With the rapid increase in internet users and customer reviews playing the major role in social media gave rise to sentiment analysis. Pre-processing of input text during sentiment analysis eliminates the incomplete and noisy data. Typically, sentiment is manifested separately and applying a pre-processing model for optimizing the cross-domain sentiment classification is highly required. In this paper, a method called Hidden Markova Continual Progression Cosine Similar (HM-CPCS) is proposed to explore the impact of pre-processing and optimize sentiment analysis. First, a measure of subsequent and antecedent probabilities of tags is made using Hidden Markova POS Tagger for the given input dataset. Subsequent and antecedent probabilities of tags are obtained by measuring the transition probabilities between states (i.e. domains) and observations (i.e. review statements) ensuring feature extraction accuracy. Next, the Continual Progression Stemmer continuously stems the text by adding prefix and suffix to form structured words for the given shortcuts and therefore reduce Error Rate Relative to Truncation (ERRT). Finally a Cosine Similarity function is applied to remove stop word for cross-domain sentiment analysis and classification. The performance evaluation of HM-CPCS method is done with standard benchmark data sets of consumer product and services reviews extracted from Sentiwordnet. The parameters used in evaluation are number of customer review words, execution time, accuracy and error rate. Experimental analysis shows that HM-CPCS method is able to reduce the time to extract the opinions from reviewers by 46% and improve the accuracy by 9% compared to the state-of-the-art works.

The growth performance and intracellular lipid production potentiality of two identified yeast stains Candida tropicalis (S5) and Issatchenkia orientalis (D5) in N-limited medium under different carbon, nitrogen sources, carbon/nitrogen ratio and temperature was studied . Among all carbon sources, glucose was the best in lipid weight, lipid content, lipid yield and lipid productivity and recorded a large values (5.73&6.51gl-1) of cell biomass in both strains. The increases in cell biomass reached its maximum value by use of yeast extract in both strains (8.62 and 8.78gl-1, respectively). However, urea was the preferable N source for lipid production in both strains. On the other hand, KNO3 proved to be the lowest values treatment among all parameters. The interaction treatments yeast extract + (NH4)SO4 and Urea + peptone were unfavorable for cell biomass, but it was convenient for oil yield parameters in Candida. The data showed that the maximum cell biomass was obtained at 54 and 100 C/N ratio for Candida and Issatchenkia, respectively. The data also exhibited that designated sharp increases in lipid weight, lipid content and lipid productivity were developed by the two yeast strains reached their maximum values at C/N ratio 115 and 100, respectively. Further increase in C/N ratio resulted in drop in lipid weight; this drop was slight in Candida above 115, whereas it was drastically in Issatchenkia after C/N 100.

Abstract: In currently, the revolution in a high-speed broadband network is the requirement and also endless demand for high data rate and mobility. To achieve above requirement, the 3rd Generation Partnership Project (3GPP) has been established the Long Time Evolution (LTE). LTE has established an improved LTE radio interface named LTE-Advanced (LTE-A) and it is a promising technology for providing broadband, mobile Internet access. But, better Quality of Service (QoS) to provide for customers is the main issue in LTE-A. To reduce the above issue, the packets should be utilized by using one of the most significant function of packet scheduling to upgrading system performance via determines the throughput performance. In existing scheme, the user with poor Channel Quality Indicator (CQI) has smaller throughput issue is not focused. In this paper, a Hybrid Weighted Round Robin with Shortest Job First (HWRR-SJF) Scheduling technique is proposed to enhance efficient throughput and fairness in LTE system for stationary and mobile users. In this proposed scheduling, to schedule users according to a different criterion like fairness and CQI. HWRR-SJF Scheduling has been proposed for scheduling of the users and it produces increased throughput for various SNR values simulated alongside Pedestrian and Vehicular moving models. The proposed method also uses a 4G-LTE filter or Digital Dividend (DD) in order to align the incoming signal. The digital dividend is used to remove white spaces, which refer to frequencies assigned to a broadcasting service but not used locally. The proposed model is very effective for users in terms of the performance metrics like packet loss, throughput, packet delay, spectral efficiency, fairness and it has been verified through MATLAB simulations

Abstract: In Unstable Mobile nodes in network does not maintain accuracy of data transmission as maximum level, since nodes characteristics are updated, then nodes receive data’s are intruded, its packet information is missed. For that time, congestion is made for current routing path, so consider that path is failure, also provide re transmission. It occupies more energy, and packet drop rate. In proposed Enhanced data Accuracy based Path Discovery (EAPD) technique is used to provide transmitting and receiving data has higher accuracy. It verifies the every node communication in routing path have maximum data accuracy, they are selected, otherwise communication data have minimum data accuracy is rejected. Backing route selection algorithm is constructed to avoid intrusion for communication period, it discovery the path, which are not loss the data from packets, since congestion is easily identified. It reduces energy consumption, and packet drop rate.

Abstract: The future generation of wireless system is expected to provide multi class services, multimedia at any time anywhere with seamless mobility and Quality of Service (QoS). In such environment, optimal vertical handoff is a challenging issue. Unnecessary handoff causes wastage of network resources and thus affects the QoS of network. To reduce the handoff, in this paper, a Modified Cat Swarm Behavior based Optimization (MCSBO) based handoff algorithm is proposed in heterogeneous wireless mobile network. Initially, resource-poor mobile nodes, and resource-rich mobile nodes are clustered using Modified Expectation Maximization (MEM) to reduce the handoffs efficiently. Here, parameters like packet loss rate, dynamic new calls blocking probability, Bandwidth, and Cost along with the velocity of the Mobile Terminal (MT) are considered for the design of the Handoff algorithm. The Membership Functions (MFs) for each of the parameter are determined and corresponding Membership Degrees are evaluated from their concerned MFs. The optimized fuzzification of the parameters is obtained via developing the appropriate weight vector. The weight vector is optimized using MCSBO algorithm. Experimental results show that the proposed algorithms attained best performance bandwidth utilization, handoff dropping rate and handoff rate compared to existing handoff decision vector algorithms.

Bioremediation of pesticides is imperative for a sustainable environment. For this purpose soil borne, pure fungal strains; Aspergillus niger and Penicillium chrysogenum were augmented in soils spiked with herbicide, Chlorsulfuron from four distinct regions of Pakistan. These strains were found to utilize Chlorsulfuron as their carbon and energy sources. Solid-liquid extraction of pesticide was followed by analysis through high performance liquid chromatography and gas chromatography mass spectrometry-SIM (selected ion monitoring mode). The use of chromatographic techniques for analysis of Chlorsulfuron and its transformation products is of paramount importance. The SIM mode enhanced the sensitivity of GCMS momentously, subsequently distinguishing chemicals even in lower detection limits. Chemical hydrolysis experiments, performed on the same soils were also found to degrade Chlorsulfuron (50%) but to a lesser extent than biodegradation (76 and 74% by both strains). Degradation rate followed first order reaction kinetics. Two major metabolites were obtained after degradation; 2-chlorobenzenesulfonamide and 2-amino-4-methoxy-6-methyl-1,3,5-triazine. Aspergillus niger degraded Chlorsulfuron (76%) slightly more than Penicillium chrysogenum (74%). R2 for degradation rates for all soil samples by both fungal strains were close to 1 and P values were less than 0.05, indicating significance of results.