A t umour is an uncontrolled growth of tissues in any part of the body. Tumours are of different types and characteristics and have different treatments. Detection of a tumour in the earlier stages makes the treatment easier. Scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumours. This paper provides a systematic literature survey of techniques for brain tumour segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques. This survey covers publicly available datasets, enhancement techniques, segmentation, feature extraction, and the classification of three different types of brain tumours that include gliomas, meningioma, and pituitary and deep learning algorithms implemented for brain tumour analysis. Finally, this survey provides all the important literature on the detection of brain tumours with their developments.

There is great diversity in the field of medical science due to computational power and technical innovation, especially in identifying human heart disease. Today it is one of the deadliest human heart diseases in the world and have very serious effects on human life. Accurate and timely identification of heart disease in humans can be very helpful in prevent heart failure in its early stages and will improve patient survival. Manual method for determining the heart disease is biased and can vary between researchers. In this regard, efficient and reliable machine learning algorithms resources for detecting and classifying people with heart disease and those who are healthy. According to suggestion in our study, we identified and predicted heart disease in humans using a variety of machine learning algorithms and using heart disease dataset to evaluate its performance using various measures, such as sensitivity, specificity, F-measure, and classifier accuracy. For this purpose, we used nine machine learning classifiers for the final dataset before and after hyper parameter tuning of machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. In addition, we verify their accuracy on a standard heart disease dataset by performing several standardized, pre-processing procedures of the data set and hyper parameter tuning. In addition, to train and validate machine learning algorithms, we implemented standard K-fold cross-validation technique. Finally, the experimental results show that the accuracy of the predictive classifiers with improved hyper parameter tuning and achieved remarkable results with data normalization and hyper parameter tuning of machine learning classification.

: Fatty acid vesicles are suspensions of closed lipid bilayers that are composed of fatty acids and their ionized species which are restricted to a narrow pH range. The fatty acid molecules are oriented in such a way that their hydrocarbon tails are directed toward the membrane interior and the carboxyl groups are in contact with water. Recent innovations provide an opportunity to formulate fatty acid vesicles with distinguishing features such as extension of pH range, insensitivity toward divalent cations, easy alteration in membrane composition, very simple systems in terms of chemical nature and enhanced stability properties. This review contains detail about the present stature of fatty acid vesicles, comparative study of fatty acid vesicles with conventional liposomes, unique features of fatty acid vesicles (dynamicity, stability, matrix effect etc.,) and key evaluation parameters of fatty acid vesicles. Fatty acid vesicles were found to have high penetration capacity, good bio-distribution properties, increased diffusion rate and optimum drug deposition nature than other vesicular forms. They have various applications in transdermal delivery, follicular delivery and in brain targeting drug delivery systems (drugs that are unable to cross blood brain barrier due to high solubility). This review focuses on various researches conducted on fatty acid vesicles with reference to its formulation and evaluation parameters. © 2020 iGlobal Research and Publishing Foundation. All rights reserved.

Job shop scheduling has always been one of the most sought out research problems in Combinatorial optimization. Job Shop Scheduling problems (JSSP) are categorized under NP hard problems. In recent years the meta heuristic algorithms have been proved effective to solve hardcore NP problem. Firefly Algorithm is one of such meta heuristic techniques which is nature inspired from firefly characteristic. Its potential can be enhanced further by hybridizing it with other known evolutionary algorithms and thereby getting improved results in less computational time. In this paper we have proposed a new hybrid technique christened as HyFA, by hybridizing Firefly algorithm(FA) with simulated annealing (SA) and Greedy heuristics approach (GHA). The hybrid technique has the advantages of all three algorithms and are combined in such a way that a quicker and better optimal solution is obtained. Our proposed HyFA is coded in Matlab with an objective to minimize the makespan (Cm). The novel hybrid technique is then used to evaluate 1-25 Lawrence problems taken from literature. The results show the proposed technique is more effective not only in getting optimal results but has significantly reduced computational time. Keywords: Scheduling, Optimisation, Job shop scheduling, Meta-heuristics, Firefly, Simulated Annealing, Greedy heuristics Approach.