Enhancing Feature Selection Accuracy using Butterfly and Lion Optimization Algorithm with Specific Reference to Psychiatric Disorder Detection & Diagnosis

As the complexity of medical computing increases the use of intelligent methods based on methods of soft computing also increases. During current decade this intelligent computing involves various meta-heuristic algorithms for Optimization. Many new meta-heuristic algorithms are proposed in last few years. The dimension of this data has also wide. Feature selection processes play an important role in these types of wide data. In intelligent computation feature selection is important phase after the pre-processing phase. The success of any model depends on how better optimization algorithms is used. Sometime single optimization algorithms are not enough in order to produce better result. In this paper meta-heuristic algorithm like butterfly optimization algorithm and enhanced lion optimization algorithm are used to show better accuracy in feature selection. The study focuses on nature based integrated meta-heuristic algorithm like Butterfly Optimization and lion-based optimization. Also, in this paper various other Optimization algorithms are analyzed. The study shows how integrated methods are useful to enhance the accuracy of any computing model to solve Complex problems. Here experimental result has shown by proposing and hybrid model for two major psychiatric disorders one is known as autism spectrum and second one is Parkinson's disease

Improving Accuracy of Integrated Neuro-Fuzzy Classifier with FCM based Clustering for Diagnosis of Psychiatric Disorder

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Autism spectrum disorder (ASD) is a neurodevelopment disorder. Clinical decision-making process is complex. Due to complex nature of disease sign and its symptoms clinical decision making may lead to misclassification. To deal with such complex medical problems methods or approaches of soft computing play an important role. This paper will focus on presenting an integrated Neuro-fuzzy model. This integrated model has the learning strength of neural network and knowledge representation ability of fuzzy logic. Modified Adaptive Neuro –Fuzzy inference system (M-ANFIS) is used here for classification and predication. Here Fuzzy C-mean (FCM) Clustering is used first to make classes of data before presenting in to ANFIS. This FCM based class will reduce the classifier computational overhead. Precision error and recall, F-measure and accuracy matrices are used to compare the experimental results with other classic methods

A rare case of solitary exostosis of capitate

Exostosis arising from carpal bones is rare and only a few cases have been reported till date. A 29 years old female came to the outpatient department with a localized swelling present on the dorsum of her right wrist since the past three years. On examining the patient clinically, a well-defined protuberance was observed over the dorsal aspect of the right wrist. CT report showed bony outgrowth over the dorsum of the capitate extending beyond the carpometacarpal joint. In surgical intervention, the mass was removed from the base, which grossly had an appearance of chondral origin. The biopsy report confirmed the diagnosis of exostosis (osteochondroma). Hence, excising the exostosis surgically led to achievement of adequate motion of the patient's wrist along with the additional cosmetic correction benefit

A COMPARATIVE STUDY OF MULTIDIMENSIONAL TRAIT ANXIETY BETWEEN INTERVARSITY AND NATIONAL LEVEL HANDBALL PLAYERS OF MADHYA PRADESH

ABSTRACT An investigation was conducted with a purpose of compare the level of multidimensional trait anxiety between university and national level handball players of Madhya Pradesh. For the purpose of this investigation 40 male subjects (20 university and 20 national level players) were recruited as subjects of the study

Optimization of Work Zone Segments on Urban Roads Using Cellular Automata Model in Mixed Traffic

Increased delays and reduced speeds in work zones leads to congestion. This can be improved by optimizing the work zone length. The focus of this study is to model work zones using cellular automata model and to find the effects of work zones on traffic flow. The methodology adopted in the study involved creating work-zone on the road by blocking some of the cells and then determining traffic characteristics such as delay and queue lengths for model validation. For this the lateral movement rules of the existing Cellular Automata model were modified in order to replicate the traffic movement near work zones. This model is calibrated and validated using data from work zone observed near a metro rail station in Delhi. From the analysis it was evident that the queue length increased with increase in the length of work zone. Several relationships were tried between delay and work zone length. Among them the rational form was found suitable

Comparative efficacy of intra-articular hyaluronic acid and corticosteroid injections in the management of knee osteoarthritis

Background: Osteoarthritis management includes a myriad of treatment modalities. This study compared the effects of corticosteroid and Hylan G-F 20 injections on knee osteoarthritis outcomes. Methods: Patients were randomized to receive either corticosteroid or Hylan G-F 20 injections. Outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index, knee society rating system scores, and visual analog scale scores, collected at baseline, 3 months, and 6 months. Results: Baseline demographic and clinical parameters were comparable between both groups. The corticosteroid group demonstrated a significant decrease in the WOMAC score over time (p<0.001). Hylan G-F 20 group showed significant improvements in both the WOMAC scores and Visual Analog Scale scores over time (p<0.01). Gender-based sub-analysis suggested both treatments were effective in men, while in women, significant benefits were seen only with Hylan G-F 20. Conclusions: Both corticosteroid and Hylan G-F 20 demonstrated efficacy in managing knee osteoarthritis, albeit in different domains. The results suggest the need for individualized treatment plans and further research into potential gender-based variations in treatment response

