68-73Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, has been a bottleneck to several clinical researchers
due to data modularization, subjective analysis, and shifts in the accurate prediction of the disorder amongst the sample
population. Subjective clinical research suffers from a lengthy procedure, which is a time-consuming process. In this paper,
Sailfish Optimization (SFO), a recently developed nature-inspired meta-heuristics optimization algorithm, is being utilized
to detect ASD. The hunting methodology of sailfish inspires SFO. Classical SFO has examined the search space in only one
direction that affects its converging ability. The Random Opposition Based Learning (ROBL) strategy enhances the
exploration capacity of SFO and successfully converges the predictive model to global optima. The proposed ROBL-based
SFO (ROBL-SFO) selects relevant features from autism spectrum disorder (child and adult) datasets. According to the
results obtained, the proposed model outperforms the convergence capability and reduces local-optimal stagnation compared
to conventional SFOs

Balakrishnan, K

Dhanalakshmi, R

Khaire, Utkarsh Mahadeo

NOPR

Indian Journal of Radio & Space Physics Vol.50, June 2021, pp. 68-73 Detecting Autism spectrum disorder with sailfish optimisation K Balakrishnana*, R Dhanalakshmia & Utkarsh Mahadeo Khaireb aDepartment of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli,  Tiruchirappalli 620 012, India bDepartment of Data Science and Intelligent Systems, Indian Institute of Information Technology Dharwad, Karnataka 580 009, India Received: 12 February 2021; Accepted: 5 March 2021 Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, has been a bottleneck to several clinical researchers due to data modularization, subjective analysis, and shifts in the accurate prediction of the disorder amongst the sample population. Subjective clinical research suffers from a lengthy procedure, which is a time-consuming process. In this paper, Sailfish Optimization (SFO), a recently developed nature-inspired meta-heuristics optimization algorithm, is being utilized to detect ASD. The hunting methodology of sailfish inspires SFO. Classical SFO has examined the search space in only one direction that affects its converging ability. The Random Opposition Based Learning (ROBL) strategy enhances the exploration capacity of SFO and successfully converges the predictive model to global optima. The proposed ROBL-based SFO (ROBL-SFO) selects relevant features from autism spectrum disorder (child and adult) datasets. According to the results obtained, the proposed model outperforms the convergence capability and reduces local-optimal stagnation compared to conventional SFOs. Keywords: Autism, Random opposition-based learning, Sailfish optimization 1 Introduction Autism is a neurodevelopment disorder with unique characteristics like social Interaction, improper behaviour, and communication with others. A study reveals that one child out of sixty-eight under the age of 8 and one adult out of 13 under sixty has autism in the United States of America1. Conventional clinical diagnosis involves a parent interview, a medical examination, a hearing test, observation, lead screening, speech and language evaluation, and early-stage sensory-motor assessment may reduce the chance of effect. Autism is a group of spectrum disorders with typical symptoms. Diagnosing autism is dissimilar in terms of autism in children and autism in adults2. Plenty of researchers provide better predicting algorithms in terms of accuracy with the ASD datasets and a real-world dataset3. The feature is an essential element in the machine learning classification problem. Selecting the best feature or relevant features will provide better accuracy in classification problems. Conventionally, most of the high dimensional datasets have more than 60000 features or attributes with fewer samples, not exceeding 100. The critical or relevant features can be identified using feature selection techniques viz., filter, wrapper, and embedded methods4. Specifically, the selection of features has an effect on obtaining the best outcomes with the precision of classification. The Meta-Heuristic (MH) algorithm is more prominent in many engineering-related areas. Factors like uncomplicated and flexible implementation, local optima avoidance, and the independence of the gradient details makes MH most celebrated technique in the realm of Feature Selection (FS). It can be classified into four different groups, namely the Evolutionary algorithm (Genetic Algorithm5 and Harmony Search6), Physics-based algorithm (Magnetic optimization algorithm7 and Gravitational Search Algorithm8), Swarm-based algorithm (Particle Swarm Optimization9 and Whale Optimization Algorithm10), and Human-based algorithm (Mine Blast Algorithm11). Out of these four classifications of meta-heuristic algorithms, Swarm optimization algorithms have the advantage of preserving the search space, rejecting any data when a new population has generated, and using less memory12,13. SFO14 is a meta-heuristic algorithm inspired by a hunting technique of group of sailfish. The attack alternation approach of sailfish has been the core inspiration of the SFO algorithm. The —————— *Corresponding author (E-mail: bala.k.btech@gmail.com)BALAKRISHNAN et al: LEANING SAILFISH OPTIMIZATION   69classical SFO suffers from stagnation at local optima and slower convergence speed because of the non-linear motion in prey search. Two groups of prey and predator populations have been used to represent the group hunting approach for SFO. The alteration attack breaks down the collective behaviour of the grouping policy. The prey movements are updated using the elite matrix, which selects the best position. In this article, the converging ability strengthened and increased SFO performance by introducing Random Opposition Based Learning (ROBL) into classical SFO. ROBL technique is used to improve exploration of the search space, motivated by the opposition between entities. The major drawback of the classical SFO is that it explores the search space only in one direction, affecting its optimal performance and finding difficulty in a convergence of the model in global optima. Therefore, the ROBL strategy is used to improve the efficiency of the optimization algorithm and to substantially support the population to get out of the local minima.   2 Materials and Methods This section provides mathematical modelling and the detail description of the classical SFO and Random Opposition-Based Learning.  2.1 Sailfish optimization The SFO is a population-based MH optimization method driven by an attack-alteration of a group of hunting sailfishes that hunts a school of sardines. The SFO can search for the prey in a multidimensional search space. The sailfishes generate the random population which were then treated as a candidate solution. This algorithm observes two populations: 1) sailfish population, 2) sardine population. The sardine positions, elite, and injured matrix were used to find the best population. The alteration attack method is used to update the best sailfish and sardine position in the matrix. The optimization approach of SFO is provided in the pseudocode-114.  2.2 Random opposition-based learning The initial population of random search agents is generated by MH algorithms based on prior knowledge. Due to the optimization algorithm's movement in only one direction of the search space, the algorithm may fall into local optima, and the result may not find optimal. The OBL technique effectively enhances the convergence capacity of MH algorithms, which overcomes the problem of optimality15. The search space can explore in both directions of the error surface. Consider o is random search agent range throughout [ub, lb] and is an opposite search agent, which is determined by Eq. 1  o lb ub o … (1)  where, lb and ub depict the random search agent's  lower and upper boundaries, the opposite search agents are generated in n dimension search spaces using the Eqs 2 & 3.  o o , o , … o  ... (2)  o o , o … … o  ... (3)   Pseudocode-1: Sailfish Optimization Initialize the random search agents for sailfish and sardine Initialize the parameters  Find the fitness and build an elite matrix and injured matrix  while (Check termination criteria) for each sailfish, calculate µi using Eq. (18) Update the position using Eq. (17) end for Find the attack power using Eq. (21) if attack power < 0.5 Calculate α and β using Eq.(22) and Eq.(23), respectively Update the selected sardine position using Eq. (20) else Update the all-sardine position using Eq.(20) end if Find the fitness of all sardine  if there is a better solution in the sardine population, Replace the sailfish with injured sailfish using Eq.(24) Remove the hunted sardine from the population Update the best sailfish and best sardine  end if end while Return Best sailfish  The opposite direction values of 𝐨 is generated using the Eq4  o lb ub o … (4)  The random OBL concept was used to improve the convergence ability, which enhanced the diversity of the population. The Eq 4 was modified with the random values in between [0, 1]. The ROBL is mathematically represented in the below Eq5  o lb ub ∗ rand … (5)  2.3 Dataset description The performance of the proposed algorithm was experimented with autism spectrum disorder INDIAN J RADIO SPACE PHYS, VOL 50, JUNE 2021   70 dataset for child and adult those were downloaded from internet. This ASD Diagnosis dataset contained 23 attributes which included one class attribute for predicting the disease and 22 features or attributes. Table 1 represents detailed description of the child's autism spectrum disorder dataset16. Tables (2&3) represent the number of samples available in child and adult dataset17, both ASD and Non ASD patients.  