A Mathematical Model for Enhancing Cybersecurity in IoT Networks Using LSTM-Based Anomaly Detection and Optimization J Merlin Florrence Communications on Applied Nonlinear Analysis, 2025 The widespread adoption of Internet-of-Things (IoT) devices has heralded an explosion in potential attack surfaces with varying capabilities and a wide variety of vulnerabilities. Because of this, IoT networks have become a favorable choice for many cyber-attacks due to the difficulty and complexity that traditional cybersecurity approaches face in managing these types of networks with large numbers up to millions of users leading these attacks to pose significant risks from anomalous behavior. Current approaches such as rule-based intrusion detection system (IDS) and signature-based models are inadequate to be utilised in the dynamic IoT enclaves where they tend to generate too many false positives and may miss unknown threats. In this study, motivated by hybrid methods utilizing machine learning-based anomaly detection wrapped around optimization algorithms, we suggest a full-fledged mathematical model applying these possible solutions for cybersecurity in IoT networks. This model uses a variety of unsupervised learning techniques in order to detect and remediate new threats dynamically at run time. The models learn the optimal thresholds for detection and resource allocation to perform the fastest possible response under low resources constraints, by leveraging optimization algorithms like genetic algorithm or particle swarm optimization. It shows a significant enhancement in anomaly detection accuracy and reduction of false positives compared to conventional methods. Nevertheless, problems in measurement overhead, model scalability and management of big IoT environments characterized by low-computation resources are still being faced. Nevertheless, the proposed model is promising for large-scale applications (e.g., smart cities, industrial IoT, and healthcare applications) in a critical context where cyber threats should be detected on time to help guarantees integrity of operation. Our results indicate that a machine learning-based intrusion detection system in conjunction with optimization techniques can be developed into solid and adaptable cybersecurity infrastructure to safeguard the expanding IoT world.
An advanced hybrid algorithm (haDEPSO) for engineering design optimization integrating novel strategies for enhanced performance Utkal Surseh Patil, A. Krishnakumari, M. Saravanan, M. Muthukannan, Ramya Maranan, R. Rambabu Metaheuristics Algorithm and Optimization of Engineering and Complex Systems, 2024 This research presents haDEPSO, a pioneering hybrid technique for engineering design optimization. Combining the strengths of Differential Evolution (DE) and Particle Swarm Optimization (PSO), haDEPSO offers a versatile answer to the difficulties of contemporary optimization settings. The methodology combines a precise integration of DE's robust exploration capabilities with PSO's efficient exploitation tactics, ensuring adaptability across diverse problem environments. Through 10 trials, performance measures such as fitness function value, convergence speed, and diversity meter reveal haDEPSO's consistent optimization power. Scalability testing reveals the algorithm's effectiveness in addressing situations of varying sizes, yet challenges occur in particularly massive instances. These findings contribute to a deep knowledge of haDEPSO's strengths and restrictions, driving subsequent advancements for better applicability in engineering design optimization.
A Novel Approach to Predicting Personality Behaviour from Social Media Data Using Deep Learning International Journal of Intelligent Systems and Applications in Engineering, 2024
Deep Learning-Enabled Image Segmentation for Precise Retinopathy Diagnosis International Journal of Intelligent Systems and Applications in Engineering, 2024
Detection of Traffic Congestion from Surveillance Videos using Machine Learning Techniques S Govinda Rao, R RamBabu, B S Anil Kumar, V Srinivas, P Varaprasada Rao 6th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2022 Proceedings, 2022 Smart Cities applications, automated traffic control and management is the most trending research area. With the improving needs of developed towns and cities traffic congestion, now a days this the traffic congestion control and its applications has large needed facing problem in the increased population cities. Peeled eye camera photos and videos can be watched efficiently to detect traffic congestions in most of the areas in the grown populated cities. The earlier researchers had observed more on traffic signal controls through photos executed by using different algorithms of machine learning. There is existing research available on traffic signal controls through image processing and various machine learning methods. The image features are extracted and interpreted for the same. Deep learning algorithm, convolutional neural network (CNN) is proposed for effective detection of traffic congestion. There were existing works available in traffic detection using machine learning and deep learning approaches. Machine learning, Nowadays, traffic surveillance systems collect a lot of videos or images and store them for the live monitoring purposes. Deep learning techniques are used sparingly in traffic surveillance and control systems. Various images with various weather conditions are collected and are used as training dataset. Advantages of deep learning have been exploited in many applications, which use computer vision and image analysis. One of such applications is traffic monitoring, in which large amounts of video or images are processed for effective learning. The traffic surveillance can only monitor, which cannot detect the traffic on particular time.
Modified hierarchical clustering algorithms to evaluate the similarities of growth factor IR inhibitors by using regression analysis S Govinda Rao, R Rambabu, P VaraPrasada Rao 2018 4th International Conference on Computing Communication and Automation Iccca 2018, 2018 In the bioinformatics area it expose an amazing development at the crossroads of biology, medicine, information science, and computer science. The pictures neatly explain that nowadays in this field research is as reproductive in the data mining research. However, maximum bioinformatics research handles with the tasks of identification and classification, tree or network induction from data. Clustering techniques are mostly employed in the sector of information technology, medicine as well as bioinformatics.In this paper, the modified hierarchical clustering algorithms are introduced and applied to orthologous IGF-1R protein sequences and it can produce orthologous clusters of sequences and phylogenetic trees are formed Compared to existing hierarchical algorithms these new algorithms are very efficient, it takes less time to execute and clustering accuracy is also better.Another contribution is acceptable attempt has been made on understanding the role of IGF-1R. The outcome enabled research in extended further to delineate the dependency of Physio-chemical properties, on the activity of inhibitors, and to study the multivariate regression analysis on a set of 87 IGF-1R inhibitors are dependent variables and some of independent variables resulted in F-test: 8.812, r value: 0.794 and r2 value of 0.631, respectively. The data set was introduced for the presence of outliers by calculating the leverages and standard residuals and finally 6 compounds were eliminated. A new regression model was attempted 76 compounds training set and 5 compound validation set. A Regression plot is obtained and justifies the predictive ability of the regression model. Finally, the designing or screening compounds libraries for new analogues should enhance the inhibitory activity against IGF-1R.
A Novel Approach in Clustering Algorithm to Evaluate the Performance of Regression Analysis Govinda Rao S., Varaprasada Rao P., Rambabu R., Chandra Sekahar Reddy P. Proceedings of the 8th International Advance Computing Conference Iacc 2018, 2018 This paper, introduced a new methodology to raise the metric of a journal’s impact. This method is depending on finding clusters from SC Imago database and creates datasets utilizing a modified k-means clustering algorithm. Farther, developing of linear regression analysis on these datasets is perplexed by seeing index values are dependent variables and citation parameters as independent variables result in assessing contributing factors to increase bibliometric index of any journal. next step, cluster quality metrics enforced to evaluate the perfectness of fit of the cluster such as homogeneity score, completeness score, V measure, accommodated rand score and silhouette coefficient. The output of modified k-means algorithm on a dataset of 1445 journals resulted in 3 clusters (k=3). Each cluster data clustered depending on the title.The regression analysis states that the publisher who desires to enhance his journal bibliometric indexes should deliberate the advice conferred, in this work, bring large number of paper submissions to their journal especially. Almost four indices which are of main importance in the publisher industry having been used this. The analysis ensure in strong advantage as the testing of output produced including regression parameters clarified with the identification of outliers by the inclusion of relative error calculation. Accordingly, seeing the suggestive features with increase or decrease in TD3, TC3, CD3, CD2 and RD values, the publisher would profit from raising their respective bibliometric index.
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Quality and degree of pollution of groundwater, using PIG from a rural part of Telangana State, India NS Rao, B Sunitha, R Rambabu, PVN Rao, PS Rao, BD Spandana, ... Applied Water Science 8 (8), 227 , 2018 2018 Citations: 143
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Computational analysis and function prediction of a hypothetical protein 1RW0 R Rambabu, SR Peri, AR Allam Int. J. Comp. Bioinformatics and In Silico, 58-62 , 2012 2012 Citations: 6
Quality and degree of pollution of groundwater, using PIG from a rural part of Telangana State, India. Applied Water Science, 8, 227 N Subba Rao, B Sunitha, R Rambabu, PV Nageswara Rao, P Surya Rao, ... 2018 Citations: 5
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Modified Hierarchical Clustering algorithms to Evaluate the Similarities of Growth Factor IR Inhibitors by Using Regression Analysis SG Rao, R Rambabu, PVP Rao 2018 4th International Conference on Computing Communication and Automation … , 2018 2018 Citations: 2
A Novel Approach in Clustering Algorithm to Evaluate the Performance of Regression Analysis R Rambabu 2018 IEEE 8th International Advance Computing Conference (IACC), 47-52 , 2018 2018 Citations: 2
A group average cluster analysis of few IGF1R sequences using modified group average link clustering algorithm R Rambabu, PS Rao International Journal of Computer Applications 150 (11) , 2016 2016 Citations: 2
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LINEAR REGRESSION ANALYSIS AND VALIDATION STUDIES OF INSULIN-LIKE GROWTH FACTOR (IGF-1) RECEPTOR INHIBITORS R RAMBABU, DP SRINIVASA RAO i-manager’s Journal on Information Technology, Vol. 5 l No. 3 l June … , 2016 2016 Citations: 1
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