Autonomous agents—systems that make independent decisions without human input—are foundational to modern robotics and enable intelligent responses in dynamic situations. In this work, a hybrid agent-based system that integrates software agents, or programs that represent users with multiple decision-making modules. The design integrates perception (gathering and interpreting sensory data), planning (scheduling a sequence of actions), and reinforcement learning, in which agents use feedback from their surroundings to improve their actions through try and error. Mathematical modelling and experimental evaluation reveal efficiency gains over rule-based systems that rely only on predefined instructions. Consequently, the framework ensures adaptability, scalability, and robustness in uncertain, unpredictable environments. Results show a 17% higher success rate and reduced execution time.
S. K, Sheetal Kusal, Usha Desai· 2026 6th International Confe...· 0 citations
Accurate channel estimation remains a fundamental bottleneck in the performance of any coherent Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) receiver, particularly when the system is required to operate over a wide range of signal-to-noise ratios (SNRs) and under multipath fading. In this paper, we present the design, implementation, and experimental validation of a complete MIMO-OFDM transceiver running on two Raspberry Pi 4 single-board computers connected over a Wi-Fi link, in which the conventional Least Squares (LS) channel estimator is enhanced with a four-layer feedforward Deep Neural Network (DNN). The transmitter supports adaptive Quadrature Amplitude Modulation (QAM) schemes ranging from 16-QAM to 256-QAM, which can be selected by the user through a browser-based Flask dashboard. At the receiver, the bit error rate (BER) is computed in real time, while the active processing stage is displayed on an onboard 16×2 LCD. The DNN was trained offline using 100,000 synthetic complex channel samples and reduces the channel estimation mean squared error (MSE) from 0.1810 (LS) to 0.1676, corresponding to an improvement of approximately 0.33 dB in MSE. This improvement translates into an equivalent signal-to-noise ratio (SNR) gain of approximately 1.0–1.5 dB over the LS baseline in the 22–30 dB region of the BER-versus-SNR curve for 256-QAM. The end-to-end system reliably transmits text, grayscale images, and parallel text-and-image streams across the configured channel models. To the best of our knowledge, this work represents one of the first hardware-validated demonstrations of DNN-assisted OFDM channel estimation on a low-cost embedded platform.
Twinkle Srusti J K, Padmajadevi G, D. K C et al.· 2026 6th International Confe...· 0 citations
Compact 28/38 GHz mmWave antennas enable high-performance 5G MIMO communications. This paper presents the design and optimization of 28/38 GHz millimeter-wave (mmWave) antennas for high-performance 5G applications. The proposed antennas utilize patch and MIMO array configurations on low-loss substrates like Rogers RT5880 to deliver high gain and efficient radiation patterns. Defected Ground Structures (DGS) and parasitic elements enhance gain (> 7 dBi), minimize mutual coupling, and achieve an Envelope Correlation Coefficient (ECC) < 0.005, ideal for MIMO systems. Validation via CST Studio Suite simulations confirms return losses below −10 dB, radiation efficiency exceeding 80%, and excellent isolation in arrays. These antennas address path loss and atmospheric absorption in 5G mmWave networks, enabling ultra-high data rates for mobile devices, IoT, and next-generation wireless systems. The framework supports extensions to multi-band operation, beam-steering, and scalable MIMO configurations with sustained efficiency and low element correlation.
Mahesha S, Sushma N, D. C et al.· 2026 International Conferenc...· 0 citations
The rapid expansion of cloud computing and large-scale data centers has significantly increased energy consumption and carbon emissions, creating critical sustainability concerns for modern computing infrastructures. This paper proposes the Adaptive Carbon-Aware Virtualized Energy-efficient Scheduling (ACAVES) framework to improve resource utilization and reduce environmental impact in cloud environments. The framework combines workload monitoring, task classification, virtual machine consolidation, carbon-aware scheduling, and energy optimization within an integrated architecture. An adaptive scheduling mechanism allocates workloads according to utilization patterns, energy requirements, and carbon emission estimates. Experimental evaluation was performed using heterogeneous workloads containing 10,000 tasks executed over 50 physical servers and 200 virtual machines. Results demonstrate that the proposed ACAVES framework reduced energy consumption from 520 kWh to 385 kWh and carbon emissions from 310 kgCO2 to 215 kgCO2. Additionally, server utilization improved from 68% to 87%, while average task completion time decreased from 820 ms to 670 ms, confirming the effectiveness and scalability of the proposed sustainable scheduling framework.
S. K, Kishore Bitra, Usha Desai· 2026 International Conferenc...· 0 citations
Diabetes has become a health problem worldwide. It often goes unnoticed until it causes health issues. Finding diabetes early using a lot of health and personal data can help reduce the diseases impact and healthcare costs. This study proposes a machine learning system for diabetes prediction. This system uses techniques to prepare data select important features handle unequal class distributions and combine multiple models. It is designed to process types of data from Electronic Health Records (EHRs) lifestyle factors and clinical measurements efficiently. Multiple machine learning models, for example tree-based classifiers, simple linear models and combined models are. Tested. Cross-validation is used to ensure the models are reliable and can be scaled up. The prediction of diabetes mellitus is based on identifying factors, so the importance analysis of characteristics is used to find the most influential predictors of diabetes. Oversampling of medical data involves the use of oversampling to overcome the problem of class distributions. The findings indicate that the given approach is more accurate, precise, possesses higher recall and F1-score, as well as ROC-AUC, compared to other models. This developed system offers an understandable solution for assessing diabetes risk early. It can be used in healthcare screening systems and clinical decision-support platforms for diabetes mellitus.
Thatikonda Krishna Kalyan Gupta, Oruganti Yashwanth Reddy, I. S et al.· 2026 International Conferenc...· 0 citations
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations