Category Archives: Uncategorized

Computer Science

Uncategorized

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 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

The Evolution of HR from On-Premise to Oracle Cloud HCM: Challenges and Opportunities

Authors: Kranthi Kumar Routhu

Abstract: The rapid evolution of information technology over the past two decades has fundamentally transformed the way organizations manage their human capital. Human Resource (HR) systems, once confined to on-premise infrastructures, have steadily progressed toward cloud-based Human Capital Management (HCM) ecosystems that integrate data, analytics, and user experience into a unified digital platform. This transformation reflects not merely a technological shift, but a strategic reorientation of HR’s role from administrative recordkeeping to value-driven talent management. Traditional on-premise HR systems such as Oracle E-Business Suite and PeopleSoft provided strong control, customization, and data security but required heavy maintenance, complex upgrades, and significant capital expenditure. In contrast, cloud-based HCM platforms introduced a service-oriented model that offers agility, scalability, and continuous innovation through subscription-based delivery. Oracle’s HCM Cloud represents a culmination of this digital evolution, combining the reliability and maturity of legacy systems with the adaptability of cloud-native architecture. The purpose of this study is to examine how this migration reshaped enterprise HR strategy and infrastructure. It explores the transformation journey from on-premise deployment to Oracle Cloud HCM, focusing on the organizational, technical, and regulatory challenges encountered during migration. Furthermore, it evaluates the strategic opportunities created through the adoption of Oracle’s cloud-driven HR ecosystem, particularly in enhancing workforce analytics, compliance automation, and employee engagement. Through this lens, the study demonstrates how Oracle’s Cloud HCM framework serves as both a technological and organizational enabler of modern HR excellence.

DOI: https://doi.org/10.5281/zenodo.17669776

Intelligent Data Quality Engineering: A Hybrid Framework Integrating Constraints, Probabilistic Reasoning, And AI-Driven Validation

Authors: Srujana Parepalli

Abstract: Data quality has emerged as a critical challenge in modern enterprise information systems, as rapid growth in data volume, velocity, and heterogeneity amplifies issues such as inconsistency, incompleteness, redundancy, and semantic ambiguity across distributed platforms. Traditional rule-based data validation techniques, including integrity constraints and handcrafted business rules, offer strong interpretability and auditability but often fail to scale or adapt in dynamic environments where schemas, data sources, and usage patterns continuously evolve. In contrast, purely statistical and machine-learning driven approaches excel at identifying latent patterns and anomalies in large datasets but frequently suffer from limited explainability, making governance, regulatory compliance, and root-cause analysis difficult. This article presents an integrated framework for Intelligent Data Quality Engineering that synergistically combines constraint-based validation, probabilistic modeling, and AI-driven anomaly detection to overcome these limitations. By grounding adaptive learning models in well-established research on conditional functional dependencies, probabilistic databases, and entity resolution, the framework enables predictive detection of quality issues and supports self-healing data pipelines capable of learning from historical errors and feedback. This hybrid approach effectively bridges deterministic data rules with adaptive intelligence, delivering scalable, transparent, and governance-aligned data quality solutions suitable for enterprise-grade analytics and decision systems.

DOI: http://doi.org/10.5281/zenodo.17987694

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

Relevance Of Faculty Development Programmes In Meeting Contemporary Requirements Of Higher Education Sector

Authors: Dr. Suman Dhawan

Abstract: Faculty development programs (FDPs) have emerged as a strategic intervention in the scenario of higher learning in India. In fact, FDPs and their nexus with career advancement would be less meaningful in the absence of their continued relevance to the academic requirements of the faculty. This study is aimed at re-examining faculty development programs in the framework of their ‘relevance’ to the challenges in the arena of higher learning. In the research, the authors carried out an in-depth analysis of the alignment of FDPs with the needs of the faculty in the domain of teaching, research, the use of technologies, professional growth, and the need for national development. For the paper, the authors used empirical data obtained from Orientation and Refresher programmes conducted by the Academic Staff Colleges in Delhi. In the concluding part of the paper, the authors propose suggestions that can improve the relevance of FDPs in the changing scenario of higher education in the country.

