In a dynamic market which is getting more and more uncertain, efficient supply chain management has emerged as a challenge of utmost importance to organizations. Conventional optimization methods are not usually capable of adapting to the changing demands in real-time and complicated operational constraints. The paper introduces a predictive supply chain optimization model that uses AI and is developed on the basis of a new Hybrid Deep Reinforcement Learning algorithm (HDRL-SCO). The proposed solution will combine the application of Long Short-Term Memory (LSTM) networks to make precise demand forecasts with a Deep Q-Network (DQN) to make smart decisions. The system is able to learn the best policies to manage inventory, transportation planning, and order fulfillment, by constantly interacting with the environment by modelling the supply chain as a Markov Decision Process. As the experimental outcomes show, the suggested model will result in a considerable decrease in the overall operating costs, a higher level of services, increased inventory turnover as well as a decrease in the delays in the fulfillment process as compared to the traditional approaches. The framework is highly adaptive, scalable, and resilient to uncertain and dynamic environments and thus can be applied in the real-life supply chain.
B. Rajnarayanan, C. R. Usha, Venkata Appaji Sirangi et al.· International Conference on...· 0 citations
With the advent of seventh-generation (7G) wireless systems, the spectrum environment is extremely dynamic and heterogeneous, and traditional methods of sensing do not offer reliable and efficient performance. This paper introduces a self-evolving spectrum sensing system to enable adaptive and intelligent spectrum access based on a deep reinforcement-based learning paradigm. The framework depicts the sensing process as a sequence decision problem where an autonomous agent is able to continually refine its policy as it engages with the environment. The multi-objective reward formulation is designed to maximize the combination of the detection accuracy, false alarms, energy usage, and using the spectrum. Moreover, an adaptive representation of state mechanism is also introduced to indicate the temporal changes and short-term changes in the spectrum occupancy. The self-evolution strategy proposed adjusts learning parameters and decision policies in a dynamic manner that gives a robust operating in a non-stationary environment. The overall analysis of the experiment shows that the structure achieves detection probability of 97.1, false alarm rate reduced to minimum of 3.8, spectral usage maximized to above 92 and convergence rate is quicker as compared to the existing techniques. These results confirm the appropriateness of the proposed method in overcoming the issues of the next-generation wireless systems.
A.L Sriram, H. N. Divya, S. Sabarinathan et al.· International Conference on...· 0 citations
Finding cancer early and making a good treatment plan are both important for boosting survival rates. MRI, PET, and CT are advanced imaging techniques that have greatly improved cancer screening, staging, and therapy monitoring. However, their high costs and need for specialised equipment make them hard to get, especially in places with few resources. In this setting, optical imaging technologies are becoming affordable and portable options for finding cancer early. Image preprocessing techniques were used to improve data quality in order to deal with problems including dataset imbalance and noise. They used the Haar wavelet approach to extract features from medical photos that were important. A hybrid CNN-Transformer architecture was suggested, with four main parts: Shallow Feature Extraction (SFE), CNN/Transformer, Deep Feature Fusion (DFF), and up-sampling. This combination uses CNN to find local features and Transformers to find global dependencies, which makes deep feature learning strong and adaptable. The proposed model had an overall accuracy of 97.13%, a precision of 95.30%, a specificity of 96.74%, and an AUC of 98.74%. This shows that it is quite good at finding cancer. These results show that the model could provide accurate, quick, and easyto-use diagnostic solutions.
Deepika Upadhyay, S. Manikandan, R. V. S. Praveen et al.· International Conference on...· 0 citations
The fast uptake of electric vehicles (EVs) has resulted in the proliferation of EV charging stations, which place a great burden on the contemporary power distribution systems. The harmonic distortion, voltage instability, reactive power imbalance, and incredibly unpredictable load demand are the key issues that are involved, due to the various loading behavior of the charging users. More dynamic and nonlinear conditions are more likely to surpass the conventional control approaches and result in poor power quality and efficiency of the system. To mitigate such problems, in this paper, an intelligent framework of control-based power quality improvement in EV charging stations is proposed. The suggested system will be an integration of real-time sensing, advanced signal processing, and predictive control, which is run on machine learning to dynamically monitor and control electrical parameters. In order to achieve stable grid interaction, a closed-loop design is employed, comprising of sensing units, intelligent controllers, and power conditioning devices such as active power filters and voltage source converters. The smart controller continuously compares the voltage, current and harmonic components with real time and predicts load changes in the future according to the learning-based model. Based on this analysis, the corrective actions are developed to mitigate the disturbances such as sagging voltage, harmonic injection and reactive power variations. The major power quality measures such as voltage deviation, Total Harmonic Distortion (THD), and power factor improvement are used to assess the performance of the proposed system. The experimental results have indicated that the performance of the system has been greatly enhanced with a dramatic reduction in THD, improved voltage regulation at peak load conditions, and near unity power factor at the peak load. The suggested framework will guarantee efficient, reliable, and adaptable working of EV charging infrastructure, making it appropriate to be incorporated into the contemporary smart grid systems.
J. A., D. Mouli, P. Suresh et al.· 2026 International Conferenc...· 0 citations
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