2.4 The proposed framework Figure 1 represents the proposed framework of ROBL-SFO. The proposed framework was carried out in three phases-Initialization phases, updating phase, and classification phase which is explained as follows.  2.4. 1 Initialization phase The initial population of random search agents were generated during the initialization phase.SFO contains two different populations: 1) sailfish and 2) sardine. The random search agent of sailfish and sardine initial population is represented in Eqs 6 & 7, respectively. RF_PopulationRF1,1 ⋯ RF1,d⋮ ⋱ ⋮RF50,1 ⋯ RF50,d 50*d  … (6)  R_PopulationR , ⋯ R ,⋮ ⋱ ⋮R , ⋯ R , ∗ … (7)  The search agent (n) fixed as 50, and dimensions (d) depends on the input dataset. The opposite populations of sailfish and sardine are generated using the Eq 5 and represented in Eqs 8 & 9 respectively  RF_PopulatıonRF , ⋯ RF ,⋮ ⋱ ⋮RF , ⋯ RF , ∗ … (8)  R_PopulatıonR , ⋯ R ,⋮ ⋱ ⋮R , ⋯ R , ∗ … (9)  Finally, by integrating both sailfish populations (RF_Population and RF_Populatıon), sardine populations (R_Population and R_Populatıon), the proposed model produces the position matrix, which is represented in Eqs  10 & 11 respectively RF_Position_matrixPRF , ⋯ RPF ,⋮ ⋱ ⋮PRF , ⋯ PRF , ∗ … (10)  R_Position_matrixPR , ⋯ RP ,⋮ ⋱ ⋮PR , ⋯ PR , ∗   … (11) To determine each sailfish's fitness value and sardine is computed in Eq 11 based on Eqs 12 & 13, respectively.  RFf RF , ⋯ RF ,⋮ ⋱ ⋮f RF , ⋯ RF , ∗FRF⋮FRF … (12)  Table 1 — Dataset description16  Attribute Values  A1 to A10 Yes, indicates value 1; No indicatesvalue 0  Age Value ranges from 1 to 80  Sex Value 1 indicates male; Value 0indicates female  Ethnicity Aboriginal, White, Black, Hispania,Latino middle Eastern, South Asia,etc.  Jaundice Yes, indicates value 1; No indicatesvalue 0  Family ASD Yes, indicates value 1; No indicatesvalue 0  Residence Different states and countries inAsia, South Asia, etc.  Used App Before Yes, indicates value 1; No indicatesvalue 0  Score The value ranges from 0 to 10  Screening Type 1-3,4-11,12-16, 17 and above  Language English, Russian, Spanish, French  User Self, Parent, Relative, Others  ASD Class Yes indicates value 1; No indicatesvalue 0 Table 2 — Autism spectrum disorder -Child16  Description Count  Total number of records 292  Total number of positive autism cases 141  Total number of negative autism cases 151   Table 3 — Autism spectrum disorder -Adult17  Description Count  Total number of records 704  Total number of positive autism cases 188  Total number of negative autism cases 515 BALAKRISHNAN et al: LEANING SAILFISH OPTIMIZATION 71R_f R , ⋯ R ,⋮ ⋱ ⋮f R , ⋯ R , ∗FR⋮FR… (13)  where, f measures the fitness function, RF_fitness, and R_fitness saves each sailfish's fitness value and sardine and return the fitness or objective function value. The proposed model maps the continuous values of the random search agents to binary by using the following equation  x ,1, ,0, otherwise… (14) 2.4.2 Updating phase The best position of sailfish in each iteration was saved as an elite matrix. Initially, the predator searched for prey and updated the elite matrix based on the best predator. For each iteration, the injured sardine location was also saved, and this sardine would be chosen as the best target for sailfish hunting. The elite matrix and injured position were represented in Eqs 15 & 16. Such positions would have a significant impact on SFO performance and avoid the wasting of time to re-discover previously discarded solutions. RF_elite Y , ⋯ Y ,⋮ ⋱ ⋮Y , ⋯ Y , ∗ … (15) R_injured Z , ⋯ Z ,⋮ ⋱ ⋮Z , ⋯ Z , ∗ … (16) where, Y indicates the top predator. Here, both the predators and prey were considered a search agent because sometimes prey may also act as potential predators.  