 

 

Published by:

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

 

Designing Enterprise-Wide Reference Data Foundations For Consistency, Control, And Operational Integrity Across Complex Institutional Environments

Authors: Nagender Yamsani

Abstract: Enterprise-wide reference data has emerged as a foundational element for ensuring consistency, control, and operational integrity within complex institutional environments where fragmented data ownership and system proliferation create structural risk. Persistent inconsistencies in shared reference domains often undermine governance objectives, increase reconciliation effort, and propagate errors across dependent processes, highlighting a gap between enterprise data strategy and practical implementation models. The purpose of this research is to establish a structured architectural and operating framework for centralized reference data foundations that aligns stewardship accountability, governance controls, and technical design into a cohesive institutional capability. A mixed-methods approach is adopted, integrating qualitative analysis of enterprise operating models and governance mechanisms with comparative evidence mapping drawn from large-scale institutional reference data implementations. The findings demonstrate that effective centralization depends not on tooling alone, but on the coordinated design of stewardship roles, control workflows, integration patterns, and distribution services that collectively enforce data integrity at scale. The research contributes to a practical, implementation-oriented framework that clarifies how reference data hubs can be institutionalized as shared infrastructure rather than treated as isolated data initiatives. The implications extend to both academic inquiry and professional practice by providing a replicable foundation for reducing operational risk, strengthening governance assurance, and enabling dependable downstream consumption in environments characterized by high system interdependence and regulatory sensitivity.

DOI: http://doi.org/10.5281/zenodo.18296676

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

Development Of Functionalized Schiff Base Derivatives And Their Role In Advanced Organic And Medicinal Chemistry

Authors: Janaki Ramarao Kasa, Dr. Chitra Gupta

Abstract: Due to their straightforwardness in synthesis, controllable electronic properties, high coordination abilities, and extensive biological importance, Schiff base derivatives concentrate greater importance and expertise in the research of organic and medicinal chemistry. The present study article surveys literature up to January 2016 so as to examine the impact of trends in functionalization on the chemical behaviour and medical uses of Schiff base antecedents. The research design used in the study is structured secondary research with the 60 peer-reviewed references published up to the beginning of the year 2016. The coding of these articles was by year of publication, structural classification, functional group modification, the ability to form metal-complex, biological use, and profile of activity reported. This was to identify the dominant research themes, gauge the major research treatment and interpretive uses, and to determine whether structural functionalization and metal coordination accepted superior performance. It has been disclosed that since 2010 and 2015, there was an increase in the number of studies published on Schiff bases, along with the most widespread types of use in the field were antimicrobial, anticancer, antioxidant, enzyme inhibitory, and chemosensing purposes. Schiff bases based on heteroaryl, quinoline, quinazoline, isatin, triazole and coumarin were particularly noticeable. Metal-complexed Schiff bases were found to be more commonly linked with DNA interaction, cytotoxicity and redox-based activity whereas metal free versions were more common in antimicrobial and enzyme-inhibition studies. Electron-withdrawing groups, incorporation of heterocyclic rings and the use of donor atoms like nitrogen and oxygen were always used to enhance reactivity and biological potential. The paper concludes that functionalized Schiff base derivatives were widely used as molecular platforms with twist before 2016 in selecting synthetic organic chemistry and coordination chemistry and medicinal chemistry, and the development of multifunctional therapeutics, molecular probes and catalytic systems used after 2016 builds off their earlier development.

Atmospheric Chemistry Of Greenhouse Gases And Their Role In Global Warming

Authors: Dr. Sarika Sharma

Abstract: Atmospheric chemistry plays an important role in the global climate system as greenhouse gases (GHGs) are involved in the Earth's climate system, radiation, and atmosphere. GHGs such as carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and halogenated compounds absorb infrared light and emit it in the atmosphere of Earth as greenhouse gases, and this is associated with the greenhouse effect. The heat in the lower atmosphere is retained, and global warming and the surface temperature of the Earth are increasing. As such, the chemistry of greenhouse gases depends on the concentration of atmospheric gases as well as their chemical composition, reactivity, lifetime, and interaction with solar and terrestrial radiation (e.g., photochemical reactions, oxidation processes, gas-aerosol interaction). For example, methane oxidation and nitrogen oxide cycles play an important role in ozone production and secondary radiative forcing, so that the chemistry of atmospheric chemistry and climate are interrelated. Since the 20th century, anthropogenic activities such as combustion of fossil fuels, industrial pollution, deforestation, and agricultural processes have increased the GHG levels in our atmosphere, thus adding to the natural greenhouse effect. However, CO₂ is the most important greenhouse gas present now, but it is not the only one that is responsible for warming, and other gases such as CH₄ and N₂O are essential in the global warming process as well. Atmospheric chemistry reveals that the greenhouse effect is not only dependent on CO₂, but many interacting gases are involved in the climate processes. Recent studies have also shown that changes in the composition of the atmosphere can lead to severe weather events, radiative forcing, and climate feedback loops, and the consequences can be dramatic for global warming. In any system for climate change, the interplay of greenhouse gases, aerosols, and chemical reactions in the atmosphere should be taken into account. From a global perspective, understanding the chemistry and nature of greenhouse gases is necessary to understand what is driving us toward global warming. The chemical properties and interactions of these gases are also useful in understanding how climate change must be countered in the long run and how to identify solutions to this problem for climate policy.

DOI: https://doi.org/10.5281/zenodo.19416017

Published by: