Computer Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems
64
Scopus Publications
1893
Scholar Citations
20
Scholar h-index
32
Scholar i10-index
Scopus Publications
Gaussian dual adjacency graph based spatial correlated and temporal time dependent traffic prediction in Bangalore City Sathish Kumar Ravichandran, Chin-Shiuh Shieh, Mong-Fong Horng, Arulmurugan Ramu, Archana Sasi Scientific Reports, 2025 With the rapid increase in the population, transportation systems are challenged by several issues. Traffic congestion is customary and traffic accidents occur frequently deteriorating traffic environments. To take the edge off these issues and enhance transportation efficiency, accurate traffic forecasting is critical. Accurate temporal time time-dependent traffic predictions are essential for ensuring the safety and efficiency of an intelligent traffic management system. Nevertheless, owing to the intrinsic spatial and temporal dependencies of traffic flow it is still a challenging problem. To solve this, some methods are proposed taking into consideration the detailed traffic patterns across major roads and intersections, while complicated spatiotemporal dynamics and interdependencies between traffic flows are not taken into account. In this work, a method called Gaussian Dual Adjacency Graph-based Spatial Correlated and Temporal Time-dependent (GDAG-SCTT) traffic prediction in Bangalore city is proposed. Initially with the raw traffic patterns obtained from Bangalore's traffic pulse dataset as input are subjected to three different processes, namely, pre-processing and feature extraction. First, Local-Global Invariant Inter Quartile and Min-Max Normalization based Traffic Data Pre-processing is applied to the Bangalore's traffic pulse dataset. Next, the extraction of spatial and temporal features is done by using a Gaussian Kernel Dynamic Adjacency based Spatial Correlated and Temporal Time-dependency based feature extraction model. By applying this pre-processing outliers are removed and finally normalized pre-processed results are obtained. Followed by which, using Spatial Correlated Graph Convolutional Neural Network spatial features are extracted and using Temporal Long Short Term Time-dependency Memory temporal features are extracted. To evaluate the GDAG-SCTT method's performance, classification metrics like precision, recall and accuracy along with regression metrics like root mean square error, training time are validated and analyzed. The GDAG-SCTT achieved higher performance compared to other state-of-the-art methods on our collected Bangalore's traffic pulse dataset demonstrating the efficiency in reducing root mean square error by 28% while improving overall accuracy by 25% in an extensive manner.
Afaan Oromoo Textual Entailment Classification Using Deep Learning Approach Diro Tolosa, Arulmurugan Ramu, Ramata Mosissa, Teshome Debushe, Desalegn Tasew, Diriba Gichile International Journal of Basic and Applied Sciences, 2025 Natural language processing (NLP) is the field that enables computers to understand and use human language. Textual entailment—a key NLP task— determines if a hypothesis can logically follow from a given premise. As we reviewed, the model designed and developed for other languages is not used for Afaan Oromoo textual entailment classification, as its semantics and syntax are different when compared with other languages. To address the gap, we proposed an Afaan Oromoo textual entailment classification model. We used Support Vector Machine (SVM) as a baseline to compare with three deep learning architectures: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) by comparing their performance to identify the most effective approach with fasttext and word2vec word embedding. We collected a dataset of 13,060 sentence pairs in Afaan Oromoo. The accuracy of SVM was 55.82% and the accuracy of CNN, LSTM, and BiLSTM was 72.8%, 75.57% and 80.47% respectively, with fasttext word embedding. Considering the limited resources available for Afaan Oromoo NLP, the result is encouraging. As a starting point, this study offers a basis for additional investigation and advancement in this field and contributes to the development of Afaan Oromoo's Natural Language Processing capabilities.
Performance Evaluation of Shor Algorithm on Simulated Quantum Hardware with Circuit Level Analysis Thamaraimanalan T, Anandakumar Haldorai, Arulmurugan Ramu, Mariyappan K Journal of Machine and Computing, 2025 Shor’s algorithm stands as a breakthrough in quantum computing due to its potential to factor large integers exponentially quicker than classical algorithms. However, implementing and evaluating this algorithm on real quantum computer hardware remains exciting due to qubit limitations, gate noise, and hardware constraints. This research presents a comprehensive performance evaluation of Shor’s algorithm using simulated quantum backends provided by Qiskit. A flexible and generic implementation is proposed, allowing dynamic input of integers to be factored, with randomized co-prime selection and automated circuit generation. The algorithm is tested on various semiprime numbers, such as 15, 21, and 35, using IBM’s Aer simulator. A major contribution of this work is the circuit-level analysis conducted both before and after transpilation. Metrics such as gate counts, circuit depth, and simulator runtime are extracted to assess scalability and resource requirements. High-resolution plots of the pre-transpiled circuits are saved to visualize algorithmic complexity, while post-transpilation metrics inform future quantum hardware feasibility. The output measurement distributions are analyzed to estimate periodicity and derive correct factors. The proposed implementation is compared with existing fixed-instance Shor demonstrations to highlight its flexibility and extensibility. Experimental results show consistent success in factor retrieval and provide valuable insight into circuit growth and complexity under realistic constraints. This analysis lays the groundwork for future adaptation to NISQ hardware and contributes to understanding Shor’s algorithm from both computational and architectural perspectives.
