VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

The Genetic Algorithm is an area in the field of Artificial Intelligence that is founded on the principles of biological evolution. Visualization techniques help in understanding the searching behaviour of Genetic Algorithm. lt also makes possible the user interactions during the searching process. It is noted that active user intervention increases the acceleration of Genetic Algorithm towards an optimal solution. In proposed research work, the user is aided by a visualization based on the representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. The user participates in the search by proposing a new individual. This is difTerent from existing Interactive Genetic Algorithm in which selection and evaluation of solutions is done by the users. A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. This visual tool enables the display of the evolution of gene values from generation to generation to observing and analysing the behaviour of the search space with user interactions. Individuals for the next generation are selected by using the objective function. Hence, a novel humanmachine interaction is developed in the proposed approach. The efficiency of the proposed approach is evaluated by two benchmark functions. The analysis and comparison of VIGA-20 is based on convergence test against the results obtained from the Simple Genetic Algorithm. This comparison is based on the same parameters except for the interactions of the user. The application of proposed approach is the modelling the branching structures by deriving a rule from best solution of VIGA-20. The comparison of results is based on the different user's perceptions, their involvement in the VIGA-20 and the difference of the fitness convergence as compared to Simple Genetic Algorithm

VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

The Genetic Algorithm is an area in the field of Artificial Intelligence that is founded on the principles of biological evolution. Visualization techniques help in understanding the searching behaviour of Genetic Algorithm. lt also makes possible the user interactions during the searching process. It is noted that active user intervention increases the acceleration of Genetic Algorithm towards an optimal solution. In proposed research work, the user is aided by a visualization based on the representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. The user participates in the search by proposing a new individual. This is difTerent from existing Interactive Genetic Algorithm in which selection and evaluation of solutions is done by the users. A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is implemented to accomplish this goal. This visual tool enables the display of the evolution of gene values from generation to generation to observing and analysing the behaviour of the search space with user interactions. Individuals for the next generation are selected by using the objective function. Hence, a novel humanmachine interaction is developed in the proposed approach. The efficiency of the proposed approach is evaluated by two benchmark functions. The analysis and comparison of VIGA-20 is based on convergence test against the results obtained from the Simple Genetic Algorithm. This comparison is based on the same parameters except for the interactions of the user. The application of proposed approach is the modelling the branching structures by deriving a rule from best solution of VIGA-20. The comparison of results is based on the different user's perceptions, their involvement in the VIGA-20 and the difference of the fitness convergence as compared to Simple Genetic Algorithm

Mental health challenges and psycho-social interventions amid COVID-19 pandemic: A call to action for Pakistan

The increase in death and spread-related coronavirus (COVID-19) has shifted the world focus to the containment of the disease by emphasising measures to prevent spread in the general population. Such a complex, threatening, and unprecedented situation has left the psycho-social wellbeing needs of general public unaddressed. This paper aims to review the current COVID-19 scenario and its effects on the psycho-social wellbeing of people; and an attempt to shed some light on the aforementioned questions. Furthermore, the review will propose some recommendations for overcoming the mental illness issues, during and after the COVID-19 outbreak. We extracted information from reliable published international and national literature and reviewed anecdotes from media content from January to June 2020. The mental health implications of this outbreak will be long-lasting; however, by prioritising, investing diligently, and taking a collective approach, this challenge can be dealt with in a promising manner

Molecular epidemiology of SARS-CoV-2: A tertiary care hospital experience from Pakistan

Objectives: The current study was conducted to assess the molecular epidemiology of SARS-CoV-2 in the general population. Methodology: This study was conducted from April to July 2020, at the Molecular Diagnostic Laboratory, Pakistan Atomic Energy Commission (PAEC) General Hospital Islamabad, Pakistan. A total of 28,274 nasopharyngeal swabs were collected in Viral Transport Medium (VTM) media from symptomatic and asymptomatic individuals at the sample collection centers of our hospital and other affiliated hospitals. RNA was extracted using both automated and manual extraction platforms as per the manufacturer's instructions. Multiple qualitative reverse transcription real-time PCR kits for the identification of SARS-CoV-2 were used. Results: The results showed that 1,722 (6.09%) were positive for SARA-CoV-2 RNA. The males exhibited a prevalence of 2.76% while females showed a high prevalence of 13.44%. Among males,  most patients 424 (31.47%) were in the age group of 31-40 years followed by the age group of 41-50 years 306 (22.71%). Similarly among females, the majority of patients were from the age group 31-40 years with 91 (24.66%) followed by 41-50 years of age group 70 (18.66%) confirmed cases. Conclusion: The molecular epidemiological data may support the national policy formulation, transmission tracking, and the execution of measures to control viral transmission

Does individualized guided selection of antiplatelet therapy improve outcomes after percutaneous coronary intervention? A systematic review and meta-analysis

Background: The potential benefits of individualized guided selection of antiplatelet therapy over standard antiplatelet therapy in improving outcomes in patients undergoing percutaneous coronary intervention (PCI) have not been established. Therefore, we pooled evidence from available clinical trials to assess the effectiveness by comparing the two regimens in patients undergoing PCI.Methods: We queried two electronic databases, MEDLINE and Cochrane CENTRAL, from their inception to April 20, 2021 for published randomized controlled trials in any language that compared guided antiplatelet therapy, using either genetic testing or platelet function testing, versus standard antiplatelet therapy in patients undergoing PCI. The results from trials were presented as risk ratios (RRs) with 95% confidence intervals (CIs) and were pooled using a random-effects model.Results: Eleven eligible studies consisting of 18,465 patients undergoing PCI were included. Pooled results indicated that guided antiplatelet therapy, compared to standard therapy, was associated with a significant reduction in the incidence of MACE [RR 0·78, 95% CI (0·62-0·99), P = 0·04], MI [RR 0·73, 95% CI (0·56-0.96), P = 0·03], ST [RR 0·66, 95% CI (0·47-0.94), P = 0·02], stroke [RR 0·71, 95% CI (0·50-1.00), P = 0·05], and minor bleeding [RR 0·78, 95% CI (0·66-0.91), P = 0·003].Conclusions: Individualized guided selection of antiplatelet therapy significantly reduced the incidence of MACE, MI, ST, stroke, and minor bleeding in adult patients when compared with standard antiplatelet therapy. Our findings support the implementation of genetic and platelet function testing to select the most beneficial antiplatelet agent

Detection of severity level of diabetic retinopathy using Bag of features model

Diabetic retinopathy is a vascular disease caused by uncontrolled diabetes. Its early detection can save diabetic patients from blindness. However, the detection of its severity level is a challenge for ophthalmologists since last few decades. Several efforts have been made for the identification of its limited stages by using pre‐ and post‐processing methods, which require extensive domain knowledge. This study proposes an improved automated system for severity detection of diabetic retinopathy which is a dictionary‐based approach and does not include pre‐ and post‐processing steps. This approach integrates pathological explicit image representation into a learning outline. To create the dictionary of visual features, points of interest are detected to compute the descriptive features from retinal images through speed up robust features algorithm and histogram of oriented gradients. These features are clustered to generate a dictionary, then coding and pooling are applied for compact representation of features. Radial basis kernel support vector machine and neural network are used to classify the images into five classes namely normal, mild, moderate, severe non‐proliferative diabetic retinopathy, and proliferative diabetic retinopathy. The proposed system exhibits improved results of 95.92% sensitivity and 98.90% specificity in relation to the reported state of the art methods

Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition

This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views

Investigating EEG Patterns for Dual-Stimuli Induced Human Fear Emotional State

Most electroencephalography (EEG) based emotion recognition systems make use of videos and images as stimuli. Few used sounds, and even fewer studies were found involving self-induced emotions. Furthermore, most of the studies rely on single stimuli to evoke emotions. The question of “whether different stimuli for same emotion elicitation generate any subject-independent correlations” remains unanswered. This paper introduces a dual modality based emotion elicitation paradigm to investigate if emotions can be classified induced with different stimuli. A method has been proposed based on common spatial pattern (CSP) and linear discriminant analysis (LDA) to analyze human brain signals for fear emotions evoked with two different stimuli. Self-induced emotional imagery is one of the considered stimuli, while audio/video clips are used as the other stimuli. The method extracts features from the CSP algorithm and LDA performs classification. To investigate associated EEG correlations, a spectral analysis was performed. To further improve the performance, CSP was compared with other regularized techniques. Critical EEG channels are identified based on spatial filter weights. To the best of our knowledge, our work provides the first contribution for the assessment of EEG correlations in the case of self versus video induced emotions captured with a commercial grade EEG device

DEBT SUSTAINABILITY A Comparative Analysis of SAARC Countries

Abstract. The paper analyses the conditions of public and external debt sustainability in four major countries of SAARC, viz. Pakistan, India, Sri Lanka and Bangladesh. For this purpose, traditional debt ratios have been examined by comparing them with threshold levels; and also computed the necessary as well as the sufficient conditions for debt sustainability by using theoretical framework. The results show that all the four countries have been experiencing episodes of unsustainable debt burden due to large fiscal and current account imbalances. It also appears that debt would continue to be an issue periods ahead unless corrective policy measures to address structural imbalances are taken