Vijay Anand Rajasekaran

Concept drift, data distributions shifting over time, degrades static machine-learning classifiers built on the independent and identically distributed (i.i.d.) assumption, raising false-negative rates across network intrusion detection, financial fraud analytics, and health monitoring. Adaptive methods exist to counter drift, but nearly all sacrifice interpretability, ruling them out for regulated deployments. Temporal Evolving Adaptive Learning with Explainable Artificial Intelligence (TEAL-XAI), a Temporal Evolving Adaptive Learning framework introduced here, maintains drift robustness, interpretability, and computational efficiency simultaneously rather than trading one for the others. It rests on three components: exponential temporal decay weighting [ w _t = exp(–λ ( T – t )), with an empirically validated optimal λ of 0.05]; adaptive sliding-window retraining that recalibrates its decision threshold at every window; and a closed-loop SHapley Additive exPlanations (SHAP) module whose feature attributions steer feature selection each retraining cycle, rather than only explaining results afterward. Tested against four baselines, including a four-layer Deep Neural Network (DNN), on Knowledge Discovery and Data Mining Cup 1999 (KDD Cup 1999), the Network Security Laboratory Knowledge Discovery in Databases (NSL-KDD), and the newer University of New South Wales—Network Benchmark 15 (UNSW-NB15) benchmark, TEAL-XAI reaches 99.73% accuracy, 99.71% F1-score, and 0.9998 area under the curve–receiver operating characteristic curve (AUC-ROC) on the first benchmark, with paired t -tests, Wilcoxon signed-rank tests, and McNemar's tests confirming significance ( p < 0 0.05 for all three tests; p < 0.005 for the t -test and McNemar's test). Under sudden concept drift, it is it is 9.9 and 10.5 times more resilient than Random Forest and the DNN baseline, respectively, losing 0.61 percentage points against their 6.04 and 6.41. On UNSW-NB15, it ties for the best F1-score (94.27%) among the five models. Still, it does not carry over the cross-validation and drift-resilience edge seen on the older benchmarks, a mixed result reported honestly rather than smoothed over. A six-configuration ablation study attributes gains to each component, temporal SHAP tracking shows that the explanations adapt rather than remain static, and cross-dataset transfer confirms that the framework generalizes beyond memorizing a single benchmark.

Precision medicine requires predictive models that can exploit genomic, clinical, imaging and continuously monitored physiological data at the same time, yet most existing models operate on a single modality and behave as black boxes. This paper proposes the Integrated Multi-Modal Contextual Network (IMCN), a predictive modelling framework that combines multi-modal transformer networks for cross-modal fusion, recurrent neural networks with attention for real-time sequential signals, pre-trained autoencoder networks for dimensionality reduction of high-dimensional genomic data, and a context-aware multi-task learning network for personalised risk and treatment predictions. SHapley Additive exPlanations (SHAP) are integrated to provide global and local feature attributions, so that clinicians can see which genomic markers, clinical variables and contextual factors drive each prediction. Across breast, lung, colorectal, cardiovascular, diabetic and chronic kidney disease cohorts derived from The Cancer Genome Atlas, the framework reports higher AUC, precision, sensitivity, recall and F1-score than the literature-reported benchmarks used for comparison, together with a reduction in false positives. Limitations, including the use of simulated physiological monitoring signals and the absence of independently re-implemented baselines, are stated explicitly.

This study presents a simulation-based approach to improving energy efficiency and security in IoT-based smart agriculture systems. The suggested architecture combines a new Blended Clustering Energy Efficient Routing (BCEER) protocol with an authentication mechanism based on Physical Unclonable Functions (PUFs) to respond to the main issues in resource-limited, sensor-oriented settings. BCEER maximizes cluster head selection using adaptive energy thresholds to minimize communication overhead and ensure extended network lifetime. Moreover, authentication based on PUF provides hardware-level identity authentication through challenge-response pairs, without demanding heavy cryptography computation, to improve security. For safe signal processing, polynomial approximation techniques were analyzed for sign function approximation, and error analysis indicated improving accuracy as the approximation degree grows. The findings show that the hybrid method enhances energy distribution, enhances authentication, and provides computationally efficient signal processing, making the system relevant for efficient and secure large-scale deployment in intelligent farming IoT networks.

