Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm.

The one-dimensional cutting-stock problem (1D-CSP) consists of obtaining a set of items of different lengths from stocks of one or different lengths, where the minimization of waste is one of the main objectives to be achieved. This problem arises in several industries like wood, glass, and paper, among others similar. Different approaches have been designed to deal with this problem ranging from exact algorithms to hybrid methods of heuristics or metaheuristics. The African Buffalo Optimization (ABO) algorithm is used in this work to address the 1D-CSP. This algorithm has been recently introduced to solve combinatorial problems such as travel salesman and bin packing problems. A procedure was designed to improve the search by taking advantage of the location of the buffaloes just before it is needed to restart the herd, with the aim of not to losing the advance reached in the search. Different instances from the literature were used to test the algorithm. The results show that the developed method is competitive in waste minimization against other heuristics, metaheuristics, and hybrid approaches.

A novel intelligent strategy-based thermodynamic modeling and analysis of solar-assisted vapor absorption refrigeration system.

In recent times, solar energy has been utilized for refrigeration systems due to its efficiency and clean form of energy. Moreover, the evacuated tube collector (ETC)-assisted vapor absorption refrigeration system plays a significant role in the modern industrial world compared to the traditional electrical system. However, the conventional vapor absorption refrigeration system design is complex in nature and causes corrosion in the system. Therefore, in this research, novel Generalized Approximate Reasoning-Based Intelligent Control (GARIC) and Hybrid Ant Colony African Buffalo Optimization (HACABO) methods based on an ETC-linked 5-kW vapor absorption refrigeration system are proposed depending on lithium bromide-water (LiBr-H2O). Primarily, the Haryana region's solar radiation and weather parameters were taken over a year to simulate the ETC system. ETC collects the solar energy for the refrigeration cycle, and the efficiency of the ETC is estimated using the GARIC method as per the input of solar radiation, collector area, and used solar energy. Moreover, the efficiency of the ETC is optimized using the proposed HACABO method. The modified polynomial fits curved equation is utilized for performance analysis. The simulation model of the solar cooling absorption system is carried out in the MATLAB platform. The coefficient of performance (COP) rate of the absorption cycle has gained 0.82% with the help of HACABO. Compared to other recent associated models, the proposed model has maximized the COP in the finest range.

Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique.

Breast cancer is one of the primary causes of death that is occurred in females around the world. So, the recognition and categorization of initial phase breast cancer are necessary to help the patients to have suitable action. However, mammography images provide very low sensitivity and efficiency while detecting breast cancer. Moreover, Magnetic Resonance Imaging (MRI) provides high sensitivity than mammography for predicting breast cancer. In this research, a novel Back Propagation Boosting Recurrent Wienmed model (BPBRW) with Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) mechanism is developed for detecting breast cancer in an earlier stage using breast MRI images. Initially, the MRI breast images are trained to the system, and an innovative Wienmed filter is established for preprocessing the MRI noisy image content. Moreover, the projected BPBRW with HKH-ABO mechanism categorizes the breast cancer tumor as benign and malignant. Additionally, this model is simulated using Python, and the performance of the current research work is evaluated with prevailing works. Hence, the comparative graph shows that the current research model produces improved accuracy of 99.6% with a 0.12% lower error rate.