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This study applies artificial neural networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R² value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices but vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning.

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Context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. The most common way to implement this approach is via triphone modeling. Nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. One approach to solve this problem is via parameter tying. In this paper, clustering has been carried out on HMM state parameters and the states allocated to any cluster are tied to decrease the overall number of system parameters and achieve robust training. Two types of groupings, one based on the final trained model set parameters and their inter-model distances and the other based on the training data and a decision tree, have been carried out. In the implementation of the later, a decision tree based on the acoustic properties of the Persian (Farsi) language and the phonetic similarities and differences has been designed. The results obtained have shown the usefulness of both the approaches. However, the second approach has the advantage of making the estimation of unseen model parameters possible.

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A parallel hybrid system of HMM and GMM modeling techniques was implemented and used in a telephony speaker verification and identification system. Spectral subtraction and Weighted Projection Measure were used to render this system more robust against additional noise. Cepstral Mean Subtraction method was also applied for the compensation of convolution noise due to transmission channel degradation and differences in the frequency response of telephone handsets. For a population of 100 speakers of FARSDIGITS1 database with a SNR of 8.8 dB, a speaker identification performance of 95.51% and a speaker verification error rate of 0.37% were obtained. Several score normalization methods in utterance and frame level and weighting of model scores were also implemented, and then compared and evaluated. It was shown that these methods improve discrimination between speakers and yield a reduction of speaker verification and identification error rates.

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The geometric distribution of states duration is one of the main performance limiting assumptions of hidden Markov modeling of speech signals. Stochastic segment models, generally, and segmental HMM, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. In this paper, a new duration modeling approach is presented. The main idea of the model is to consider the effect of adjacent segments on the probability density function estimation and evaluation of each acoustic segment. This idea not only makes the model robust against segmentation errors, but also it models gradual change from one segment to the next one with a minimum set of parameters. The proposed idea is analytically formulated and tested on a TIMIT based context independent phoneme classification system. During the test procedure, the phoneme classification of different phoneme classes was performed by applying various proposed recognition algorithms. The system was optimized and the results have been compared with a continuous density hidden Markov model (CDHMM) with similar computational complexity. The results show slight improvement in phoneme recognition rate in comparison with standard continuous density hidden Markov model. This indicates improved compatibility of the proposed model with the speech nature.

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Forensic phonetics is a subfield of forensic linguistics in which acoustic information and phonetic features of phones are investigated for completing the forensic cases where one of the existing evidences is related to the guilty. One of the most important tasks of a forensic phonetician is forensic speaker recognition. For doing this, the phonetician is asked to estimate the degree of similarity between the given records of the guilty’s and the suspect’s speech, and determine whether these two sound evidences match to each other or not. The objective of this study, which was conducted on the sound data from 10 Persian native speakers of both sexes, was to investigate the possibility of using Logarithmic spectrographs of vowels as a key for forensic speaker recognition tasks. The results showed that using these logarithmic spectrographs may be a useful means with perfect reliability in the tasks related to forensic speaker recognition.  

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In this paper, we used a shape matching algorithm to recognize Farsi digits. For each sampled point on the contour of a shape, we obtain a descriptor showing the distribution of the other points of the contour, with respect to this point. Based on these descriptors, we find the corresponding points of the two contours and take the sum of their distances as a dissimilarity measure between two shapes. Then we define a geometric transformation that maps the sampled points of the one shape to the corresponding points of the other shape. The bending energy of this transform is taken as the second dissimilarity measure between two shapes. We optimized the parameters of the matching algorithm for the recognition of Farsi digits and used the method of minimum distance from the class prototypes for the recognition. In a test on a set of 1288 digits, we obtained a recognition rate of 89.9%. This result was obtained without any post processing

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In this paper, an interactive model for individual normal behaviour of drivers is presented in which the mutual effect of vehicles has been incorporated. Temporal features obtained from vehicles tracking and their motion history is utilized for generating a model of normal behaviour. Because of non-stationarity of behaviour, Hidden Markov Model has been used for interactive model. This model has three main parts. The first part is the history of antecedent trajectory which for this purpose has proposed a Centers Transition Matrix (CTM) that is some type of spatio-temporal information-data bank from motions seen in the old frames. The second part is based on the linguistic features or motion recognition of vehicles, these motions contain forward, turn right and left, lane changing to right and left motion. The third part is constituted from low level features which contain Velocity and distance to neighbor object. Also CTM is efficient in search at similar blob in image sequences and it can determine the radius and region of search. This top-down feedback caused an increment of performance of RLS tracker and object searching. In the presented system, we obtained a 81.2% membership rate to normal model. Also types of motion are recognized using HMM with a recognition rate of up to 82.7%. Prediction error is reduced on many vehicles trajectory by at least 80% using a feedback system.

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In this paper, to test the theory of Honneth, historical methods and analysis technique were used. The analysis of socio-economic forces during the 40s and 50 in the Islamic Revolution of Iran showed that Shah failed in the modernization program involved: secularism, nationalism and capitalism. Along with feeling humiliated, mis-recognition, denial and injustice by some social forces, established a major change in the approach to policy seminary. Clergymen showed a tendency to transformed orientation, and different social classes were mobilized against the regime. They felt humiliatly because of the modernization project. Because of the regime’s emphasis on nationalism, political parties and different urban middle classes including academics, writers and intellectuals felt mis- recocgnition. All of these resons led to increasing the motivation of clergymen to join the revelution. But also the economic forces played a effective role to the victory of the Islamic Revolut­ion in Iran. Traditional merchants, immigrants and poor people were the losers of the modernization project. Capitalism was unfaid and detrimental to them.They also joined the coalition. Based on these results, the recognition is one of the main causes of masses, mobalization against the regime.    

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This paper aims to study some Muslim economists’ views towards pre- suppositions of demand theory in economics, benefiting from the method of analyzing rational behaviors, as well as considering the epistemology of utility. In economics in general and Islamic economics in particular, there not only exists a sharp difference in utility - as a basis for demand theory, but also, there are some vital differences in recognizing utility. Considering the differences here influences theorization of demand in Islamic economics.

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Speech emotion can add more information to speech in comparison to available textual information. However, it will also lead to some problems in speech recognition process. In a previous study, we depicted the substantial changes of speech parameters caused by speech emotion. Therefore, in order to improve emotional speech recognition rate, in a first step, the effects of emotion on speech parameters should be evaluated and in the next steps, emotional speech recognition accuracy be improved through application of suitable parameters. The changes in speech parameters, i.e. formant frequencies and pitch frequency, due to anger and grief were evaluated for Farsi language in our former research. In this research, using those results, we try to improve emotional speech recognition accuracy using baseline models. We show that adding parameters such as formant and pitch frequencies to the speech feature vector can improve recognition accuracy. The amount of improvement depends on parameter type, number of mixture components and the emotional condition. Proper identification of emotional condition can also help in improving speech recognition accuracy. To recognize emotional condition of speech, formant and pitch frequencies were used successfully in two different approaches, namley decision tree and GMM.

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In this paper, in order to detect the number of transmitting antenna in MIMO communication systems, it is proposed that the techniques such as AIC & MDL, which have been primarily designed so as to detect the number of Gaussian sources, are applied. Then a hypothesis testing based method for recognizing the type of modulation in MIMO communication systems with block orthogonal codes is suggested; in which in order to reduce the complexity of the traditional methods, simpler likelihood functions for testing hypotheses are applied. Furthermore, because in all modulation scheme detection methods, a proper estimation of channel gain (channel matrix) is required; in this paper, a new and efficient method based on SAGE iterative algorithm for estimation of channel matrix in MIMO communication system with space-time block codes is proposed. At the end of this paper, the performance and effectiveness of all proposed modules are separately and jointly analyzed by numerical simulations.

