E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

Test analysis of detection of damage to a complicated spatial model structure

A two-stage damage detection approach is proposed and experimentally demonstrated on a complicated spatial model structure with a limited number of measurements. In the experiment,five known damage patterns,including 3 brace damage cases and 2 joint damage cases,were simulated by removing braces and weakening beam鈥揷olumn connections in the structure. The limited acceleration response data generated by hammer impact were used for system identification,and modal parameters were extracted by using the eigensystem realization algorithm. In the first stage,the possible damaged locations are determined by using the damage index and the characteristics of the analytical model itself,and the extent of damage for those substructures identified at stage I is estimated in the second stage by using a second-order eigen-sensitivity approximation method. The main contribution of this paper is to test the two-stage method by using the real dynamic data of a complicated spatial model structure with limited sensors. The analysis results indicate that the two-stage approach is ableto detect the location of both damage cases,only the severity of brace damage cases can be assessed,and the reasonable analytical model is critical for successful damage detection.

A proposed approach for detecting terahertz pulses by using double few-cycle laser pulses with opposite carrier envelope phases

Previous research shows that few-cycle laser（FCL） pulses with low energy and without a bias field can be used to coherently detect terahertz（THz） pulses. As we know, it is very difficult to stabilize the carrier envelope phase（CEP） of FCL pulses, i.e., there are some random fluctuations for the CEP. Here we theoretically investigate the influence of such instability on the accuracy of THz detection. Our results show that although there is an optimum CEP for THz detection, the fluctuations of the CEP will lead to terrible thorns on the detected THz waveform. In order to solve this problem, we propose an approach using two few-cycle laser pulses with opposite CEPs, i.e., their CEPs are differed by π.

Intuitive approach towards detection of attention tunneling while using a head-up display

Head-up display （HUD）, a primary cockpit display, helps in optimizing a pilot＇s attention towards aircraft and outside events. Slight mismatch in the balance may cause missed events; this phenomenon is called attention tunneling and affects the situational awareness of the pilot. This work reports an intuitive approach to detect attention tunneling while use of HUD in aircrafts. Texture analysis of a composite HUD camera video provided three distinguishing parameters, viz., contrast, correlation, and homogeneity. These three texture parameters are used as inputs for a fuzzy inference-based assistive detection system which could be used for distinguishing tunneled and nontunneled HUD operation.

A forced alignment approach to detect Chinese repetitive stuttering

A forced alignment based algorithms to detect Chinese repetitive stuttering is studied. According to the features of repetitions in Chinese stuttered speech,improvement solutions are provided based on the previous research findings.First,a multi-span looping forced alignment decoding networks is designed to detect multi-syllable repetitions in Chinese stuttered speech.Second,branch penalty factor is added in the networks to adjust decoding trend using recursive search in order to reduce the error from the complexity of the decoding networks. Finally,we re-judge the detected stutters by calculating confidence to improve the reliability of the detection result.The experimental results show that compared to previous algorithm,the proposed algorithm can improve system performance significantly,about 18%average detection error rate relatively.

DETECTION OF PLASMODIUM FALCIPARUM LACTATE DEHYDROGENASE A PROMISING APPROACH TO QUICK DIAGNOSIS OF MALARIA

To establish a quick and applicable diagnostic method has been a major target in immunological research on malaria. Of all approaches studied, circulating antigen (CAg) detection seems to be the most promising. Scientists have encountered tough difficulties in the analysis and purification of malaria parasite antigen because of its high complexity. Studies showed that plasmadium falciparum (P.f) lactate dehydrogenase (pLDH), as a specific CAg of malaria parasite. is apparently different from that of human erythrocytes (rLDH) both in their physical and biochemical characteristics. and is very easy to be identifed. Consequently, pLDH detection has a great potential to be developed into a method for assessing parasitemia. This paper reviewed the methods for the purification. separation, identification of pLDH and the prospect of its clinical applications as an ideal detector of the presence of malaria parasite in order to speed up this research.