A security scheme based on blockchain technology with modified extreme gradient boosting decision tree-based trust management system for vehicular net Jafar Ali Ibrahim Syed Masood, Chakravarthy N. S. Kalyan, M. Sathya, Sarankumar Ramasamy, Reynaldo G. Alvez, D. Bujjibabu, Arulmurugan Ramu Leveraging Vanets and Blockchain Technology for Urban Mobility, 2025 VANETs are crucial for ITS, but their wireless nature poses security risks. Blockchain offers secure authentication and privacy. This chapter proposes a BC-based security system with a TMS using RSUs and CAs. The system identifies malicious nodes and forged messages using reputation and message metrics. A modified XGBoost and decision tree model calculate trust based on role, distance, and direct trust assessment. Experiments show BC-TMS is efficient, resilient, and ensures VANET security. Compared to existing centric trust models, the proposed paradigm is straightforward, trustworthy, and effective. In order to test BC-TMS in terms of safety, correctness, and reliability, a series of experiments are carried out, and the findings demonstrate that BC-TMS is not only efficient but also highly resilient, thereby establishing a trust model for VANETs that ensures the utmost security and protection.
Elephant Herding Optimization with SVM for Early Liver Disease Prediction 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Research on Deep Neural Network for Afaan-Oromo Language Text-to-Speech Synthesis Diriba Gichile Rundasa, Arulmurugan Ramu, Teshome Debushe Adugna, Chala Sembeta Teshome, Desalegn Tasew Journal of Computer Science, 2025 : Text-to-speech synthesis is the automatic translation of unlimited natural language sentences from Text to spoken form that closely mimics the spoken form of the same Text by a native speaker of the language. The purpose of a Texttext-to-speech synthesizer is to generate comprehensible, natural signalling human voice from text transcriptions. Despite the wide range of potential applications for Text-to-speech systems, the field is language-dependent, with most efforts concentrated on accessible languages, especially English. The linguistic resources required to make a speech from texts are lacking for under-resourced languages like the Afaan-Oromo language. To develop an Afaan Oromo language text-to-speech synthesizer, a speech dataset was prepared, which is 10644 text and audio pairs in numbers and assembled from dependable sources. After that, the proposed model is developed, which incorporates nonstandard terminology, including acronyms, currencies and numerals, in addition to common terms and names. The deep neural network was selected for this study because it has a good ability to convert Text into complex spoken Text. A number of experiments were carried out to find the best-performing model. To assess the performance of the model objectively, the attention mistake is used where, whereas to assess the models' performance subjectively, the Mean Opinion Score or scale (MOS) test is used. Subsequently, the objective outcomes evaluation revealed that Deep Voice (DV) 3 produced 18 of the 248 words in the evaluation sentence set. At the same time, Tacotron-2(two) made attention errors, which are two in number. Moreover, MOS scores for naturalness and intelligibility have made 4.36 and 4.33 out of five (5) for Tacotron-2 (two), respectively and 3.32 and 3.04 for Deep Voice(DV) 3, respectively. Because it can translate intricate verbal information into auditory feature parameters, the deep neural network was selected for this research. Therefore, the Tacotron-2 (two) model yielded good results and promising results compared with Deep Voice (DV) 3, making it suitable for a range of applications, such as smart education, different telephone inquiry services, and recommendation systems, which are the most common areas of the system.
A Review of Pattern Recognition and Machine Learning Teshome Debushe Adugna, Arulmurugan Ramu, Anandakumar Haldorai Journal of Machine and Computing, 2024 This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.
Artificial Intelligence Model for Software Reusability Prediction System R. Subha, Anandakumar Haldorai, Arulmurugan Ramu Intelligent Automation and Soft Computing, 2023 The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the software system design to be resilient. To make the software design more efficient, it is essential to assess the reusability of the components used. This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system. The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity. Metrics are obtained from the SN software system, which is then subjected to min-max normalization to avoid any saturation during the learning process. The feature extraction process is made more feasible by enriching the data quality via outlier detection. The reusability of the classes is estimated based on a tool called Soft Audit. Software reusability can be predicted more effectively based on the proposed FRF-ANN (Flexible Random Fit - Artificial Neural Network) algorithm. Performance evaluation shows that the proposed algorithm outperforms all the other techniques, thus ensuring the optimization of software reusability based on aging resilient. The model is then tested using constraint-based testing techniques to make sure that it is perfect at optimizing and making predictions.
Preface Eai Springer Innovations in Communication and Computing, 2023
Preface Eai Springer Innovations in Communication and Computing, 2023
The Future of Outcome-Based Education (OBE): Leveraging Machine Learning (ML) for Adaptive Curriculum Design and Real-Time Learning Monitoring R Venkatesh, DMD Preethi, A Ramu Transforming Outcome-Based Education with Machine Learning, 29-60 , 2026 2026
Gaussian dual adjacency graph based spatial correlated and temporal time dependent traffic prediction in Bangalore City SK Ravichandran, CS Shieh, MF Horng, A Ramu, A Sasi Scientific Reports 15 (1), 41208 , 2025 2025
7th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing SM Anandakumar Haldorai, Arulmurugan Ramu BDCC , 2025 2025
DNGR: Deep Neural Graph-Based Recommendation System for Scholarly Paper Retrieval MB Dr. M Sathya, Arulmurugan Ramu, Velkumar K International Journal of Basic and Applied Sciences 14 (5), 300-305 , 2025 2025
Designing Machine Learning Driven Dual Adjacency Graph Based Spatiotemporal Traffic Prediction for Smarter Urban Mobility and Congestion Managemnet in Bengaluru City SK Ravichandran, CS Shieh, MF Horng, A Ramu, A Sasi 2025
Enhancing Multi-Label News Text Classification for an Understudied Language: A Comprehensive Study on CNN Performance and Pre-Trained Word Embeddings AR Diriba Gichile Rundasa International Journal of Engineering, Science and Information Technology 5 … , 2025 2025
Afaan Oromoo Textual Entailment Classification Using Deep Learning Approach DG Diro Tolosa , Arulmurugan Ramu, Ramata Mosissa, Teshome Debushe, Desalegn ... International Journal of Basic and Applied Sciences 14 (3), 6 , 2025 2025
Performance Evaluation of Shor Algorithm on Simulated Quantum Hardware with Circuit Level Analysis MK Thamaraimanalan T, Anandakumar ,Haldorai Arulmurugan Ramu Journal of Machine and Computing 5 (03), 16 , 2025 2025 Citations: 3
Research on Deep Neural Network for Afaan-Oromo Language Text-to-Speech Synthesis CSTDT Diriba Gichile Rundasa, Arulmurugan Ramu, Teshale Debushe Adugna Journal of Computer Science 21 (5), 12 , 2025 2025
Assessing the Impact of Business Intelligence on Decision Support Environments in Enterprise Systems A Ramu Journal of Enterprise and Business Intelligence 5 (2), 076-085 , 2025 2025 Citations: 1
Elephant Herding Optimization with SVM for Early Liver Disease Prediction. A Ramu, TD Adugna, SK Ravichandran Grenze International Journal of Engineering & Technology (GIJET) 11 , 2025 2025
Design and Performance Evaluation of an AI-Driven Hybrid Simulation Model for LoRaWAN Networks A Ramu, A Hodza 2025
Mapping Research Trends in Satellite Imagery Applications for Agriculture: A Bibliometric Analysis A Hodza, A Ramu 2025
Nonlinear Effects of Inter Firm Competition on Innovation in Cooperative Research Networks A Ramu 2025
The Effect of Competitor Alliances on New Venture Milestone Achievement Through Cox Proportional Hazards Modeling A Ramu 2025
Machine learning for cyber threat detection using historical vulnerabilities and security standards A Ramu Journal of Computer and Communication Networks, 043-051 , 2025 2025 Citations: 1
Exploring Artificial Intelligence Applications in the Agricultural Sector A Ramu Journal of Smart and Sustainable Farming , 2025 2025
A Security Scheme Based on Blockchain Technology With Modified Extreme Gradient Boosting Decision Tree-Based Trust Management System for Vehicular Net JAIS Masood, CNS Kalyan, M Sathya, S Ramasamy, RG Alvez, ... Leveraging VANETs and Blockchain Technology for Urban Mobility, 109-134 , 2025 2025 Citations: 1
A Review of Pattern Recognition and Machine Learning AH Teshome Debushe Adugna, Arulmurugan Ramu Journal of machine and Computing 4 (01), 10 , 2024 2024 Citations: 34
A Review of Manufacturing Technologies on the Industry: Categories, Integration and Impacts A Ramu JOURNAL OF ENTERPRISE AND BUSINESS INTELLIGENCE Учредители: Anapub … , 2024 2024
MOST CITED SCHOLAR PUBLICATIONS
Early detection of lung cancer using wavelet feature descriptor and feed forward back propagation neural networks classifier R Arulmurugan, H Anandakumar Computational vision and bio inspired computing, 103-110 , 2018 2018.0 Citations: 229
Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm. D Devikanniga, A Ramu, A Haldorai EAI Endorsed Transactions on the Energy Web , 2020 2020.0 Citations: 193
Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability: A. Haldorai, A. Ramu A Haldorai, A Ramu Neural Processing Letters 53 (4), 2385-2401 , 2021 2021.0 Citations: 191
Security and channel noise management in cognitive radio networks A Haldorai, A Ramu Computers & Electrical Engineering 87, 106784 , 2020 2020.0 Citations: 155
Social aware cognitive radio networks: effectiveness of social networks as a strategic tool for organizational business management A Haldorai, A Ramu, S Murugan Social network analytics for contemporary business organizations, 188-202 , 2018 2018.0 Citations: 155
Evolution, challenges, and application of intelligent ICT education: An overview A Haldorai, S Murugan, A Ramu Computer Applications in Engineering Education 29 (3), 562-571 , 2021 2021.0 Citations: 128
Region-based seed point cell segmentation and detection for biomedical image analysis R Arulmurugan, H Anandakumar International Journal of Biomedical Engineering and Technology (IJBET) 27 … , 2018 2018.0 Citations: 115
An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer A Ramu Citations: 64
A Review of Pattern Recognition and Machine Learning AH Teshome Debushe Adugna, Arulmurugan Ramu Journal of machine and Computing 4 (01), 10 , 2024 2024.0 Citations: 34
Organization internet of things (IoTs): Supervised, unsupervised, and reinforcement learning A Haldorai, A Ramu, M Suriya Business intelligence for enterprise internet of things, 27-53 , 2020 2020.0 Citations: 33
Big data innovation for sustainable cognitive computing A Haldorai, A Ramu, CO Chow Mobile networks and applications 24 (1), 221-223 , 2019 2019.0 Citations: 30
A study on mobile IPv6 handover in cognitive radio networks H Anandakumar, K Umamaheswari, R Arulmurugan International Conference on Computer Networks and Communication Technologies … , 2018 2018.0 Citations: 29
Computational intelligence and sustainable systems H Anandakumar, R Arulmurugan, CC Onn EAI/Springer Innovations in Communication and Computing , 2019 2019.0 Citations: 26
An intelligent-based wavelet classifier for accurate prediction of breast cancer A Haldorai, A Ramu Intelligent Multidimensional Data and Image Processing, 306-319 , 2018 2018.0 Citations: 24
A Study Circle Process for Environmental Pollution and Management A Ramu, A Haldorai Journal of Enterprise and Business Intelligence, 247-258 , 2022 2022.0 Citations: 23
Techniques Advantages and Limitations of Neuroimaging: A Systematic Review A Ramu, A Haldorai Journal of Biomedical and Sustainable Healthcare Applications 54 , 2024 2024.0 Citations: 20
An optimal approach to enhance context aware description administration service for cloud robots in a deep learning environment R Subha, A Haldorai, A Ramu Wireless Personal Communications 117 (4), 3343-3358 , 2021 2021.0 Citations: 20
Business Intelligence for Enterprise Internet of Things A Haldorai, A Ramu, SAR Khan Springer International Publishing , 2020 2020.0 Citations: 20
Artificial intelligence and machine learning for enterprise management H Anandakumar, R Arulmurugan 2019 International Conference on Smart Systems and Inventive Technology … , 2019 2019.0 Citations: 20
Artificial intelligence and machine learning for future urban development A Haldorai, A Ramu, S Murugan Computing and Communication Systems in Urban Development: A Detailed … , 2019 2019.0 Citations: 20
