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Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique.

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Machine recognition of faces is a biometric process in which face of a person is recognized by comparing the present image of a person with the image already present in the database. Demand is increasing rapidly as recognition is a vigorous research issue because of its non-copier characteristic. Compelling attention has been received by this technology because it has potential for tremendous applications like criminal identification, bank/store security, credit card verification, healthcare, marketing, automatic attendance etc. Face recognition is very secure method but its performance is degraded by some factors. Several researchers have recommended methods to nullify the effects of these factors. This paper provides a review on some effective 2D and 3D face images techniques with pose variations which are compared on the basis of recognition rates. From the discussed 2D face images techniques, recognition rate up to 100% was obtained by Kernal Canonical Correlation analysis (KCCA) only if input images are less than 200 images. If input images are more than 200 then 2D image based approach has higher recognition rate and is also simpler. From the discussed 3D techniques, recognition rate is highest of morphable model and also this technique is not affected by occlusion.

Wireless communications have been developed widely and rapidly in the modern world especially during the last decade. Orthogonal Frequency Division Multiplexing (OFDM) has grown to a popular communication technique for high speed communication. Besides of the advantages, one of main disadvantage of OFDM is high peak to average power ratio (PAPR). In this paper, a PAPR reduction method is proposed that is based on combining clipping with Walsh Hadamard Transform (WHT).WHT is a precoding technique which is having less complexity compared to the other existing power reduction techniques and also it can reduce PAPR considerably and results in no distortion.The performance of the proposed scheme is examined through computer simulations and it is found that power reductions are obtained.

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