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Reading skills is one of the most important language skills. The Success in learning a second language depends on this skill. But it is noted that the final semester students of the Arabic language and literature department who are ready to be graduated and even many graduates of this field are not able to read the Arabic texts correctly and fluently. Therefore, the authors of the present study intend to use the descriptive-analytical method and survey method to evaluate the reading skill of Arabic of undergraduate students to identify their weaknesses on reading and offer some suggestions for developing them. It should be noted that different studies have been conducted on reading skill in Iran and Arabic countries, but none of these studies investigate the weaknesses of Arabic language and literature students of Iran state universities in reading skill. So the present study is completely different from previous studies. The main question of the study is: how weak are Arabic language and literature students in reading skill and what are the reasons of these weaknesses? The statistical sample of the present study has been formed by124 undergraduate students in the Arabic language and literature department in 12 Iranian state universities, 41 of them are male and 83 are female. The tool used in the study is a researcher-made test, which has been designed for the first time for reading skill in Arabic language and literature and the existing models in the Arabic countries have been used to design the test. The results reflected the weakness of students and their lack of mastery in this skill. Although the students who are ready to be graduated, faces weakness in all levels of reading. There is a kind of consensus amongst the experts of Arabic language on this issue. The most important reason of this problem is that there is no special lesson for reading skill in the syllabus.
 

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This paper introduces a novel approach to improve performance of speech recognition systems using a combination of features obtained from speech reconstructed phase space (RPS) and frequency domain analysis. By choosing an appropriate value for the dimension, reconstructed phase space is assured to be topologically equivalent to the dynamics of the speech production system, and could therefore include information that may be absent in analysis approaches based on linear methods. Also, complicated systems such as speech production system can present cyclic and oscillatory patterns and Poincare sections could be used as an effective tool in analysis of such trajectories. In this research, a statistical modeling approach based on Gaussian Mixture models (GMM) was applied to the Poincare sections of speech RPS. The final feature set is obtained from a feature selection stage omong parameters of GMM model and the usual Mel Frequency Cepstral coefficients (MFCC). An HMM-based speech recognition system and the TIMIT speech database are used to evaluate performance of the proposed feature extraction system for isolated and continuous speech recognition. Experiments represent about 5.7% absolute isolated phoneme recognition accuracy improvement in isolated phoneme recognition performance. The new approach is shown to be a viable and effective alternative to traditional feature extraction methods, particularly for signals such as speech with strong nonlinear characteristics.

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The nature of metaphor, metaphoric understanding, and its functions have been recognized as three main issues in research and theoretical formulations on metaphor and metaphor processing. In general, metaphor is defined as understanding and experiencing one thing based on another. Metaphor can also be considered as an expression that has two conceptual domains in which one of the domains is experienced and understood according to the other. These two conceptual domains are known as target domain and source domain. In this study, we examine the relationship between conceptual metaphor and formation of a schema in short texts regarding target and source domains.
    The main tools of the study were the texts written in fluent Persian and divided into two categories of metaphorical texts and their equivalent non-metaphorical versions. Both texts have had a shared schema. Furthermore, the number of words were equal in both texts. For this purpose, 3 metaphorical and 3 equivalent non-metaphorical short texts were designed in Psychopy software in 2 visual and auditory versions and 47 people were exposed to the short-term recognition after reading/listening to it. There were eight texts including 3 metaphorical and 3 non-metaphorical ones plus 2 texts that were used as filler texts which were excluded from the final analyses. The texts were organized in such a manner that the metaphorical text played before its corresponding non-metaphorical text with an interval. At the next round, the non-metaphorical text was played before its metaphorical version.
    The sample was selected through convenience sampling which included 80 twenty to twenty-five-year-old students of Foreign Languages School and Management School of Allameh Tabatabai University in Tehran. Since variations in memory capacity can affect the test results, they were given a Persian word recognition test to ensure relative consistency among all participants' memory capacity.
     The test processes in the visual and auditory tests were the same except in the method of presenting the texts on the screen or playing through the headphone.
    In this study, descriptive and inferential statistical methods were used for analyzing the data and providing tentative answers to the research questions. All the analyses were implemented SPSS V.23 software. To analyse the data in each of the visual and auditory tasks separately, Friedman non-parameter test was used. For comparing the data of the visual and auditory tasks, Mann-Whitney test was used.
    Results indicated that in metaphorical texts, there are traces of the non-metaphorical text`s main schema. This finding brings us closer to the assumption that it is source domain`s schema that projects on target domain in metaphors and makes it more understandable.
 
