Ravendra Ratan Singh

Review: Energy Efficient Wireless Sensor Network Routing Algorithm [105-108]

Author: Rishank Rathore, Mr. Akhilesh Bansiya

Container Intelligence At Scale: Harmonizing Kubernetes, Helm, And OpenShift For Enterprise Resilience

Authors: Harish Govinda Gowda

Abstract: Containers have become the backbone of modern enterprise IT, providing portability, agility, and consistency across environments. However, scaling containers across hybrid and multi-cloud infrastructures requires more than orchestration—it demands governance, security, and resilience. This article explores how Kubernetes, Helm, and OpenShift can be harmonized to achieve container intelligence at scale. Kubernetes provides orchestration, Helm simplifies application deployment and lifecycle management, and OpenShift delivers governance, compliance, and enterprise-grade security. By layering these tools together, organizations can create resilient, scalable ecosystems that balance agility with trust. The discussion highlights key challenges in scaling containers, the role of each tool, and best practices for enterprise adoption, emphasizing that true resilience comes from harmonizing orchestration, management, and governance into one cohesive framework.

DOI: http://doi.org/10.5281/zenodo.16992539

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