Design of Optimized Wavelet Packet Algorithm to Improve Perception of Sensorineural Hearing Impaired

A novel optimized wavelet packet algorithm is proposed to improve the perception of sensorineural hearing-impaired people. In this work, we have developed optimized wavelet packet along with, biorthogonal wavelet basis functions using MATLAB Code. Here, we have created eight bands based on auditory filters of quasi octave bandwidth. Evaluation was carried out by conducting listening tests on seven subjects with bilateral mild to severe sensorineural hearing loss. The speech material used for the listening test consisted of a set of fifteen nonsense syllables in VCV context. The test results show that the proposed algorithm improves the recognition score, speech quality and transmission of overall feature specifically over the unprocessed signal. The response time also reduces significantly.

Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

Nowadays,virtual machine migration(VMM)is a trending research since it helps in balancing the load of the Cloud effectively.Several VMM-based strategies defined in the literature have considered various metrics,such as load,energy,and migration cost for balancing the load of the model.This paper introduces a novel VMM strategy by considering the load of the Cloud network.Two important aspects of the proposed scheme are the load prediction through the support vector regression(SVR)and the optimal VM placement through the proposed dragonfly-based crow(D-Crow)optimization algorithm.The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm(CSA)into dragonfly algorithm(DA).Also,the proposed VMM strategy defines a load balancing model based on the energy consumption,load,and the migration cost to achieve the energy-aware VMM.The simulation of the proposed VMM strategy is done based on the metrics such as load,energy consumption,and the migration cost.From the results,it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%,10.0368%,and 11.0639%for the load,energy consumption,and migration cost,respectively.

A hybrid model for energy efficient spectrum sensing in cognitive radio

Purpose-Cognitive radio(CR)plays a very important role in enabling spectral efficiency in wireless communication networks,where the secondary user(SU)allows the licensed primary users(PUs).The purpose of this paper is to develop a prediction model for spectrum sensing in CR.Design/methodology/approach-This paper proposes a hybrid prediction model,called krill-herd whale optimization-based actor critic neural network and hidden Markov model(KHWO-ACNN-HMM).The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs.For better sensing,the eigenvalue based on cooperative sensing used in CR.Finally,a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing.The predicted results of KHWO-ACNN and HMM are combined by a fusion model,for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.Findings-The performance of the prediction model is evaluated based on metrics,such as probability of detection,probability of false alarm,throughput and sensing time.The proposed spectrum sensing method achieves maximum probability of detection of 0.9696,minimum probability of false alarm rate as 0.78,minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.Research implications-The proposed method is useful in various applications,including authentication applications,wireless medical networks and so on.Originality/value-A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models.The proposed hybrid model outperformed the other techniques.

Curcumin:reclaiming the lost ground against cancer resistance

Curcumin, a polyphenol, has a wide range of biological properties such as anticancer, antibacterial, antitubercular, cardioprotective and neuroprotective. Moreover, the anti-proliferative activities of Curcumin have been widely studied against several types of cancers due to its ability to target multiple pathways in cancer. Although Curcumin exhibited potent anticancer activity, its clinical use is limited due to its poor water solubility and faster metabolism. Hence, there is an immense interest among researchers to develop potent, water-soluble, and metabolically stable Curcumin analogs for cancer treatment. While drug resistance remains a major problem in cancer therapy that renders current chemotherapy ineffective, curcumin has shown promise to overcome the resistance and re-sensitize cancer to chemotherapeutic drugs in many studies. In the present review, we are summarizing the role of curcumin in controlling the proliferation of drug-resistant cancers and development of curcumin-based therapeutic applications from cell culture studies up to clinical trials.