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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: vikaspatanker

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: vikaspatanker

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

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: vikaspatanker

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: vikaspatanker

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: vikaspatanker