 

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Routine verbal communication almost never occurs in quiet. Speech perception disorder in noise is one of the most common complaints of people of all ages. In our living environment, there are different types of background noise that have different masking effects. In general, verbal noises have more signal target masking. Babble noise as an interfering factor can lead to speech perception disorders. Speech phonemes are alienated into consonants and vowels across languages. These phonemes are different in terms of production and perceptual mechanism. Persian has 6 vowels /i/, /e/, /a/, /â/, /o/, and /u/. Vowels are the nucleus of the syllables and words, vowel errors can lead to disorders in speech perception process. Now the question arises as to whether the ability of vowel recognition in the presence of babble noise is influenced by age, signal to noise ratio, gender, and educational level? Also, if the above factors affect the recognition of the Persian vowel, which vowels are more vulnerable to these effects?
Therefore, due to the absence of similar studies on the effect of the aforementioned factors on the recognition of Persian vowels, the present study examined the effect of age, signal to noise ratio, sex, and educational level on the recognition of Persian vowel in the fourth and fifth decades of life. This observational and cross sectional study was performed on 60 adults with normal hearing in the age range of 30-49 years with mean and standard deviation (SD) of 38.88±6.23 years old (thirty people aged 30 to 39 years old with average age and standard deviation (SD) of 33.40±2.35 years and thirty aged 40 to 49 years with average age and standard deviation (SD) of 44.73±2.33 years) from available samples. After the auditory and speech evaluation, the recognition of Persian vowels were examined in consonant-vowel-consonant syllable in the presence of babble noise in signal-to-noise ratios of 0, -5 and -10, along with  the randomized presentation of stimuli to the right ear. The purpose of the random presentation of syllables in the present study was to avoid memorizing words.Comparison the recognition score of six Persian vowels showed significant differences in three signal-to-noise ratios (p = 0.001). Also, comparison the vowels recognition scores were significant in two age ranges of 30-39 and 40-49 years old in three signal-to-noise ratios (p = 0.001). However, not found significant differences between the sexes (P= 0.991) and different educational levels (P= 0.282). Also, in three signal to noise ratios of 0, -5 and -10, the recognition scores of the front vowels were better than the back vowels. In signal-to-noise ratios of 0 and -5, the highest mean of recognition score was associated with / a / vowel and in the signal-to-noise ratio of -10 with / i / was present. Also, there was the lowest mean of recognition score in signal-to-noise ratios 0, -5 and -10 with / u / vowel. The present study showed that the average recognition of Persian vowels is affected by age, signal-to-noise ratio, and type of vowel. As the age increases and the signal-to-noise ratio decreases, the average recognition score of the vowels decreases significantly in the presence of babble noise
 
 

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Despite their substantial role in natural discourse, idioms often intimidate EFL students. Therefore, finding effective ways of mitigating students’ frustration has been a fundamental consideration in language teaching. Mnemonics, as associative memory tools, are largely acknowledged to be instrumental in reducing the cognitive load involved in language learning, particularly in learning lexical items. This study was conducted to explore the effects of linguistic, verbal, and visual mnemonics on empowering language learners in the recall and recognition of English idioms. Two-hundred seventy intermediate-level EFL learners preparing themselves for IELTS took part in this study. The participants were in nine groups of 30 members each. They were divided into three main groups, each of which was taught idioms using one of the above-mentioned mnemonics. The MANOVA procedure was used to analyze the collected data. The results revealed statistically significant differences among these instructional strategies in case of recall, with linguistic mnemonics being the most influential. The findings of the study can have theoretical implications for researchers in that they can shed light on some of the dark corners of the field and spark interest for further research. The findings can also have some pedagogical implications for teaching programs, curriculum developers, educational policymakers, teachers, and language learners. Developing a clearer understanding of how these mnemonics influence idiom learning can help the stakeholders make more informed decisions about how to treat idioms.
 

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Multi-channels Electroencephaloram (EEG) needs a long preparation time for electrode installation. Furthermore, using a large number of EEG channels may contain redundant and noisy signals which may deteriorate the performance of the system. Therefore, channels reduction is a necessary step to save preparation time, enhance the user convenience and retain high performance for an EEG-based system. In this study, we present a simple and practical EEG-based emotion recognition system by optimizing the channels number based on two different Common Spatial Pattern (CSP) channel reduction methods. We applied feature extraction based on the Fast Fourier Transform (FFT) algorithm and classification method based on the Support Vector Machine (SVM) and K-nearest neighbor (KNN) which make our proposed system an efficient and easy-to-setup emotion recognition system. According to experimental results, the proposed system using small number of channels not only does not increase the error of the system, but also improves the performance of the system compared to the use of total number of channels.    

