Trail and punishment of the head of states that commit international crimes were faced with many impairs\nand problems. Among these, the most important legal impair is criminal immunity. The history has shown that these\naccused have generally had s sufficient immunity and not ever s submit to justice. Nullification of the criminal\nimmunity of the head of states is necessary because of matters including public international order, international\npeace and security, domestic international order, prevention of non-punishment of the criminals and deterrence so\nthat prevent of non-punishment of the criminals. Effective implementation of prosecution and trail of the head of the\nstates and nullification of their immunity deals with some impairs, constrains, costs and challenges. Among these\nimpairs are the non-existence of necessary and suitable statutes, non-willing of states and marshaling evidence.\nThese impairs should be identified and rectifies by states. This paper is designed in two parts. The first one deals\nwith necessities of nullification of the criminal immunity and the second part analysis the impairs and constrains of\npunishment of the head of states.

Speaker recognition systems are some kind of biometric systems used in security applications based on remote control phones. This paper presents an approach to balancing the increase in the number of speakers, runtime and error rate of the system. The efficiency of these systems often depends on a particular number of people.With a sudden increase in the number of speakers in terms of time, the recognition error rate experiences a sharp rise. This paper introduces a parameter to select the optimal number,noisy samples for training and testing the Support Vector Machine (SVM) classifier.This approach aims to significantly increase learning speed andto test speaker recognition systems by setting noisy parameters for learning the model and recognizing people. In the first stage,we use the ReliefF algorithm to select the optimal feature subset, then the SVM classifier to achieve better accuracy in recognition by changing existing SVM kernels and automatically setting the balance parameter during training. This method is evaluated based on the accuracy/(Total time ) ratio. The results show that by setting the balance parameter compared with non-setting, above ratio always shows a higher value.

A mobile ad hoc network (MANET) consists of a set of mobile hosts that can communicate with each other without the assistance of base stations. In MANETs, the dynamic nature of the network topology is a major reason for link failures. In this paper, we proposed the Ad hoc on-demand distance vector (AODV) routing (FLAODV) protocol for routing in mobile ad hoc networks to increase durability path. The performance of this routing protocol is studied using opnet10.5. The simulation results of the FLAODV show that the protocol is quite efficient and superior to AODV with the respects to the average Route Discovery Time, the average Packets overhead, average throughput and average end-to-end delay.

Ad hoc networks are dynamically configurable wireless networks that have no fixed infrastructures and do not require predefined configurations. \nIn large, distributed systems, like ad hoc networks, centralized learning of routing or movement policies may be impractical. We need to employ learning algorithms that can learn independently, without the need for extensive coordination. A search for alternative methods of routing packets has resulted in reinforcement learning (RL) as a good approach to adaptive routing. RL methods are able to learn and adapt to a unknown and changing environment. In This paper we propose a new intelligent routing protocol with RL approach for wireless ad hoc networks. The Intelligent Wireless Ad Hoc Routing protocol (IWARP) is an on-demand protocol and self configuring. It selects optimal routes based on local information and past experience.\nThe proposed scheme uses the distributed Q-Learning framework, to select a stable route in order to enhance system performance. Our study also compares the performance of the IWARP protocol with the well-known Ad hoc On-Demand Distance Vector (AODV) protocol, Results obtained by a simulation campaign show that IWARP increases the throughput and decreases the data dropped and number of hops per route.

In this article, a class of complex harmonic meromorphic functions with positive coefficients is introduced by making used of basic hypergeometric functions. We consider some properties such as coefficient inequality, growth theorems and extreme points.

Sentiment analysis or Opinion mining is the process of detecting the subjective information in given text. Text may include subjective information like opinions, attitudes and feelings. Sentiment analysis also has an important potential role as enabling technologies for other systems. This paper employed two semi supervised probabilistic approaches called JST model and Reverse JST model to detect sentimental topic. The system designed in this paper classifies positive and negative labels of an online review. In JST, the document level sentiment classification is based on topic detection and topic sentiment analysis. JST process, the sentiment labels are associated with documents, the topics are generated dependent on sentiment distribution and words are generated conditioned on the sentiment topic pair. In Reverse JST, the sentiment label is dependent on the topics. In this process, where the topics are associated with documents, the sentiment labels are associated with topics and words are associated with both topics and sentiment labels. In LDA, where topic are associated with documents and words are associated with topic distribution. JST and Reverse JST are evaluated on four different domains using the Gibbs Sampling Algorithm. The nature of JST makes it highly portable to other domains. It compares JST and Reverse JST with latent dirichlet allocation. In this paper observed topic and topic sentiment detected by JST are indeed coherent and informative.

Propose:\nThe main drawback of current antibiotic therapies is the emergence and rapid increase of antibiotic resistance. Nocardiae are aerobic, Gram-positive, catalase positive, non motile actinomycetes. N. brasiliensis was reported as antibiotic producer. Propose of the study was to determine antibacterial activity of N. brasiliensis PTCC 1422 against isolated Enterobacteriaceae from Urinary Tract Infections (UTIs). \n\nMethod:\nThe common bacteria from UTIs were isolated from hospital samples. Antimicrobial susceptibility test was performed for the isolated pathogens using Kirby-Bauer disk diffusion method according to clinical and Laboratory Standards Institute guideline. Antagonistic activity of N. brasiliensis PTCC 1422 was examined with Well diffusion methods. Supernatant of N. brasiliensis PTCC 1422 by submerge culture was analysed with Gas chromatography mass spectrometry (GC-MS).\n\nResults:\nIsolated strains were included Escherichia coli, Klebsiella pneumoniae, Serratia marcescens and Proteus mirabilis. The most common pathogen isolated was E. coli (72.5 %). Bacterial isolates revealed the presence of high levels of antimicrobial resistances to Ceftriaxone and low resistance to Cephalexin. Supernatant of N. brasiliensis PTCC 1422 showed antibacterial activity against all of the isolated microorganisms in Well diffusion method. \n\nConclusion:\nAs antibacterial resistance among bacterial uropathogen is an evolving process. Therefore, in the field on the need of re-evaluation of empiric treatment of UTIs. our present investigation indicates the substances present in the N. brasiliensis PTCC 1422 could be used to inhibit the growth of human pathogen. The study has demonstrated that N. brasiliensis PTCC 1422 has a high potential for the treatment of Urinary Tract Infections (UTIs).