A new optimization technique is proposed for solving optimization problems having single and multiple objectives, with objective functions such as generation cost, loss, and severity value. This algorithm was developed to satisfy the constraints, such as OPF constraints, and practical constraints, such as ram rate limits. Single and multi-objective optimization problems were implemented with the proposed hybrid fruit fly-based artificial bee colony (HFABC) algorithm and the non-dominated sorting hybrid fruit fly-based artificial bee colony (NSHFABC) algorithm. HFABC is a hybrid model of the fruit fly and ABC algorithms. Selecting the user choice-based solution from the Pareto set by the proposed NSHFABC algorithm is performed by a fuzzy decision-based mechanism. The proposed HFABC method for single-objective optimization was analyzed using the Himmelblau test function, Booth’s test function, and IEEE 30 and IEEE 118 bus standard test systems. The proposed NSHFABC method for multi-objective optimization was analyzed using Schaffer1, Schaffer2, and Kursawe test functions, and the IEEE 30 bus test system. The obtained results of the proposed methods were compared with the existing literature.

Recent developments in deep learning have made it possible to apply it to medical image segmentation. Deep convolutional neural networks (DCNN s) are a type of deep learning model used for segmenting medical images. This approach has been found to be effective in accurately segmenting medical images, and better results in faster segmentation times than traditional methods. DCNN s use a combination of convolutional layers, pooling layers, and activation functions to detect patterns in the input images and learn a set of features from it. DCNN s have the advantage of having fewer parameters compared to other methods. This makes them easily adaptable and portable across different applications. By using the latest advances in CNN s, more accurate segmentation can be achieved in a shorter time. This makes DCNNs a powerful tool for medical image segmentation.

The Distributed Generators (DGs) are connected in distribution system to reduce the power losses and to improve the reliability of the distribution system. The most important process to decrease the total power loss and to improve the reliability of the system is to identify the proper placement of DG units and also to find the amount of power to be generated by them. A hybrid technique is proposed which includes Particle Swarm Optimization (PSO) and Neural Network. By fixing DGs in suitable optimal locations and by generating power based on the load conditions, the total power loss in the system can be reduced and the system reliability can be improved. The proposed method is tested for different load conditions on IEEE 30 bus system, by connecting one DG, two DGs, three DGs and four DGs in the system. The results obtained show the improved voltage profiles and reliability indices like EENS and ECOST.

This paper provides a progressed convolutional neural community (CNN) version for medical picture segmentation. It specializes in improving the accuracy and speed of today's segmenting of scientific photos and improving modern semantic segmentation of first-rate present-day clinical photos. Modern accuracy is improved by introducing pre-processing layer, residual connection, attention module, and up sampling layer to the CNN. The stepped-forward overall performance state-of-the-art pace is achieved through dual-project cutting-edge, wherein the segmentation task is divided into two sequential sub-obligations, modern-day semantic segmentation and shape regression. The improved version is examined on publicly available scientific datasets, achieving 49a2d564f1275e1c4e633abc331547db segmentation performance. The results show that the proposed method is robust, green, and generalizable..

Background: Customers expect quality, uninterrupted power with cost-effective electricity in the latest trend. However, outages, severe storms, old infrastructure, and cost pressures can lead to ambiguity in power generation and transmission. To improve line power transmission capability, the right flexible AC transmission systems (FACTS) device may save millions of dollars. Methods: In this study, a FACTS controller named Optimal Generalized Interline Power Flow Controller (OGIPFC) was developed. Furthermore, for optimization, the Modified Marine Predator Algorithm (MMPA), which is a modification of the recently developed Marine Predator Algorithm (MPA). The optimum technique was used to evaluate a set of prioritized considered objective minimizations. A variety of factors must be maximized, such as generation cost, emissions, and power loss. Results: The performance of the proposed algorithm was analysed on benchmark test functions, and then single objective optimization problems of standard IEEE-30 bus system were solved and compared with the existing algorithms. The proposed algorithm was restricted to solving the single objective problem only, so it was further implemented with non-dominating sorting to solve the multiobjective optimization problem. The proposed multi-objective version is named as Non-dominating Sorting Modified Marine Predator Algorithm (NSMMPA), and it was validated on benchmark test functions and the IEEE-30 bus system. Conclusion: Finally, the OPF problem was solved with the incorporation of OGIPFC using the proposed methods, which resulted in better solutions and made the system more effective in operation.

Accurate Cardiac Arrest Risk Forecasting (ACARF), with Ensemble studying, is a predictive analytics method that may assist clinical experts in predicting the dangers of cardiovascular illnesses for their patients. This approach combines multiple knowledge of algorithms into an ensemble that can, as it should be, assess a man or woman's danger of cardiac arrest, thereby supporting personalized care. ACARF uses an extended function set to become aware of predictive information to maximize its effectiveness. It includes clinical information, clinical history, medications, and lifestyle choices. The version is then educated and evaluated to ensure the excessive accuracy of its predictions. The effects of the ensemble can then be used to inform clinicians of their sufferers' hazard for cardiac arrest and to broaden customized scientific interventions. The use of ACARF with Ensemble learning has been determined to significantly enhance the accuracy of predictions of cardiac arrest relative to the conventional unmarried-set of rules technique.