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A borderline intelligence student is the one with basic capability for literacy but of a lower rate and depth in learning compared to their peers. Such students have many difficulties in learning the language, reading and writing. The main purpose of this study is to provide a new solution based on linguistic and psycholinguistic theories to increase the development of language recognition in these children. The technique of total image words was used in an innovative method to manipulate all the senses of the child in his learning. The data of this research have been collected in a field and case study. The case is a 13-year-old borderline intelligence girl living in Mashhad. The suggested training program in this research consists of four stages. The first step is to work on reading by using flashcards of total image words and using the child’s visual and auditory senses. The second step is to emphasize the meaning and semantic chain of the words. In the third stage, we try to strengthen the child’s comprehension with short stories, and in the fourth stage, which is the practical stage, the child acquires the ability to make meaningful sentences and short stories. The test of language development (TOLD 3) was used for pre- and post-tests. The results show that the total image word technique has a positive effect on the language cognitive development of the borderline intelligence student and except in sentence imitation, all other subtests show an improvement of 18 to 66 per cent.

1. Introduction
The development is a continuous, multidimensional, and flexible process. One of the most important dimensions in a child’s development is the growth of intelligence and linguistic cognition. Learning is a very complex process that everyone is involved in throughout their lives. But the problem arises when not all children can adapt to traditional learning methods that are less flexible. That is why there is a big gap between their potential and their performance.
A Child of borderline intelligence (slow learner) is a student who has the ability to learn the necessary educational skills, but the amount and depth of their learning is less than the average of their peers. Slow learners have many problems learning language and reading and writing. The purpose of this study is to provide a new solution based on theories of linguistics and psycholinguistics to promote the development of language cognition in such children. For this purpose, we tried to engage all the senses of the child in their learning by using the Total Image Words Technique in an innovative way.
Research Questions:
1) Can teaching by means of the Total Image Words Technique influence the development of language concepts in slow-learning children?
2) Does teaching by means of the Total Image Words Technique affect the comprehension of slow-learning children?
3) Does teaching by means of the Total Image Words Technique have an effect on promoting the reading and writing skills of slow-learning children?

2. Literature Review
One of the important theories used in this research is Gestalt Theory, which is a kind of holistic speculation. According to this theory, in confrontation with various phenomena, the sole nature of the components does not play a role in determining the identity of the collections. In fact, the overall structure and composition of each phenomenon is more than just the sum of its components. This means that each particular part can have its own meaning, but it is the overall configuration that can give it its full meaning.
In the Total Image Words Technique, as its name suggests, this theory is well used to teach slow learners, because the child first sees and learns the whole word in the form of text and image and then goes from whole to parts. Since in the Total Image Words Technique, words are placed in the form of categories and frameworks that are related to each other and are in a semantic chain, this technique has benefitted from Fillmore's Frame Semantics and Semantic Chain.
Some researchers consider slow learners to be a group between normal and exceptional children. A slow-learning child “is one who has the ability to learn necessary academic skills but at a rate and depth below average of the same age peers" (Suranjana et al., 2015, p. 130). The performance of these students in school has been considered by various researchers. According to Ghafourian (2017, p. 57), the slow student “has the ability to progress in education in a regular school, but performs below the average of their school level.” These children score lower on IQ and academic achievement tests than their peers, but their scores are not low enough to cause them to need special education.”
It is at these times that teachers’ creativity and experience can lead to more efficient education. Such teachers can provide more learning opportunities to students with different levels of learning through a variety of teaching methods and multipurpose practices and support the learners to improve their learning capacities.
These students are only slightly different from normal students in terms of mental development, so they can study with their peers. Ready (2006) reports that these students have an IQ between 76 and 89 and make up about 18% of the total student population. The most important features of these children are the following:
1. Low accuracy and focus. The solution to this problem is to use appropriate games and exercises and get help from experts.
2. Inattention in doing homework. To solve this problem, the amount of their homework should be reduced and its quality should be given more importance.
3. Lack of sufficient self-confidence. Self-confidence can be increased by giving proper motivation and encouragement.
4. Having poor public relations.
5. Being slow in doing and solving complex and multifaceted problems.

3. Methodology
  In line with the purpose of the study, the effect of the Total Image Words Technique on a thirteen-year-old girl named Negar in Mashhad, a city in Iran, was investigated. This study was a case study and quasi-experimental design with a pre-test and post-test that was conducted over a period of four months. The Child was initially exposed to Wechsler’s Intelligence Test (EISC) for children and Wineland’s Social Maturity Scale. The results confirmed that the child was slow-learning (having borderline intelligence). The data collection tools used in this research are educational cards that include an image of an object or a phenomenon along with the written form of a word to which an object or phenomenon refers. Language development test (TOLD 3) was also used for pre-test and post-test.

4. Results
A TOLD3 pre-test was administered initially and a post-test of the same type was given after the training period. Table 1 presents the results of the paired-sample T-test of the two sets of scores.
Table 1.
Paired-Sample T-test of standard scores & subtests of TOLD in pre-& Post-tests

two-tailed P value	df	t	Paired differences
			95% confidence interval of this difference		Std. error of Difference	Mean
			upper	lower
0.0074	5	4.3386	-1.09	-4.25	0.615	-2.67	Pretest -Posttest

Considering the obtained P-value in the table, it can be said with 95% confidence that the difference in the learner's performance before and after the application of the Total Image Words has been significantly different.
 In Figure 1, the average language skills in the pre-test and post-test are compared.

Figure1.
Comparison of average language skills in subtests of TOLD in pre-&post-tests



In Figure 2, the standard scores of the sub-tests in the pre-test and post-test are compared with each other and shown as a bar graph.

Figure 2.
Comparison of standard scores in subtests of TOLD in pre-&post-tests



5. Discussion
In the following, we present the results of this research according to the skills and seek to analyze the research questions and hypothesis.
 Spoken Language subtest: This score represents the sum of the standard scores of six subtests that measure semantic and syntactic aspects. For this reason, compared to the other six skills, it gives the best and most comprehensive picture of a person's overall language ability. All language-related features and systems are included in this subtest. In the case of Negar, the test results show an improvement of about 16% in this score, which means that she has been able to improve in all sub-tests during this period. The result of this subtest confirms our research hypothesis about the effect of the Total Image Words Technique on the development of language cognition in slow learners.
Semantic skill: Vocabulary is a special semantic ability that is evaluated with the semantic score. Children who are successful in this endeavour know a great deal about words. In this research, because in the second stage, the trainer worked on the characteristics of the meanings and details of the words, the child paid more attention to the different meanings of the word and her vocabulary increased. We see that Negar’s score has grown by about 15%. This means that she was able to better understand the words and their meaning, and this could be a positive answer to our research questions because the first and second research questions asked whether this technique expands the concepts of language and whether late learners’ comprehension is effective or not. The results obtained from this subtest also confirm the objectives of the research.
Syntactic skill: Negar has had a growth of about 13% in this score. Although she was not able to make more complex and long sentences due to her late learning, overall, these results are a positive answer to our third research question, which asked about the effect of the Total Image Words Technique on increasing reading and writing skills.
Listening skill: The performance of Negar in this score is also good and has reached 123 from a former score of 108, which indicates a growth of about 14%.
Organizational skill: Negar has been able to perform well in this skill and have an 11% growth in the post-test.
Speaking skill: Negar's speaking score in this test has increased from 95 to 113, which also shows a significant growth of 19%.

6. Conclusion
Finally, after comparing the obtained results and analyzing them, we come to the conclusion that if children’s different senses are used in teaching slow learners, their performance will be better. The results show that the Total Image Word Technique is effective on the development of language cognition of the slow learner and, except for sentence imitation in all other language subtests, the child shows growth between 18 and 66 per cent. This method, in particular, has increased her vocabulary and comprehension to the point where the child can make meaningful short stories.
 

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