MANISH BABASAHEB GUDADHE

EDUCATION

Ph. D. (Submiited) Comp. Sci. & Engg RTM Nagpur University, Nagpur Dec 2021 -- --
M. E. (Wireless Comm. & Camp.) RTM Nagpur University, Nagpur 2008 Distinction 75.40 %
B. E. (Computer Technology) Nagpur University, Nagpur 2000 Firstclass 64%
H.S.S.C. Maharashtra Board, Nagpur 1996 Firstclass 67.33%
S.S.C. Maharashtra Board, Nagpur 1994 Distinction 81.71%

RESEARCH INTERESTS

Database, Data Analytics, Data Replication, Cloud Computing

Scopus Publications

Teacher Training Models for Integrating Technology in Special Education
International Journal of Special Education, 2026
AI-POWERED BEAT DETECTION AND ITS EDUCATIONAL USES
Manish Gudadhe, Gunveen Ahluwalia, Prince Kumar, Ansh Kataria, Ronald Doni A, Hanna Kumari
Shodhkosh Journal of Visual and Performing Arts, 2025
AI powered beat recognition is a huge step forward in audio signal processing since it brings together the power of machine learning and the intricate rhythms of music. Earlier methods for the search of beats were based on energy analysis of the signal and frequency decomposition, which were many times limited by the variation of the pace, the type and the quality of the recordings. Nowadays, deep learning has led to the ability of computer systems to learn rhythmic patterns from large datasets. This allows beats to be found in a much greater variety of musical styles more accurately and adaptively. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) work with the data particularly well because they extract out the hierarchical time patterns as well as finds relationships between events in audio data. The effects this technology will have on the way we teach are huge. AI-driven beat recognition in music classes helps to improve rhythm training by providing students with feedback in real time in order to help them improve their timing and rhythmic awareness. In addition to the normal means of teaching music, AI beat recognition enables interesting learning tools and games with rhythm, as well as virtual instruments that change according to the action of the user. These systems promote engagement in both the online and school environments through learning environments that are both flexible and personalized with feedback and apps that tie music and math to cognitive science. Case studies show that platforms with AI beats recognition make learning more fun, keeps students motivated and helps them to understand the rhythmic ideas.
Clustering-Based Aggregation of High-Utility Patterns from Unknown Multi-database
Abhinav Muley, Manish Gudadhe
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
High-utility patterns generated from mining the unknown and different databases can be clustered to identify the most valid patterns. Sources include the internet, journals, and enterprise data. Here, a grid-based clustering method (CLIQUE) is used to aggregate patterns mined from multiple databases. The proposed model forms the clusters based on all the utilities of patterns to determine the interestingness and the correct interval of its utility measure. The set of all patterns is collected by first mining the databases individually, at the local level. The problem arises when the same pattern is identified by all of the databases but with different utility factors. In this case, it becomes difficult to decide whether the pattern should be considered as a valid or not, due to the presence of multiple utility values. Hence, an aggregation model is applied to test whether a pattern satisfies the utility threshold set by a domain expert. We found that the proposed aggregation model effectively clusters all of the interesting patterns by discarding those patterns that do not satisfy the threshold condition. The proposed model accurately optimizes the utility interval of the valid patterns.
Synthesizing high-utility patterns from different data sources
Abhinav Muley, Manish Gudadhe
Data, 2018
In large organizations, it is often required to collect data from the different geographic branches spread over different locations. Extensive amounts of data may be gathered at the centralized location in order to generate interesting patterns via mono-mining the amassed database. However, it is feasible to mine the useful patterns at the data source itself and forward only these patterns to the centralized company, rather than the entire original database. These patterns also exist in huge numbers, and different sources calculate different utility values for each pattern. This paper proposes a weighted model for aggregating the high-utility patterns from different data sources. The procedure of pattern selection was also proposed to efficiently extract high-utility patterns in our weighted model by discarding low-utility patterns. Meanwhile, the synthesizing model yielded high-utility patterns, unlike association rule mining, in which frequent itemsets are generated by considering each item with equal utility, which is not true in real life applications such as sales transactions. Extensive experiments performed on the datasets with varied characteristics show that the proposed algorithm will be effective for mining very sparse and sparse databases with a huge number of transactions. Our proposed model also outperforms various state-of-the-art distributed models of mining in terms of running time.
Extraction & visualization of social relations on social networking services using association rule mining
International Journal of Scientific and Technology Research, 2018
Performance analysis survey of data replication strategies in cloud environment
Manish B. Gudadhe, Avinash J. Agrawal
ACM International Conference Proceeding Series, 2017
Data replication in cloud computing emerged as a popular alternative to the traditional cluster based replication because of the exponential growth in usage of the internet with an explosion of data sources and usability of data. To provide high data availability, improved performance and sustaining growing demand of data, cloud paradigm became a popular platform for replication of the frequently used data. This research article mainly concentrates on the availability of replicated data in the cloud under various situations. The variations in popularity of content, the number of replicas, placement of replicas, distribution of data nodes and workload will be analyzed to their impacts on the serviceability and data availability. The paper will investigate replication strategies based on heterogeneous cloud storage, explosive query load outburst, network bandwidth and varying content popularity. This work is an attempt to provide a comparative analysis of performance under different values of number of replicas, network bandwidth and content popularity.