There is an ever-growing need for optimizing efficient nano and quantum communication systems and this demand calls for more advanced optimization techniques than those that have been around for quite some time. The conventional genetic algorithms (GAs) proved to be quite helpful in general but proved to be poor when it comes to fast convergence and adaptability to the changing communication environment. Moreover, fault tolerance and resource efficiency in these communication channels are poorly addressed, giving rise to a huge roadblock to practical implementations in a number of different use cases. This research presents a set of novel hybrid optimization methods aimed at addressing the issues previously mentioned. In the first place, a Quantum-inspired Genetic Algorithm (QIGA) with Simulated Annealing (SA) Initialization is proposed through augmenting traditional genetic operations with the quantum computing paradigm. In the first instance, the hybrid approach works by utilizing the parameters derived from SA while initializing the GA population, hence effective exploration of solution space. Additionally, the Adaptive Genetic Algorithm with Reinforcement Learning (AGA-RL) is presented, which uses real-time feedback from the current environment to modify the GA parameters. This way, the system will be much more adaptive and hence be able to adjust the GA so that it can match the rapidly changing channel conditions. Simultaneously, the Fault-Tolerant Genetic Algorithm with Error-Correcting Codes (FTGA-ECC) is developed to ensure reliable data transmission, especially in an error-prone environment. To make up for resource constraints, the Energy-Aware Genetic Algorithm with Dynamic Resource Allocation (EAGA-DRA) strategy ensures that resources are well-managed by dynamically changing allocation based on the optimization process and the particular needs of the channel, hence reducing energy overhead and spatial overhead without compromising channel quality. A great leap is in the integration of Machine Learning through the Deep Reinforcement Learning-guided Genetic Algorithm (DRL-GA). The above approach exploits deep reinforcement learning to fine-tune the exploration and exploitation strategies of the GA, hence providing faster convergence and improved solution quality. All of these methods are validated by the Quantum-inspired Benchmarking Framework (QIBF), which forms a systematized comparative platform to evaluate different optimization techniques from the perspective of QCA-based communication systems. The above approaches not only provide the improvement in efficiency and reliability but also contribute to enhancing the adaptability of communication systems and provide a very good base for future research and development in the field of quantum communications and beyond, with potential applications extending to secure communication, quantum computing, and nanotechnology-based devices & scenarios. This work is multifold, promising significant advancements in the field of quantum communication and beyond. The empirical findings showed that the suggested algorithms perform significantly better than traditional approaches in several important performance metrics, such as robustness to channel errors, adaptability to changing signal-to-noise ratios, convergence speed under dynamic conditions, and solution quality in highly variable environments.

The burgeoning field of neurodegenerative disease detection and management necessitates the development of robust and comprehensive diagnostic approaches. Existing methodologies often fall short in effectively capturing the complex interplay of brain signals and genetic markers, which are crucial in the early detection and progression tracking of such diseases. This paper introduces a novel multimodal framework that leverages advanced signal processing and machine learning techniques to address these limitations, providing a more accurate and holistic understanding of neurodegenerative diseases. Our proposed model integrates multiple modalities: EEG signal analysis using Time-Frequency Analysis and Wavelet Transform, functional Magnetic Resonance Imaging (fMRI) analyzed through Independent Component Analysis (ICA) and Correlation Analysis, Magnetoencephalography (MEG) employing Beamforming and Source Localization Techniques, and Genomic Data analysis using Graph Neural Network for Genetic Pattern Recognition process. This integration is realized through the fusion of modalities using Gated Recurrent Units (GRU) and the classification into disease classes via an efficient 1D Convolutional Neural Network (CNN). The reasons for selecting these methods are twofold: they address the non-stationary characteristics of EEG signals and exploit spatial information of brain activity, while also identifying functional networks and genetic patterns associated with neurodegeneration conditions. The clinical impact of this work is profound. Tested on the BioGPS and BrainLat datasets, our framework demonstrated a 10.4% increase in precision, 8.5% increase in accuracy, 8.3% increase in recall, 9.4% increase in the Area Under the Curve (AUC), 7.5% increase in specificity, and a 2.9% reduction in delay compared to existing methods.

A neurological brain disorder that progresses over time is Alzheimer's disease. Alzheimer's disease can take years to identify, comprehend, and manifest—even in cases where signs are obvious. On the other hand, technological developments like imaging methods aid in early detection. But frequently, the results are unreliable, which delays the course of treatment. By dividing resting-state electroencephalography (EEG) signals into three groups—AD, healthy controls, and mild cognitive impairment (MCI)—this work offers a novel perspective on the diagnosis of Alzheimer's disease (AD). In order to overcome data limits and the over-fitting issue with deep learning models, we looked at augmenting the one-dimensional EEG data of 100 patients (49 AD participants, 37 MCI subjects, and 14 HC subjects) with overlapping sliding windows. Better results and early intervention could arise from this for persons afflicted with the illness. This research has the potential to significantly advance the early diagnosis of Alzheimer's disease and lay the groundwork for the creation of more precise and trustworthy diagnostic instruments for this debilitating condition. This study presents a Modified Deep Belief Network (MDBN) with a metaheuristic optimization method for detecting face expression and Alzheimer's disease using EEG inputs. The recommended method extracts significant features from EEG data in a novel way by applying the Improved Binary Salp Swarm Algorithm (IBSSA), which combines the MDBN and the metaheuristic optimization algorithm. The performance of the suggested technique MDBN-IBSSA for Alzheimer's disease diagnosis is evaluated using two publicly available datasets. The proposed technique's capacity to discriminate between healthy and ill patients is proved by the MDBN-IBSSA accuracy of 98.13%, f-Score of 96.23%, sensitivity of 95.89%, precision of 95.671%, and specificity of 97.13%. The experimental results of this study show that the MDBN-IBSSA algorithm proposed for AD diagnosis is effective, superior, and applicable.