Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning’s speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

ABSTRACT

Counter Propagation Neural Network (CPN) is a hybrid neural network because it makes use of the advantages of supervised and unsupervised training methodologies. CPN has a reputation for high accuracy and short training time. In this paper, a variant of CPN, namely preprocessed Counter Propagation Neural Network is proposed. We propose that if some preprocessing can be introduced to assign weights instead of random weight assignment during CPN training, it will result in good classification accuracy, very short training time and simple model complexity. The preprocessed CPN has promising applicability in a number of domains, among which textile defect classification is a prominent one. Textile sector is the most prospective export sector in Bangladesh. We demonstrate the utility and capability of our preprocessed CPN classifier in automated textile defect classification in the context of Bangladesh. We have found very good results.

Expert systems play an important role in medical diagnosis research. Researches are still being conducted for building expert systems capable of diagnosing different diseases. Diabetes mellitus is one of the diseases that have gained attention in the past years. Patients are usually unaware of having this disease and are finally diagnosed with diabetes after several years from onset. Since diabetes can be controlled, it is much desirable to harness it at the onset. Therefore, the prediction of onset of diseases like diabetes has been the point of interest for the researchers. Researchers are continuously trying to formulate an inference engine, a part of an expert system, in order to predict the disease at the beginning. In this paper, we present a Bayesian classification approach to identify the onset of diabetes mellitus in patients using a well-known data set as the sample. We have found an intriguing result with more than 87% accuracy.

Interrupting, altering, or stealing autism-related sensitive data by cyber attackers is a lucrative business which is increasing in prevalence on a daily basis. Enhancing the security and privacy of autism data while adhering to the symmetric encryption concept is a critical challenge in the field of information security. To identify autism perfectly and for its data protection, the security and privacy of these data are pivotal concerns when transmitting information over the Internet. Consequently, researchers utilize software or hardware disk encryption, data backup, Data Encryption Standard (DES), TripleDES, Advanced Encryption Standard (AES), Rivest Cipher 4 (RC4), and others. Moreover, several studies employ k-anonymity and query to address security concerns, but these necessitate a significant amount of time and computational resources. Here, we proposed the sanitization approach for autism data security and privacy. During this sanitization process, sensitive data are concealed, which avoids the leakage of sensitive information. An optimal key was generated based on our improved meta-heuristic algorithmic framework called Enhanced Combined PSO-GWO (Particle Swarm Optimization-Grey Wolf Optimization) framework. Finally, we compared our simulation results with traditional algorithms, and it achieved increased output effectively. Therefore, this finding shows that data security and privacy in autism can be improved by enhancing an optimal key used in the data sanitization process to prevent unauthorized access to and misuse of data.