Fig 1 — The proposed framework of ROBL-SFO INDIAN J RADIO SPACE PHYS, VOL 50, JUNE 2021 72 In general, the sailfish attack the prey when fewer contenders are available to attack in a particular region. The SFO algorithm illustrates an attack-alternating technique for sailfish when hunting in groups represented in Eq 17. RF _ RF γrand 0,1RF R2RF  … (17) where, RF  is the current position. RF is the best position of the sailfish R  is the best position of injured sailfish.RF _  is the newly updated position of sailfish. γ  is the coefficient at an ith location which is generated below Eq 18 γ 2 ∗ rand 0,1 ∗ PD PD … (18) where rand is the random value lies between [0,1]. The density of the prey in each iteration is calculated using the parameter PD. it is an important parameter to update the prey position of sailfish, which is calculated using the below formula PD 1 … (19) where, N  represents a number of sailfish and N represents a number of sardines. The elite matrix and injured matrix position were updated on every iteration. 2.4.3 Hunting and catching prey The best sailfish position was updated to a new best solution while hunting the sail and sardine fish at each iteration using Eqs 20 & 21, respectively. R _ rand 0,1 RF R pow… (20) pow C 1 2 ∗ Iter ∗ ϵ  … (21) where, R _  is the newly updated position, RF  is the current best position, rand is an arbitrary number between 0 and 1, pow is an attack power of sailfish. To update the attack power, the parameters α and β are calculated using Eqs 22 & 23, respectively. 𝛼 𝑁𝑜. 𝑜𝑓. 𝑠𝑎𝑟𝑑𝑖𝑛𝑒 ∗ 𝑝𝑜𝑤 … (22) β v ∗ Pow … (23) Finally, the hunting of sailfish, a sardine, which is expressed as  𝑈 𝑈  … (24) where,  𝑈  ,𝑈  represents the updated sailfish and sardine position, respectively. 2.4.4 Classification phase In this step, the proposed model selects the top 7 features from the vector of selected features based on the feature occurrence using the Support Vector Machine (SVM). The autism dataset, both child and adult, is divided into a training part and testing part in addition to using the 10-fold cross-validation (CV) process. 3 Results and Discussion The results obtained from ROBL-SFO were compared with the classical SFO and OBL-SFO. Figure 2 indicates the error rate of the prediction model throughout the epochs. The reduction of the Fig. 2 — Comparison of converging ability of Classical SFO, OBL-SFO, and ROBL-SFO14 BALAKRISHNAN et al: LEANING SAILFISH OPTIMIZATION 73error rate as the model progresses in each iteration demonstrated the convergence capacity of the proposed model towards the global minima. Compared to SFO and OBL-SFO, the proposed model converged faster towards the global minima for both the input datasets. The proposed model converged substantially towards the global optima after a few iterations that proved its efficiency in exploring and finding a better solution with different classifiers. The performance analysis of the predictive models for the selected feature subset is summarized in Table 4 to validate the selected feature subsets. The performance of the algorithm for the classification is measured using the Eq 25 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 … (25) Where, TP, TN, FP, and FN represent the true positive, the true negative, the false positive and the false negative, respectively. Table 4 shows the ROBL-SFO result against conventional algorithms. The accuracy is more for the child than for the adult. While comparing the Classical SFO with ROBL, there was an increase in accuracy by 3.07%. The proposed ROBL-SFO worked efficiently with SVM since yielded higher accuracy for both the datasets. From the results obtained, the identified significant contributions of the research are  (a) The incorporation of the ROBL in thesearching strategy of SFO avoids the stagnation of the predictive model at local minima. (b) In addition to the Random opposition basedSFO, the SVM classifier is used to validate selected features based on the classification accuracy.   4 Conclusion The proposed model outperforms classical SFO in the convergence rate and selecting the optimal subset of significant features. The 10-fold CV of the learning process guarantees that the model has not over fitted. From the results, it has been interpreted that the proposed model can effectively balance the trade-off between bias and variance. The proposed ROBL-SFO strategy enhances its exploration and exploitation capability that precludes local minima stagnation of the model. Acknowledgement This research was funded by the Department of Science and Technology, Government of India under the Interdisciplinary Cyber-Physical Systems (ICPS) scheme (Grant no. T-54). 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Table 4 — Classification accuracy of Classical SFO, OBL-SFO and ROBL-SFO14 Optimization method Child Adult Classical -SFO 94.44 87.62 OBL-SFO 96.83 91.42 ROBL-SFO 97.30 94.20 

Detecting Autism spectrum disorder with sailfish optimisation

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Detecting Autism spectrum disorder with sailfish optimisation

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