Modelling of seepage behaviour using available gaps at signalized intersection

Seepage behaviour is identified by the movement of two-wheelers from back of the queue in a signalized intersection to the front through small gaps between other vehicles. In mixed traffic, this is a common phenomenon when the signal turns red. This study attempts to model seepage using Cellular Automata (CA) based simulation. These interactions between the vehicles affect the safety and capacity of a facility. There are very few studies that include this behaviour in the CA based models. This study further calculates lateral and longitudinal gaps available between adjacent front and side vehicles to identify the forward movement possibilities. The study is compared with the simulation software VISSIM. It is evident that the software is unable to replicate the seepage behaviour, which is reflected in the trajectories

OPTIMIZING THE PERFORMANCE OF STATE ESTIMATION IN POWER SYSTEMS USING A NOVEL SWARM-INTELLIGENT APPROACH

For power systems to operate reliably and effectively, state estimate is essential. For monitoring and control applications, it offers real-time estimates of the system's state indicators, including voltage levels and orientations. In this paper, a novel catagonfly optimization (CFO) technique that aims to improve state estimation effectiveness in electrical systems is presented. The suggested strategy combines optimization techniques from cat swarm and dragonfly. The suggested strategy is evaluated using the IEEE-118 test environment under various simulated scenarios, and the findings are contrasted with those of other methods already in use. The optimization results and statistical error assessment demonstrate the suggested CFO technique's efficiency to the alternatives. The results of this study also have the potential to improve the incorporation of sustainable energy sources, microgrids, and additional emerging innovations into existing power grids

Selection of open or closed boundaries in a cellular automata model for heterogeneous non-lane-based traffic

Cellular automata (CA) simulation models developed for traffic are either closed or open boundary type. The selection and difference of boundaries has been studied extensively for ideal and single-lane homogeneous traffic conditions. However, the effect of these on multi-lane-heterogeneous traffic still needs attention because most of the traffic observed in many parts of the world is not single-lane homogeneous traffic. It is evident from multiple studies that open and closed boundaries affect the simulation results. Moreover, these require different inputs for simulation. This study attempts to evaluate the difference in the results of open and closed boundary simulations in heterogeneous non-lane-based traffic. The methodology discussed in this study is relatable to the field conditions. The present study includes some of the common but often ignored features in the model such as seepage of small-sized vehicles. Furthermore, this study also includes some of the previously unnoticed features while modeling the non-lane-based traffic at intersections. The modeling of open boundaries simulation can be better and easy in most of the situations compared to the closed boundaries. Closed boundary simulation results for flow-density curve show a smooth trend, whereas open boundary simulation results are scattered as observed in the field. This study further concludes that the size of the vehicle does not change the fundamental diagrams except when other characteristics such as seepage, lane change, and different maximum speeds for different modes are considered. The study used field observed influence zone of intersections to decide the dimension of intersection in the simulation model

A Hybrid Machine Learning Technique For Feature Optimization In Object-Based Classification of Debris-Covered Glaciers

Object-based features like spectral, topographic, and textural are supportive to determine debris-covered glacier classes. The original feature space includes relevant and irrelevant features. The inclusion of all these features increases the complexity and renders the classifier’s performance. Therefore, feature space optimization is requisite for the classification process. Previous studies have shown a rigorous exercise in manually selecting the best combination of features to define the target class and proven to be a time consuming task. The present study proposed a hybrid feature selection technique to automate the selection of the best suitable features. This study aimed to reduce the classifier’s complexity and enhance the performance of the classification model. Relief-F and Pearson Correlation filter-based feature selection methods ranked features according to the relevance and filtered out irrelevant or less important features based on the defined condition. Later, the hybrid model selected the common features to get an optimal feature set. The proposed hybrid model was tested on Landsat 8 images of debris-covered glaciers in Central Karakoram Range and validated with present glacier inventories. The results showed that the classification accuracy of the proposed hybrid feature selection model with a Decision Tree classifier is 99.82%, which is better than the classification results obtained using other mapping techniques. In addition, the hybrid feature selection technique has sped up the process of classification by reducing the number of features by 77% without compromising the classification accuracy