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Usage of complex words causes significant problems not only in reading but in writing as well and eventually leads to poor academic achievement of students, poorer particularly for hearing impaired children. The dual diagnosis of Autism Spectrum Disorder (ASD) and hearing impairment pose additional challenges mainly due to the difficulties that come with making accurate decisions. Hence, parents must be provided with the information about the signs and symptoms of ASD and deafness or partial hearing loss, as well as appropriate intervention strategies. Although different learning activities can be used to enlarge such children’s vocabulary, if the presented words are difficult to learn, it will be very hard to realize this. Identifying difficult words and replacing them with simple ones both make the readability of a text easier and help such children enhance their vocabulary knowledge in a shorter period of time. Therefore, in this study we propose a classification approach that identifies difficult words among a given set of words in English. The lexical and semantic features of the words in the dataset were extracted based on the language rules specific to hearing impaired children. In the classification approach, five popular classification algorithms were used and the algorithms' performance in identifying difficult words was evaluated using various performance metrics. As the results show, the K-Nearest Neighbors algorithm is the most suitable algorithm for identifying difficult words in English for the target group.

Strong liquid explosives were obtained by mixing some chemical liquids and these explosives were used in many terrorist attacks in crowded places such as airports, railway stations and shopping malls. They were also used to cause sabotage to facilities that produce, store or use hazardous chemicals in their processes. For this reason, it is very important to take the necessary measures to prevent sabotage and terrorist attacks that may occur in such places in order to ensure public and environmental safety. In this study, a machine learning based liquid control system that can be used in airports, railway stations and shopping malls as well as in places with high fire probability is proposed. The difference of the proposed system from traditional liquid scanner systems is that it can detect the hazardous liquid concentration in the solutions as well as the detection of pure flammable liquids. Linear Discriminant Analysis and Quadratic Discriminant Analysis are used as classifiers and the performances of these techniques are compared. The results show that Quadratic Discriminant Analysis offers higher accuracy and lower error rates compared to Linear Discriminant Analysis.

Salt water adversely affects human health and plant growth. In parallel with the increasing interest in non-contact determination of salt concentration in water, a novel approach is proposed in this study. In the proposed approach, S parameter measurements, which show the scattering properties of electromagnetic waves, are used. First, the relationship between salt concentration in water and permittivity values, a distinguishing feature for liquids, is shown. Then, based on the derived correlations from a set of S parameter measurements, it is shown that the salt concentration in water can be predicted. Finally, after exactly determining the relations of permittivity, salt concentration and S parameter, a system that allows non-contact determination of salt concentration is proposed. Since the proposed system makes its prediction using a classifier, decision tree algorithms are employed for this purpose. In order to evaluate the appropriateness and success of the algorithms, a set of classification experiments were held using various water samples with different levels of salt concentration. The results of the classification experiments show that the Hoeffding tree algorithm achieved the best results and is the most suitable decision tree algorithm for determining the salt concentration of liquids. For this reason, the proposed non-contact approach can be used to determine the salt concentration in water reliably and quickly if its hardware and software components can be embedded into a prototype system. HIGHLIGHTS Showing the relationship between salt concentration in water and permittivity values.; Predicting salt concentration.; Proposing a system that allows non-contact determination of salt concentration.;