RECENT SCHOLAR PUBLICATIONS

Clustering-Based Aggregation of High-Utility Patterns from Unknown Multi-database
A Muley, M Gudadhe
Transactions on Computational Science XXXIV, 29-43 , 2019
2019.0
Citations: 2
SEDReS: Storage Effective Data Replication Strategy in Cloud Environment
DAJA Manish Gudadhe
Helix The Scientific Explorer 8 (6), 4468-4473 , 2018
2018.0
Synthesizing high-utility patterns from different data sources
A Muley, M Gudadhe
Data 3 (3), 32 , 2018
2018.0
Citations: 6
Performance analysis survey of data replication strategies in cloud environment
MB Gudadhe, AJ Agrawal
Proceedings of the 1st International Conference on Big Data Research, 38-43 , 2017
2017.0
Citations: 14
Analytical Survey of Dynamic Replication Strategies in Cloud
R Karandikar, G Manish
Proceedings of the IJCA-National Conference on Recent Trends in Computer … , 2016
2016.0
Citations: 2
Year of Publication: 2016
RR Karandikar, MB Gudadhe
2016.0
Comparative analysis of dynamic replication strategies in cloud
RR Karandikar, MB Gudadhe
International Journal of Computer Applications 975, 8887 , 2016
2016.0
Citations: 7
Performance analysis for optimization of storage reallocation strategies in cloud environment
P Shelke, MB Gudadhe
International Journal of Computer Science and Applications 8 (1), 32-37 , 2015
2015.0
Citations: 2
Defense method against TCP SYN flooding Attack
MMR Thakre, MMB Gudadhe, AN Jaiswal
International Journal Of Computer Science And Applications 1 (2) , 2008
2008.0
A Survey of Dynamic Replication Strategies based on Content Popularity
R Babykutty, MB Gudadhe
International Journal of Computer Applications 975, 8887 , 0
A Survey on Performance Analysis for Optimization of Storage Reallocation Strategies in Cloud Environment
P Shelke, MB Gudadhe

MOST CITED SCHOLAR PUBLICATIONS

Performance analysis survey of data replication strategies in cloud environment
MB Gudadhe, AJ Agrawal
Proceedings of the 1st International Conference on Big Data Research, 38-43 , 2017
2017.0
Citations: 14
Comparative analysis of dynamic replication strategies in cloud
RR Karandikar, MB Gudadhe
International Journal of Computer Applications 975, 8887 , 2016
2016.0
Citations: 7
Synthesizing high-utility patterns from different data sources
A Muley, M Gudadhe
Data 3 (3), 32 , 2018
2018.0
Citations: 6
Clustering-Based Aggregation of High-Utility Patterns from Unknown Multi-database
A Muley, M Gudadhe
Transactions on Computational Science XXXIV, 29-43 , 2019
2019.0
Citations: 2
Analytical Survey of Dynamic Replication Strategies in Cloud
R Karandikar, G Manish
Proceedings of the IJCA-National Conference on Recent Trends in Computer … , 2016
2016.0
Citations: 2
Performance analysis for optimization of storage reallocation strategies in cloud environment
P Shelke, MB Gudadhe
International Journal of Computer Science and Applications 8 (1), 32-37 , 2015
2015.0
Citations: 2
SEDReS: Storage Effective Data Replication Strategy in Cloud Environment
DAJA Manish Gudadhe
Helix The Scientific Explorer 8 (6), 4468-4473 , 2018
2018.0
Year of Publication: 2016
RR Karandikar, MB Gudadhe
2016.0
Defense method against TCP SYN flooding Attack
MMR Thakre, MMB Gudadhe, AN Jaiswal
International Journal Of Computer Science And Applications 1 (2) , 2008
2008.0
A Survey of Dynamic Replication Strategies based on Content Popularity
R Babykutty, MB Gudadhe
International Journal of Computer Applications 975, 8887 , 0
A Survey on Performance Analysis for Optimization of Storage Reallocation Strategies in Cloud Environment
P Shelke, MB Gudadhe